categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2405.19315 | null | null | http://arxiv.org/pdf/2405.19315v2 | 2024-06-07T03:39:04Z | 2024-05-29T17:39:42Z | Matryoshka Query Transformer for Large Vision-Language Models | Large Vision-Language Models (LVLMs) typically encode an image into a fixed number of visual tokens (e.g., 576) and process these tokens with a language model. Despite their strong performance, LVLMs face challenges in adapting to varying computational constraints. This raises the question: can we achieve flexibility in the number of visual tokens to suit different tasks and computational resources? We answer this with an emphatic yes. Inspired by Matryoshka Representation Learning, we introduce the Matryoshka Query Transformer (MQT), capable of encoding an image into m visual tokens during inference, where m can be any number up to a predefined maximum. This is achieved by employing a query transformer with M latent query tokens to compress the visual embeddings. During each training step, we randomly select m <= M latent query tokens and train the model using only these first m tokens, discarding the rest. Combining MQT with LLaVA, we train a single model once, and flexibly and drastically reduce the number of inference-time visual tokens while maintaining similar or better performance compared to training independent models for each number of tokens. Our model, MQT-LLAVA, matches LLaVA-1.5 performance across 11 benchmarks using a maximum of 256 tokens instead of LLaVA's fixed 576. Reducing to 16 tokens (8x less TFLOPs) only sacrifices the performance by 2.4 points on MMBench. On certain tasks such as ScienceQA and MMMU, we can even go down to only 2 visual tokens with performance drops of just 3% and 6% each. Our exploration of the trade-off between the accuracy and computational cost brought about by the number of visual tokens facilitates future research to achieve the best of both worlds. | [
"['Wenbo Hu' 'Zi-Yi Dou' 'Liunian Harold Li' 'Amita Kamath' 'Nanyun Peng'\n 'Kai-Wei Chang']"
] |
null | null | 2405.19316 | null | null | http://arxiv.org/pdf/2405.19316v1 | 2024-05-29T17:39:48Z | 2024-05-29T17:39:48Z | Robust Preference Optimization through Reward Model Distillation | Language model (LM) post-training (or alignment) involves maximizing a reward function that is derived from preference annotations. Direct Preference Optimization (DPO) is a popular offline alignment method that trains a policy directly on preference data without the need to train a reward model or apply reinforcement learning. However, typical preference datasets have only a single, or at most a few, annotation per preference pair, which causes DPO to overconfidently assign rewards that trend towards infinite magnitude. This frequently leads to degenerate policies, sometimes causing even the probabilities of the preferred generations to go to zero. In this work, we analyze this phenomenon and propose distillation to get a better proxy for the true preference distribution over generation pairs: we train the LM to produce probabilities that match the distribution induced by a reward model trained on the preference data. Moreover, to account for uncertainty in the reward model we are distilling from, we optimize against a family of reward models that, as a whole, is likely to include at least one reasonable proxy for the preference distribution. Our results show that distilling from such a family of reward models leads to improved robustness to distribution shift in preference annotations, while preserving the simple supervised nature of DPO. | [
"['Adam Fisch' 'Jacob Eisenstein' 'Vicky Zayats' 'Alekh Agarwal'\n 'Ahmad Beirami' 'Chirag Nagpal' 'Pete Shaw' 'Jonathan Berant']"
] |
null | null | 2405.19317 | null | null | http://arxiv.org/pdf/2405.19317v1 | 2024-05-29T17:43:13Z | 2024-05-29T17:43:13Z | Adaptive Generalized Neyman Allocation: Local Asymptotic Minimax Optimal
Best Arm Identification | This study investigates a local asymptotic minimax optimal strategy for fixed-budget best arm identification (BAI). We propose the Adaptive Generalized Neyman Allocation (AGNA) strategy and show that its worst-case upper bound of the probability of misidentifying the best arm aligns with the worst-case lower bound under the small-gap regime, where the gap between the expected outcomes of the best and suboptimal arms is small. Our strategy corresponds to a generalization of the Neyman allocation for two-armed bandits (Neyman, 1934; Kaufmann et al., 2016) and a refinement of existing strategies such as the ones proposed by Glynn & Juneja (2004) and Shin et al. (2018). Compared to Komiyama et al. (2022), which proposes a minimax rate-optimal strategy, our proposed strategy has a tighter upper bound that exactly matches the lower bound, including the constant terms, by restricting the class of distributions to the class of small-gap distributions. Our result contributes to the longstanding open issue about the existence of asymptotically optimal strategies in fixed-budget BAI, by presenting the local asymptotic minimax optimal strategy. | [
"['Masahiro Kato']"
] |
null | null | 2405.19320 | null | null | http://arxiv.org/pdf/2405.19320v3 | 2024-07-05T04:59:42Z | 2024-05-29T17:51:42Z | Value-Incentivized Preference Optimization: A Unified Approach to Online
and Offline RLHF | Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of preference data, both online and offline RLHF are active areas of investigation. A key bottleneck is understanding how to incorporate uncertainty estimation in the reward function learned from the preference data for RLHF, regardless of how the preference data is collected. While the principles of optimism or pessimism under uncertainty are well-established in standard reinforcement learning (RL), a practically-implementable and theoretically-grounded form amenable to large language models is not yet available, as standard techniques for constructing confidence intervals become intractable under arbitrary policy parameterizations. In this paper, we introduce a unified approach to online and offline RLHF -- value-incentivized preference optimization (VPO) -- which regularizes the maximum-likelihood estimate of the reward function with the corresponding value function, modulated by a $textit{sign}$ to indicate whether the optimism or pessimism is chosen. VPO also directly optimizes the policy with implicit reward modeling, and therefore shares a simpler RLHF pipeline similar to direct preference optimization. Theoretical guarantees of VPO are provided for both online and offline settings, matching the rates of their standard RL counterparts. Moreover, experiments on text summarization and dialog verify the practicality and effectiveness of VPO. | [
"['Shicong Cen' 'Jincheng Mei' 'Katayoon Goshvadi' 'Hanjun Dai' 'Tong Yang'\n 'Sherry Yang' 'Dale Schuurmans' 'Yuejie Chi' 'Bo Dai']"
] |
null | null | 2405.19323 | null | null | http://arxiv.org/pdf/2405.19323v1 | 2024-05-29T17:54:22Z | 2024-05-29T17:54:22Z | Are Large Language Models Chameleons? | Do large language models (LLMs) have their own worldviews and personality tendencies? Simulations in which an LLM was asked to answer subjective questions were conducted more than 1 million times. Comparison of the responses from different LLMs with real data from the European Social Survey (ESS) suggests that the effect of prompts on bias and variability is fundamental, highlighting major cultural, age, and gender biases. Methods for measuring the difference between LLMs and survey data are discussed, such as calculating weighted means and a new proposed measure inspired by Jaccard similarity. We conclude that it is important to analyze the robustness and variability of prompts before using LLMs to model individual decisions or collective behavior, as their imitation abilities are approximate at best. | [
"['Mingmeng Geng' 'Sihong He' 'Roberto Trotta']"
] |
null | null | 2405.19327 | null | null | http://arxiv.org/pdf/2405.19327v4 | 2024-07-10T16:55:47Z | 2024-05-29T17:57:16Z | MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model
Series | Large Language Models (LLMs) have made great strides in recent years to achieve unprecedented performance across different tasks. However, due to commercial interest, the most competitive models like GPT, Gemini, and Claude have been gated behind proprietary interfaces without disclosing the training details. Recently, many institutions have open-sourced several strong LLMs like LLaMA-3, comparable to existing closed-source LLMs. However, only the model's weights are provided with most details (e.g., intermediate checkpoints, pre-training corpus, and training code, etc.) being undisclosed. To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (e.g., Pythia, Amber, OLMo), where more details (e.g., pre-training corpus and training code) are being provided. These models have greatly advanced the scientific study of these large models including their strengths, weaknesses, biases and risks. However, we observe that the existing truly open LLMs on reasoning, knowledge, and coding tasks are still inferior to existing state-of-the-art LLMs with similar model sizes. To this end, we open-source MAP-Neo, a highly capable and transparent bilingual language model with 7B parameters trained from scratch on 4.5T high-quality tokens. Our MAP-Neo is the first fully open-sourced bilingual LLM with comparable performance compared to existing state-of-the-art LLMs. Moreover, we open-source all details to reproduce our MAP-Neo, where the cleaned pre-training corpus, data cleaning pipeline, checkpoints, and well-optimized training/evaluation framework are provided. Finally, we hope our MAP-Neo will enhance and strengthen the open research community and inspire more innovations and creativities to facilitate the further improvements of LLMs. | [
"['Ge Zhang' 'Scott Qu' 'Jiaheng Liu' 'Chenchen Zhang' 'Chenghua Lin'\n 'Chou Leuang Yu' 'Danny Pan' 'Esther Cheng' 'Jie Liu' 'Qunshu Lin'\n 'Raven Yuan' 'Tuney Zheng' 'Wei Pang' 'Xinrun Du' 'Yiming Liang'\n 'Yinghao Ma' 'Yizhi Li' 'Ziyang Ma' 'Bill Lin' 'Emmanouil Benetos'\n 'Huan Yang' 'Junting Zhou' 'Kaijing Ma' 'Minghao Liu' 'Morry Niu'\n 'Noah Wang' 'Quehry Que' 'Ruibo Liu' 'Sine Liu' 'Shawn Guo' 'Soren Gao'\n 'Wangchunshu Zhou' 'Xinyue Zhang' 'Yizhi Zhou' 'Yubo Wang' 'Yuelin Bai'\n 'Yuhan Zhang' 'Yuxiang Zhang' 'Zenith Wang' 'Zhenzhu Yang' 'Zijian Zhao'\n 'Jiajun Zhang' 'Wanli Ouyang' 'Wenhao Huang' 'Wenhu Chen']"
] |
null | null | 2405.19332 | null | null | http://arxiv.org/pdf/2405.19332v1 | 2024-05-29T17:59:07Z | 2024-05-29T17:59:07Z | Self-Exploring Language Models: Active Preference Elicitation for Online
Alignment | Preference optimization, particularly through Reinforcement Learning from Human Feedback (RLHF), has achieved significant success in aligning Large Language Models (LLMs) to adhere to human intentions. Unlike offline alignment with a fixed dataset, online feedback collection from humans or AI on model generations typically leads to more capable reward models and better-aligned LLMs through an iterative process. However, achieving a globally accurate reward model requires systematic exploration to generate diverse responses that span the vast space of natural language. Random sampling from standard reward-maximizing LLMs alone is insufficient to fulfill this requirement. To address this issue, we propose a bilevel objective optimistically biased towards potentially high-reward responses to actively explore out-of-distribution regions. By solving the inner-level problem with the reparameterized reward function, the resulting algorithm, named Self-Exploring Language Models (SELM), eliminates the need for a separate RM and iteratively updates the LLM with a straightforward objective. Compared to Direct Preference Optimization (DPO), the SELM objective reduces indiscriminate favor of unseen extrapolations and enhances exploration efficiency. Our experimental results demonstrate that when finetuned on Zephyr-7B-SFT and Llama-3-8B-Instruct models, SELM significantly boosts the performance on instruction-following benchmarks such as MT-Bench and AlpacaEval 2.0, as well as various standard academic benchmarks in different settings. Our code and models are available at https://github.com/shenao-zhang/SELM. | [
"['Shenao Zhang' 'Donghan Yu' 'Hiteshi Sharma' 'Ziyi Yang' 'Shuohang Wang'\n 'Hany Hassan' 'Zhaoran Wang']"
] |
null | null | 2405.19335 | null | null | http://arxiv.org/pdf/2405.19335v1 | 2024-05-29T17:59:58Z | 2024-05-29T17:59:58Z | X-VILA: Cross-Modality Alignment for Large Language Model | We introduce X-VILA, an omni-modality model designed to extend the capabilities of large language models (LLMs) by incorporating image, video, and audio modalities. By aligning modality-specific encoders with LLM inputs and diffusion decoders with LLM outputs, X-VILA achieves cross-modality understanding, reasoning, and generation. To facilitate this cross-modality alignment, we curate an effective interleaved any-to-any modality instruction-following dataset. Furthermore, we identify a significant problem with the current cross-modality alignment method, which results in visual information loss. To address the issue, we propose a visual alignment mechanism with a visual embedding highway module. We then introduce a resource-efficient recipe for training X-VILA, that exhibits proficiency in any-to-any modality conversation, surpassing previous approaches by large margins. X-VILA also showcases emergent properties across modalities even in the absence of similar training data. The project will be made open-source. | [
"['Hanrong Ye' 'De-An Huang' 'Yao Lu' 'Zhiding Yu' 'Wei Ping' 'Andrew Tao'\n 'Jan Kautz' 'Song Han' 'Dan Xu' 'Pavlo Molchanov' 'Hongxu Yin']"
] |
null | null | 2405.19340 | null | null | http://arxiv.org/pdf/2405.19340v1 | 2024-05-02T06:03:27Z | 2024-05-02T06:03:27Z | Obtaining physical layer data of latest generation networks for
investigating adversary attacks | The field of machine learning is developing rapidly and is being used in various fields of science and technology. In this way, machine learning can be used to optimize the functions of latest generation data networks such as 5G and 6G. This also applies to functions at a lower level. A feature of the use of machine learning in the radio path for targeted radiation generation in modern ultra-massive MIMO, reconfigurable intelligent interfaces and other technologies is the complex acquisition and processing of data from the physical layer. Additionally, adversarial measures that manipulate the behaviour of intelligent machine learning models are becoming a major concern, as many machine learning models are sensitive to incorrect input data. To obtain data on attacks directly from processing service information, a simulation model is proposed that works in conjunction with machine learning applications. | [
"['M. V. Ushakova' 'Yu. A. Ushakov' 'L. V. Legashev']"
] |
null | null | 2405.19342 | null | null | http://arxiv.org/pdf/2405.19342v1 | 2024-05-14T12:53:32Z | 2024-05-14T12:53:32Z | Sonos Voice Control Bias Assessment Dataset: A Methodology for
Demographic Bias Assessment in Voice Assistants | Recent works demonstrate that voice assistants do not perform equally well for everyone, but research on demographic robustness of speech technologies is still scarce. This is mainly due to the rarity of large datasets with controlled demographic tags. This paper introduces the Sonos Voice Control Bias Assessment Dataset, an open dataset composed of voice assistant requests for North American English in the music domain (1,038 speakers, 166 hours, 170k audio samples, with 9,040 unique labelled transcripts) with a controlled demographic diversity (gender, age, dialectal region and ethnicity). We also release a statistical demographic bias assessment methodology, at the univariate and multivariate levels, tailored to this specific use case and leveraging spoken language understanding metrics rather than transcription accuracy, which we believe is a better proxy for user experience. To demonstrate the capabilities of this dataset and statistical method to detect demographic bias, we consider a pair of state-of-the-art Automatic Speech Recognition and Spoken Language Understanding models. Results show statistically significant differences in performance across age, dialectal region and ethnicity. Multivariate tests are crucial to shed light on mixed effects between dialectal region, gender and age. | [
"['Chloé Sekkat' 'Fanny Leroy' 'Salima Mdhaffar' 'Blake Perry Smith'\n 'Yannick Estève' 'Joseph Dureau' 'Alice Coucke']"
] |
null | null | 2405.19345 | null | null | http://arxiv.org/pdf/2405.19345v1 | 2024-05-17T14:00:11Z | 2024-05-17T14:00:11Z | Review of Deep Representation Learning Techniques for Brain-Computer
Interfaces and Recommendations | In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial interest. This review synthesizes empirical findings from a collection of articles using deep representation learning techniques for BCI decoding, to provide a comprehensive analysis of the current state-of-the-art. Each article was scrutinized based on three criteria: (1) the deep representation learning technique employed, (2) the underlying motivation for its utilization, and (3) the approaches adopted for characterizing the learned representations. Among the 81 articles finally reviewed in depth, our analysis reveals a predominance of 31 articles using autoencoders. We identified 13 studies employing self-supervised learning (SSL) techniques, among which ten were published in 2022 or later, attesting to the relative youth of the field. However, at the time being, none of these have led to standard foundation models that are picked up by the BCI community. Likewise, only a few studies have introspected their learned representations. We observed that the motivation in most studies for using representation learning techniques is for solving transfer learning tasks, but we also found more specific motivations such as to learn robustness or invariances, as an algorithmic bridge, or finally to uncover the structure of the data. Given the potential of foundation models to effectively tackle these challenges, we advocate for a continued dedication to the advancement of foundation models specifically designed for EEG signal decoding by using SSL techniques. We also underline the imperative of establishing specialized benchmarks and datasets to facilitate the development and continuous improvement of such foundation models. | [
"['Pierre Guetschel' 'Sara Ahmadi' 'Michael Tangermann']"
] |
null | null | 2405.19346 | null | null | http://arxiv.org/pdf/2405.19346v2 | 2024-07-09T14:30:24Z | 2024-05-17T20:36:04Z | Subject-Adaptive Transfer Learning Using Resting State EEG Signals for
Cross-Subject EEG Motor Imagery Classification | Electroencephalography (EEG) motor imagery (MI) classification is a fundamental, yet challenging task due to the variation of signals between individuals i.e., inter-subject variability. Previous approaches try to mitigate this using task-specific (TS) EEG signals from the target subject in training. However, recording TS EEG signals requires time and limits its applicability in various fields. In contrast, resting state (RS) EEG signals are a viable alternative due to ease of acquisition with rich subject information. In this paper, we propose a novel subject-adaptive transfer learning strategy that utilizes RS EEG signals to adapt models on unseen subject data. Specifically, we disentangle extracted features into task- and subject-dependent features and use them to calibrate RS EEG signals for obtaining task information while preserving subject characteristics. The calibrated signals are then used to adapt the model to the target subject, enabling the model to simulate processing TS EEG signals of the target subject. The proposed method achieves state-of-the-art accuracy on three public benchmarks, demonstrating the effectiveness of our method in cross-subject EEG MI classification. Our findings highlight the potential of leveraging RS EEG signals to advance practical brain-computer interface systems. The code is available at https://github.com/SionAn/MICCAI2024-ResTL. | [
"['Sion An' 'Myeongkyun Kang' 'Soopil Kim' 'Philip Chikontwe' 'Li Shen'\n 'Sang Hyun Park']"
] |
null | null | 2405.19347 | null | null | http://arxiv.org/pdf/2405.19347v1 | 2024-05-21T06:27:07Z | 2024-05-21T06:27:07Z | Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning
Approach | 3D spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely-largescale-programable-metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the Desired Focal Point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSIindependent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the Phase Distribution Image of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing Quasi-Liquid-Layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold. | [
"['Mohammad Amir Fallah' 'Mehdi Monemi' 'Mehdi Rasti' 'Matti Latva-Aho']"
] |
null | null | 2405.19348 | null | null | http://arxiv.org/pdf/2405.19348v1 | 2024-05-21T14:01:57Z | 2024-05-21T14:01:57Z | NERULA: A Dual-Pathway Self-Supervised Learning Framework for
Electrocardiogram Signal Analysis | Electrocardiogram (ECG) signals are critical for diagnosing heart conditions and capturing detailed cardiac patterns. As wearable single-lead ECG devices become more common, efficient analysis methods are essential. We present NERULA (Non-contrastive ECG and Reconstruction Unsupervised Learning Algorithm), a self-supervised framework designed for single-lead ECG signals. NERULA's dual-pathway architecture combines ECG reconstruction and non-contrastive learning to extract detailed cardiac features. Our 50% masking strategy, using both masked and inverse-masked signals, enhances model robustness against real-world incomplete or corrupted data. The non-contrastive pathway aligns representations of masked and inverse-masked signals, while the reconstruction pathway comprehends and reconstructs missing features. We show that combining generative and discriminative paths into the training spectrum leads to better results by outperforming state-of-the-art self-supervised learning benchmarks in various tasks, demonstrating superior performance in ECG analysis, including arrhythmia classification, gender classification, age regression, and human activity recognition. NERULA's dual-pathway design offers a robust, efficient solution for comprehensive ECG signal interpretation. | [
"['Gouthamaan Manimaran' 'Sadasivan Puthusserypady' 'Helena Domínguez'\n 'Adrian Atienza' 'Jakob E. Bardram']"
] |
null | null | 2405.19349 | null | null | http://arxiv.org/pdf/2405.19349v1 | 2024-05-21T14:02:31Z | 2024-05-21T14:02:31Z | Beyond Isolated Frames: Enhancing Sensor-Based Human Activity
Recognition through Intra- and Inter-Frame Attention | Human Activity Recognition (HAR) has become increasingly popular with ubiquitous computing, driven by the popularity of wearable sensors in fields like healthcare and sports. While Convolutional Neural Networks (ConvNets) have significantly contributed to HAR, they often adopt a frame-by-frame analysis, concentrating on individual frames and potentially overlooking the broader temporal dynamics inherent in human activities. To address this, we propose the intra- and inter-frame attention model. This model captures both the nuances within individual frames and the broader contextual relationships across multiple frames, offering a comprehensive perspective on sequential data. We further enrich the temporal understanding by proposing a novel time-sequential batch learning strategy. This learning strategy preserves the chronological sequence of time-series data within each batch, ensuring the continuity and integrity of temporal patterns in sensor-based HAR. | [
"['Shuai Shao' 'Yu Guan' 'Victor Sanchez']"
] |
null | null | 2405.19351 | null | null | http://arxiv.org/pdf/2405.19351v1 | 2024-05-22T14:40:02Z | 2024-05-22T14:40:02Z | Resonate-and-Fire Spiking Neurons for Target Detection and Hand Gesture
Recognition: A Hybrid Approach | Hand gesture recognition using radar often relies on computationally expensive fast Fourier transforms. This paper proposes an alternative approach that bypasses fast Fourier transforms using resonate-and-fire neurons. These neurons directly detect the hand in the time-domain signal, eliminating the need for fast Fourier transforms to retrieve range information. Following detection, a simple Goertzel algorithm is employed to extract five key features, eliminating the need for a second fast Fourier transform. These features are then fed into a recurrent neural network, achieving an accuracy of 98.21% for classifying five gestures. The proposed approach demonstrates competitive performance with reduced complexity compared to traditional methods | [
"['Ahmed Shaaban' 'Zeineb Chaabouni' 'Maximilian Strobel'\n 'Wolfgang Furtner' 'Robert Weigel' 'Fabian Lurz']"
] |
null | null | 2405.19356 | null | null | http://arxiv.org/pdf/2405.19356v1 | 2024-05-23T21:45:15Z | 2024-05-23T21:45:15Z | An LSTM Feature Imitation Network for Hand Movement Recognition from
sEMG Signals | Surface Electromyography (sEMG) is a non-invasive signal that is used in the recognition of hand movement patterns, the diagnosis of diseases, and the robust control of prostheses. Despite the remarkable success of recent end-to-end Deep Learning approaches, they are still limited by the need for large amounts of labeled data. To alleviate the requirement for big data, researchers utilize Feature Engineering, which involves decomposing the sEMG signal into several spatial, temporal, and frequency features. In this paper, we propose utilizing a feature-imitating network (FIN) for closed-form temporal feature learning over a 300ms signal window on Ninapro DB2, and applying it to the task of 17 hand movement recognition. We implement a lightweight LSTM-FIN network to imitate four standard temporal features (entropy, root mean square, variance, simple square integral). We then explore transfer learning capabilities by applying the pre-trained LSTM-FIN for tuning to a downstream hand movement recognition task. We observed that the LSTM network can achieve up to 99% R2 accuracy in feature reconstruction and 80% accuracy in hand movement recognition. Our results also showed that the model can be robustly applied for both within- and cross-subject movement recognition, as well as simulated low-latency environments. Overall, our work demonstrates the potential of the FIN modeling paradigm in data-scarce scenarios for sEMG signal processing. | [
"['Chuheng Wu' 'S. Farokh Atashzar' 'Mohammad M. Ghassemi' 'Tuka Alhanai']"
] |
null | null | 2405.19359 | null | null | http://arxiv.org/pdf/2405.19359v1 | 2024-05-24T06:06:05Z | 2024-05-24T06:06:05Z | Modally Reduced Representation Learning of Multi-Lead ECG Signals
through Simultaneous Alignment and Reconstruction | Electrocardiogram (ECG) signals, profiling the electrical activities of the heart, are used for a plethora of diagnostic applications. However, ECG systems require multiple leads or channels of signals to capture the complete view of the cardiac system, which limits their application in smartwatches and wearables. In this work, we propose a modally reduced representation learning method for ECG signals that is capable of generating channel-agnostic, unified representations for ECG signals. Through joint optimization of reconstruction and alignment, we ensure that the embeddings of the different channels contain an amalgamation of the overall information across channels while also retaining their specific information. On an independent test dataset, we generated highly correlated channel embeddings from different ECG channels, leading to a moderate approximation of the 12-lead signals from a single-channel embedding. Our generated embeddings can work as competent features for ECG signals for downstream tasks. | [
"['Nabil Ibtehaz' 'Masood Mortazavi']"
] |
null | null | 2405.19363 | null | null | http://arxiv.org/pdf/2405.19363v1 | 2024-05-24T16:51:10Z | 2024-05-24T16:51:10Z | Medformer: A Multi-Granularity Patching Transformer for Medical
Time-Series Classification | Medical time series data, such as Electroencephalography (EEG) and Electrocardiography (ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases. Existing methods for medical time series classification primarily rely on handcrafted biomarkers extraction and CNN-based models, with limited exploration of transformers tailored for medical time series. In this paper, we introduce Medformer, a multi-granularity patching transformer tailored specifically for medical time series classification. Our method incorporates three novel mechanisms to leverage the unique characteristics of medical time series: cross-channel patching to leverage inter-channel correlations, multi-granularity embedding for capturing features at different scales, and two-stage (intra- and inter-granularity) multi-granularity self-attention for learning features and correlations within and among granularities. We conduct extensive experiments on five public datasets under both subject-dependent and challenging subject-independent setups. Results demonstrate Medformer's superiority over 10 baselines, achieving top averaged ranking across five datasets on all six evaluation metrics. These findings underscore the significant impact of our method on healthcare applications, such as diagnosing Myocardial Infarction, Alzheimer's, and Parkinson's disease. We release the source code at url{https://github.com/DL4mHealth/Medformer}. | [
"['Yihe Wang' 'Nan Huang' 'Taida Li' 'Yujun Yan' 'Xiang Zhang']"
] |
null | null | 2405.19373 | null | null | http://arxiv.org/pdf/2405.19373v1 | 2024-05-28T14:31:11Z | 2024-05-28T14:31:11Z | Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject
Emotion Recognition | Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy of individuals leads to non-negligible natural differences in EEG signals across subjects, posing challenges for cross-subject emotion recognition. While recent studies have attempted to address these issues, they still face limitations in practical effectiveness and model framework unity. Current methods often struggle to capture the complex spatial-temporal dynamics of EEG signals and fail to effectively integrate multimodal information, resulting in suboptimal performance and limited generalizability across subjects. To overcome these limitations, we develop a Pre-trained model based Multimodal Mood Reader for cross-subject emotion recognition that utilizes masked brain signal modeling and interlinked spatial-temporal attention mechanism. The model learns universal latent representations of EEG signals through pre-training on large scale dataset, and employs Interlinked spatial-temporal attention mechanism to process Differential Entropy(DE) features extracted from EEG data. Subsequently, a multi-level fusion layer is proposed to integrate the discriminative features, maximizing the advantages of features across different dimensions and modalities. Extensive experiments on public datasets demonstrate Mood Reader's superior performance in cross-subject emotion recognition tasks, outperforming state-of-the-art methods. Additionally, the model is dissected from attention perspective, providing qualitative analysis of emotion-related brain areas, offering valuable insights for affective research in neural signal processing. | [
"['Yihang Dong' 'Xuhang Chen' 'Yanyan Shen' 'Michael Kwok-Po Ng' 'Tao Qian'\n 'Shuqiang Wang']"
] |
null | null | 2405.19374 | null | null | http://arxiv.org/pdf/2405.19374v1 | 2024-05-28T20:33:18Z | 2024-05-28T20:33:18Z | Optimal Multiclass U-Calibration Error and Beyond | We consider the problem of online multiclass U-calibration, where a forecaster aims to make sequential distributional predictions over $K$ classes with low U-calibration error, that is, low regret with respect to all bounded proper losses simultaneously. Kleinberg et al. (2023) developed an algorithm with U-calibration error $O(Ksqrt{T})$ after $T$ rounds and raised the open question of what the optimal bound is. We resolve this question by showing that the optimal U-calibration error is $Theta(sqrt{KT})$ -- we start with a simple observation that the Follow-the-Perturbed-Leader algorithm of Daskalakis and Syrgkanis (2016) achieves this upper bound, followed by a matching lower bound constructed with a specific proper loss (which, as a side result, also proves the optimality of the algorithm of Daskalakis and Syrgkanis (2016) in the context of online learning against an adversary with finite choices). We also strengthen our results under natural assumptions on the loss functions, including $Theta(log T)$ U-calibration error for Lipschitz proper losses, $O(log T)$ U-calibration error for a certain class of decomposable proper losses, U-calibration error bounds for proper losses with a low covering number, and others. | [
"['Haipeng Luo' 'Spandan Senapati' 'Vatsal Sharan']"
] |
null | null | 2405.19375 | null | null | http://arxiv.org/pdf/2405.19375v2 | 2024-06-18T12:51:49Z | 2024-05-28T22:25:17Z | Improving global awareness of linkset predictions using Cross-Attentive
Modulation tokens | Most of multiple link prediction or graph generation techniques rely on the attention mechanism or on Graph Neural Networks (GNNs), which consist in leveraging node-level information exchanges in order to form proper link predictions. Such node-level interactions do not process nodes as an ordered sequence, which would imply some kind of natural ordering of the nodes: they are said to be permutation invariant mechanisms. They are well suited for graph problems, but struggle at providing a global orchestration of the predicted links, which can result in a loss of performance. Some typical issues can be the difficulty to ensure high-level properties such as global connectedness, fixed diameter or to avoid information bottleneck effects such as oversmoothing and oversquashing, which respectively consist in abundant smoothing in dense areas leading to a loss of information and a tendency to exclude isolated nodes from the message passing scheme, and often result in irrelevant, unbalanced link predictions. To tackle this problem, we hereby present Cross-Attentive Modulation (CAM) tokens, which introduce cross-attentive units used to condition node and edge-level modulations in order to enable context-aware computations that improve the global consistency of the prediction links. We will implement it on a few permutation invariant architectures, and showcase benchmarks that prove the merits of our work. | [
"['Félix Marcoccia' 'Cédric Adjih' 'Paul Mühlethaler']"
] |
null | null | 2405.19376 | null | null | http://arxiv.org/pdf/2405.19376v2 | 2024-06-02T20:21:45Z | 2024-05-28T22:31:56Z | PureEBM: Universal Poison Purification via Mid-Run Dynamics of
Energy-Based Models | Data poisoning attacks pose a significant threat to the integrity of machine learning models by leading to misclassification of target distribution data by injecting adversarial examples during training. Existing state-of-the-art (SoTA) defense methods suffer from limitations, such as significantly reduced generalization performance and significant overhead during training, making them impractical or limited for real-world applications. In response to this challenge, we introduce a universal data purification method that defends naturally trained classifiers from malicious white-, gray-, and black-box image poisons by applying a universal stochastic preprocessing step $Psi_{T}(x)$, realized by iterative Langevin sampling of a convergent Energy Based Model (EBM) initialized with an image $x.$ Mid-run dynamics of $Psi_{T}(x)$ purify poison information with minimal impact on features important to the generalization of a classifier network. We show that EBMs remain universal purifiers, even in the presence of poisoned EBM training data, and achieve SoTA defense on leading triggered and triggerless poisons. This work is a subset of a larger framework introduced in pgen with a more detailed focus on EBM purification and poison defense. | [
"['Omead Pooladzandi' 'Jeffrey Jiang' 'Sunay Bhat' 'Gregory Pottie']"
] |
null | null | 2405.19380 | null | null | http://arxiv.org/pdf/2405.19380v1 | 2024-05-29T03:24:56Z | 2024-05-29T03:24:56Z | Approximate Thompson Sampling for Learning Linear Quadratic Regulators
with $O(\sqrt{T})$ Regret | We propose an approximate Thompson sampling algorithm that learns linear quadratic regulators (LQR) with an improved Bayesian regret bound of $O(sqrt{T})$. Our method leverages Langevin dynamics with a meticulously designed preconditioner as well as a simple excitation mechanism. We show that the excitation signal induces the minimum eigenvalue of the preconditioner to grow over time, thereby accelerating the approximate posterior sampling process. Moreover, we identify nontrivial concentration properties of the approximate posteriors generated by our algorithm. These properties enable us to bound the moments of the system state and attain an $O(sqrt{T})$ regret bound without the unrealistic restrictive assumptions on parameter sets that are often used in the literature. | [
"['Yeoneung Kim' 'Gihun Kim' 'Insoon Yang']"
] |
null | null | 2405.19383 | null | null | http://arxiv.org/pdf/2405.19383v2 | 2024-05-31T08:29:26Z | 2024-05-29T08:48:52Z | Network Analytics for Anti-Money Laundering -- A Systematic Literature
Review and Experimental Evaluation | Money laundering presents a pervasive challenge, burdening society by financing illegal activities. To more effectively combat and detect money laundering, the use of network information is increasingly being explored, exploiting that money laundering necessarily involves interconnected parties. This has lead to a surge in literature on network analytics (NA) for anti-money laundering (AML). The literature, however, is fragmented and a comprehensive overview of existing work is missing. This results in limited understanding of the methods that may be applied and their comparative detection power. Therefore, this paper presents an extensive and systematic review of the literature. We identify and analyse 97 papers in the Web of Science and Scopus databases, resulting in a taxonomy of approaches following the fraud analytics framework of Bockel-Rickermann et al.. Moreover, this paper presents a comprehensive experimental framework to evaluate and compare the performance of prominent NA methods in a uniform setup. The framework is applied on the publicly available Elliptic data set and implements manual feature engineering, random walk-based methods, and deep learning GNNs. We conclude from the results that network analytics increases the predictive power of the AML model with graph neural networks giving the best results. An open source implementation of the experimental framework is provided to facilitate researchers and practitioners to extend upon these results and experiment on proprietary data. As such, we aim to promote a standardised approach towards the analysis and evaluation of network analytics for AML. | [
"['Bruno Deprez' 'Toon Vanderschueren' 'Bart Baesens' 'Tim Verdonck'\n 'Wouter Verbeke']"
] |
null | null | 2405.19384 | null | null | http://arxiv.org/pdf/2405.19384v1 | 2024-05-29T09:12:44Z | 2024-05-29T09:12:44Z | NeuralODEs for VLEO simulations: Introducing thermoNET for Thermosphere
Modeling | We introduce a novel neural architecture termed thermoNET, designed to represent thermospheric density in satellite orbital propagation using a reduced amount of differentiable computations. Due to the appearance of a neural network on the right-hand side of the equations of motion, the resulting satellite dynamics is governed by a NeuralODE, a neural Ordinary Differential Equation, characterized by its fully differentiable nature, allowing the derivation of variational equations (hence of the state transition matrix) and facilitating its use in connection to advanced numerical techniques such as Taylor-based numerical propagation and differential algebraic techniques. Efficient training of the network parameters occurs through two distinct approaches. In the first approach, the network undergoes training independently of spacecraft dynamics, engaging in a pure regression task against ground truth models, including JB-08 and NRLMSISE-00. In the second paradigm, network parameters are learned based on observed dynamics, adapting through ODE sensitivities. In both cases, the outcome is a flexible, compact model of the thermosphere density greatly enhancing numerical propagation efficiency while maintaining accuracy in the orbital predictions. | [
"['Dario Izzo' 'Giacomo Acciarini' 'Francesco Biscani']"
] |
null | null | 2405.19397 | null | null | http://arxiv.org/pdf/2405.19397v1 | 2024-05-29T18:00:01Z | 2024-05-29T18:00:01Z | Ground state phases of the two-dimension electron gas with a unified
variational approach | The two-dimensional electron gas (2DEG) is a fundamental model, which is drawing increasing interest because of recent advances in experimental and theoretical studies of 2D materials. Current understanding of the ground state of the 2DEG relies on quantum Monte Carlo calculations, based on variational comparisons of different ansatze for different phases. We use a single variational ansatz, a general backflow-type wave function using a message-passing neural quantum state architecture, for a unified description across the entire density range. The variational optimization consistently leads to lower ground-state energies than previous best results. Transition into a Wigner crystal (WC) phase occurs automatically at rs = 37 +/- 1, a density lower than currently believed. Between the liquid and WC phases, the same ansatz and variational search strongly suggest the existence of intermediate states in a broad range of densities, with enhanced short-range nematic spin correlations. | [
"['Conor Smith' 'Yixiao Chen' 'Ryan Levy' 'Yubo Yang' 'Miguel A. Morales'\n 'Shiwei Zhang']"
] |
null | null | 2405.19398 | null | null | http://arxiv.org/pdf/2405.19398v1 | 2024-05-29T18:00:01Z | 2024-05-29T18:00:01Z | Neural Scaling Laws From Large-N Field Theory: Solvable Model Beyond the
Ridgeless Limit | Many machine learning models based on neural networks exhibit scaling laws: their performance scales as power laws with respect to the sizes of the model and training data set. We use large-N field theory methods to solve a model recently proposed by Maloney, Roberts and Sully which provides a simplified setting to study neural scaling laws. Our solution extends the result in this latter paper to general nonzero values of the ridge parameter, which are essential to regularize the behavior of the model. In addition to obtaining new and more precise scaling laws, we also uncover a duality transformation at the diagrams level which explains the symmetry between model and training data set sizes. The same duality underlies recent efforts to design neural networks to simulate quantum field theories. | [
"['Zhengkang Zhang']"
] |
null | null | 2405.19414 | null | null | http://arxiv.org/pdf/2405.19414v1 | 2024-05-29T18:00:21Z | 2024-05-29T18:00:21Z | Safety through Permissibility: Shield Construction for Fast and Safe
Reinforcement Learning | Designing Reinforcement Learning (RL) solutions for real-life problems remains a significant challenge. A major area of concern is safety. "Shielding" is a popular technique to enforce safety in RL by turning user-defined safety specifications into safe agent behavior. However, these methods either suffer from extreme learning delays, demand extensive human effort in designing models and safe domains in the problem, or require pre-computation. In this paper, we propose a new permissibility-based framework to deal with safety and shield construction. Permissibility was originally designed for eliminating (non-permissible) actions that will not lead to an optimal solution to improve RL training efficiency. This paper shows that safety can be naturally incorporated into this framework, i.e. extending permissibility to include safety, and thereby we can achieve both safety and improved efficiency. Experimental evaluation using three standard RL applications shows the effectiveness of the approach. | [
"['Alexander Politowicz' 'Sahisnu Mazumder' 'Bing Liu']"
] |
null | null | 2405.19420 | null | null | http://arxiv.org/pdf/2405.19420v1 | 2024-05-29T18:01:58Z | 2024-05-29T18:01:58Z | Using Contrastive Learning with Generative Similarity to Learn Spaces
that Capture Human Inductive Biases | Humans rely on strong inductive biases to learn from few examples and abstract useful information from sensory data. Instilling such biases in machine learning models has been shown to improve their performance on various benchmarks including few-shot learning, robustness, and alignment. However, finding effective training procedures to achieve that goal can be challenging as psychologically-rich training data such as human similarity judgments are expensive to scale, and Bayesian models of human inductive biases are often intractable for complex, realistic domains. Here, we address this challenge by introducing a Bayesian notion of generative similarity whereby two datapoints are considered similar if they are likely to have been sampled from the same distribution. This measure can be applied to complex generative processes, including probabilistic programs. We show that generative similarity can be used to define a contrastive learning objective even when its exact form is intractable, enabling learning of spatial embeddings that express specific inductive biases. We demonstrate the utility of our approach by showing how it can be used to capture human inductive biases for geometric shapes, and to better distinguish different abstract drawing styles that are parameterized by probabilistic programs. | [
"['Raja Marjieh' 'Sreejan Kumar' 'Declan Campbell' 'Liyi Zhang'\n 'Gianluca Bencomo' 'Jake Snell' 'Thomas L. Griffiths']"
] |
null | null | 2405.19440 | null | null | http://arxiv.org/pdf/2405.19440v3 | 2024-07-01T14:43:51Z | 2024-05-29T18:36:59Z | On the Convergence of Multi-objective Optimization under Generalized
Smoothness | Multi-objective optimization (MOO) is receiving more attention in various fields such as multi-task learning. Recent works provide some effective algorithms with theoretical analysis but they are limited by the standard $L$-smooth or bounded-gradient assumptions, which are typically unsatisfactory for neural networks, such as recurrent neural networks (RNNs) and transformers. In this paper, we study a more general and realistic class of $ell$-smooth loss functions, where $ell$ is a general non-decreasing function of gradient norm. We develop two novel single-loop algorithms for $ell$-smooth MOO problems, Generalized Smooth Multi-objective Gradient descent (GSMGrad) and its stochastic variant, Stochastic Generalized Smooth Multi-objective Gradient descent (SGSMGrad), which approximate the conflict-avoidant (CA) direction that maximizes the minimum improvement among objectives. We provide a comprehensive convergence analysis of both algorithms and show that they converge to an $epsilon$-accurate Pareto stationary point with a guaranteed $epsilon$-level average CA distance (i.e., the gap between the updating direction and the CA direction) over all iterations, where totally $mathcal{O}(epsilon^{-2})$ and $mathcal{O}(epsilon^{-4})$ samples are needed for deterministic and stochastic settings, respectively. Our algorithms can also guarantee a tighter $epsilon$-level CA distance in each iteration using more samples. Moreover, we propose a practical variant of GSMGrad named GSMGrad-FA using only constant-level time and space, while achieving the same performance guarantee as GSMGrad. Our experiments validate our theory and demonstrate the effectiveness of the proposed methods. | [
"['Qi Zhang' 'Peiyao Xiao' 'Kaiyi Ji' 'Shaofeng Zou']"
] |
null | null | 2405.19452 | null | null | http://arxiv.org/pdf/2405.19452v1 | 2024-05-29T19:02:57Z | 2024-05-29T19:02:57Z | Gaitor: Learning a Unified Representation Across Gaits for Real-World
Quadruped Locomotion | The current state-of-the-art in quadruped locomotion is able to produce robust motion for terrain traversal but requires the segmentation of a desired robot trajectory into a discrete set of locomotion skills such as trot and crawl. In contrast, in this work we demonstrate the feasibility of learning a single, unified representation for quadruped locomotion enabling continuous blending between gait types and characteristics. We present Gaitor, which learns a disentangled representation of locomotion skills, thereby sharing information common to all gait types seen during training. The structure emerging in the learnt representation is interpretable in that it is found to encode phase correlations between the different gait types. These can be leveraged to produce continuous gait transitions. In addition, foot swing characteristics are disentangled and directly addressable. Together with a rudimentary terrain encoding and a learned planner operating in this structured latent representation, Gaitor is able to take motion commands including desired gait type and characteristics from a user while reacting to uneven terrain. We evaluate Gaitor in both simulated and real-world settings on the ANYmal C platform. To the best of our knowledge, this is the first work learning such a unified and interpretable latent representation for multiple gaits, resulting in on-demand continuous blending between different locomotion modes on a real quadruped robot. | [
"['Alexander L. Mitchell' 'Wolfgang Merkt' 'Aristotelis Papatheodorou'\n 'Ioannis Havoutis' 'Ingmar Posner']"
] |
null | null | 2405.19454 | null | null | http://arxiv.org/pdf/2405.19454v1 | 2024-05-29T19:05:11Z | 2024-05-29T19:05:11Z | Deep Grokking: Would Deep Neural Networks Generalize Better? | Recent research on the grokking phenomenon has illuminated the intricacies of neural networks' training dynamics and their generalization behaviors. Grokking refers to a sharp rise of the network's generalization accuracy on the test set, which occurs long after an extended overfitting phase, during which the network perfectly fits the training set. While the existing research primarily focus on shallow networks such as 2-layer MLP and 1-layer Transformer, we explore grokking on deep networks (e.g. 12-layer MLP). We empirically replicate the phenomenon and find that deep neural networks can be more susceptible to grokking than its shallower counterparts. Meanwhile, we observe an intriguing multi-stage generalization phenomenon when increase the depth of the MLP model where the test accuracy exhibits a secondary surge, which is scarcely seen on shallow models. We further uncover compelling correspondences between the decreasing of feature ranks and the phase transition from overfitting to the generalization stage during grokking. Additionally, we find that the multi-stage generalization phenomenon often aligns with a double-descent pattern in feature ranks. These observations suggest that internal feature rank could serve as a more promising indicator of the model's generalization behavior compared to the weight-norm. We believe our work is the first one to dive into grokking in deep neural networks, and investigate the relationship of feature rank and generalization performance. | [
"['Simin Fan' 'Razvan Pascanu' 'Martin Jaggi']"
] |
null | null | 2405.19461 | null | null | http://arxiv.org/pdf/2405.19461v1 | 2024-05-29T19:21:17Z | 2024-05-29T19:21:17Z | Clustering-Based Validation Splits for Domain Generalisation | This paper considers the problem of model selection under domain shift. In this setting, it is proposed that a high maximum mean discrepancy (MMD) between the training and validation sets increases the generalisability of selected models. A data splitting algorithm based on kernel k-means clustering, which maximises this objective, is presented. The algorithm leverages linear programming to control the size, label, and (optionally) group distributions of the splits, and comes with convergence guarantees. The technique consistently outperforms alternative splitting strategies across a range of datasets and training algorithms, for both domain generalisation (DG) and unsupervised domain adaptation (UDA) tasks. Analysis also shows the MMD between the training and validation sets to be strongly rank-correlated ($rho=0.63$) with test domain accuracy, further substantiating the validity of this approach. | [
"['Andrea Napoli' 'Paul White']"
] |
null | null | 2405.19463 | null | null | http://arxiv.org/pdf/2405.19463v1 | 2024-05-29T19:21:55Z | 2024-05-29T19:21:55Z | Stochastic Optimization Algorithms for Instrumental Variable Regression
with Streaming Data | We develop and analyze algorithms for instrumental variable regression by viewing the problem as a conditional stochastic optimization problem. In the context of least-squares instrumental variable regression, our algorithms neither require matrix inversions nor mini-batches and provides a fully online approach for performing instrumental variable regression with streaming data. When the true model is linear, we derive rates of convergence in expectation, that are of order $mathcal{O}(log T/T)$ and $mathcal{O}(1/T^{1-iota})$ for any $iota>0$, respectively under the availability of two-sample and one-sample oracles, respectively, where $T$ is the number of iterations. Importantly, under the availability of the two-sample oracle, our procedure avoids explicitly modeling and estimating the relationship between confounder and the instrumental variables, demonstrating the benefit of the proposed approach over recent works based on reformulating the problem as minimax optimization problems. Numerical experiments are provided to corroborate the theoretical results. | [
"['Xuxing Chen' 'Abhishek Roy' 'Yifan Hu' 'Krishnakumar Balasubramanian']"
] |
null | null | 2405.19466 | null | null | http://arxiv.org/pdf/2405.19466v1 | 2024-05-29T19:24:44Z | 2024-05-29T19:24:44Z | Posterior Sampling via Autoregressive Generation | Real-world decision-making requires grappling with a perpetual lack of data as environments change; intelligent agents must comprehend uncertainty and actively gather information to resolve it. We propose a new framework for learning bandit algorithms from massive historical data, which we demonstrate in a cold-start recommendation problem. First, we use historical data to pretrain an autoregressive model to predict a sequence of repeated feedback/rewards (e.g., responses to news articles shown to different users over time). In learning to make accurate predictions, the model implicitly learns an informed prior based on rich action features (e.g., article headlines) and how to sharpen beliefs as more rewards are gathered (e.g., clicks as each article is recommended). At decision-time, we autoregressively sample (impute) an imagined sequence of rewards for each action, and choose the action with the largest average imputed reward. Far from a heuristic, our approach is an implementation of Thompson sampling (with a learned prior), a prominent active exploration algorithm. We prove our pretraining loss directly controls online decision-making performance, and we demonstrate our framework on a news recommendation task where we integrate end-to-end fine-tuning of a pretrained language model to process news article headline text to improve performance. | [
"['Kelly W Zhang' 'Tiffany' 'Cai' 'Hongseok Namkoong' 'Daniel Russo']"
] |
null | null | 2405.19471 | null | null | http://arxiv.org/pdf/2405.19471v1 | 2024-05-29T19:40:27Z | 2024-05-29T19:40:27Z | The Data Minimization Principle in Machine Learning | The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. Rooted in privacy-by-design principles, data minimization has been endorsed by various global data protection regulations. However, its practical implementation remains a challenge due to the lack of a rigorous formulation. This paper addresses this gap and introduces an optimization framework for data minimization based on its legal definitions. It then adapts several optimization algorithms to perform data minimization and conducts a comprehensive evaluation in terms of their compliance with minimization objectives as well as their impact on user privacy. Our analysis underscores the mismatch between the privacy expectations of data minimization and the actual privacy benefits, emphasizing the need for approaches that account for multiple facets of real-world privacy risks. | [
"['Prakhar Ganesh' 'Cuong Tran' 'Reza Shokri' 'Ferdinando Fioretto']"
] |
null | null | 2405.19479 | null | null | http://arxiv.org/abs/2405.19479v1 | 2024-05-29T19:53:23Z | 2024-05-29T19:53:23Z | Participation in the age of foundation models | Growing interest and investment in the capabilities of foundation models has positioned such systems to impact a wide array of public services. Alongside these opportunities is the risk that these systems reify existing power imbalances and cause disproportionate harm to marginalized communities. Participatory approaches hold promise to instead lend agency and decision-making power to marginalized stakeholders. But existing approaches in participatory AI/ML are typically deeply grounded in context - how do we apply these approaches to foundation models, which are, by design, disconnected from context? Our paper interrogates this question. First, we examine existing attempts at incorporating participation into foundation models. We highlight the tension between participation and scale, demonstrating that it is intractable for impacted communities to meaningfully shape a foundation model that is intended to be universally applicable. In response, we develop a blueprint for participatory foundation models that identifies more local, application-oriented opportunities for meaningful participation. In addition to the "foundation" layer, our framework proposes the "subfloor'' layer, in which stakeholders develop shared technical infrastructure, norms and governance for a grounded domain, and the "surface'' layer, in which affected communities shape the use of a foundation model for a specific downstream task. The intermediate "subfloor'' layer scopes the range of potential harms to consider, and affords communities more concrete avenues for deliberation and intervention. At the same time, it avoids duplicative effort by scaling input across relevant use cases. Through three case studies in clinical care, financial services, and journalism, we illustrate how this multi-layer model can create more meaningful opportunities for participation than solely intervening at the foundation layer. | [
"['Harini Suresh' 'Emily Tseng' 'Meg Young' 'Mary L. Gray' 'Emma Pierson'\n 'Karen Levy']"
] |
null | null | 2405.19486 | null | null | http://arxiv.org/pdf/2405.19486v1 | 2024-05-29T20:04:23Z | 2024-05-29T20:04:23Z | Online Nonparametric Supervised Learning for Massive Data | Despite their benefits in terms of simplicity, low computational cost and data requirement, parametric machine learning algorithms, such as linear discriminant analysis, quadratic discriminant analysis or logistic regression, suffer from serious drawbacks including linearity, poor fit of features to the usually imposed normal distribution and high dimensionality. Batch kernel-based nonparametric classifier, which overcomes the linearity and normality of features constraints, represent an interesting alternative for supervised classification problem. However, it suffers from the ``curse of dimension". The problem can be alleviated by the explosive sample size in the era of big data, while large-scale data size presents some challenges in the storage of data and the calculation of the classifier. These challenges make the classical batch nonparametric classifier no longer applicable. This motivates us to develop a fast algorithm adapted to the real-time calculation of the nonparametric classifier in massive as well as streaming data frameworks. This online classifier includes two steps. First, we consider an online principle components analysis to reduce the dimension of the features with a very low computation cost. Then, a stochastic approximation algorithm is deployed to obtain a real-time calculation of the nonparametric classifier. The proposed methods are evaluated and compared to some commonly used machine learning algorithms for real-time fetal well-being monitoring. The study revealed that, in terms of accuracy, the offline (or Batch), as well as, the online classifiers are good competitors to the random forest algorithm. Moreover, we show that the online classifier gives the best trade-off accuracy/computation cost compared to the offline classifier. | [
"['Mohamed Chaouch' 'Omama M. Al-Hamed']"
] |
null | null | 2405.19497 | null | null | http://arxiv.org/pdf/2405.19497v1 | 2024-05-29T20:23:01Z | 2024-05-29T20:23:01Z | Gaussian Flow Bridges for Audio Domain Transfer with Unpaired Data | Audio domain transfer is the process of modifying audio signals to match characteristics of a different domain, while retaining the original content. This paper investigates the potential of Gaussian Flow Bridges, an emerging approach in generative modeling, for this problem. The presented framework addresses the transport problem across different distributions of audio signals through the implementation of a series of two deterministic probability flows. The proposed framework facilitates manipulation of the target distribution properties through a continuous control variable, which defines a certain aspect of the target domain. Notably, this approach does not rely on paired examples for training. To address identified challenges on maintaining the speech content consistent, we recommend a training strategy that incorporates chunk-based minibatch Optimal Transport couplings of data samples and noise. Comparing our unsupervised method with established baselines, we find competitive performance in tasks of reverberation and distortion manipulation. Despite encoutering limitations, the intriguing results obtained in this study underscore potential for further exploration. | [
"['Eloi Moliner' 'Sebastian Braun' 'Hannes Gamper']"
] |
null | null | 2405.19499 | null | null | http://arxiv.org/pdf/2405.19499v1 | 2024-05-29T20:24:42Z | 2024-05-29T20:24:42Z | Momentum for the Win: Collaborative Federated Reinforcement Learning
across Heterogeneous Environments | We explore a Federated Reinforcement Learning (FRL) problem where $N$ agents collaboratively learn a common policy without sharing their trajectory data. To date, existing FRL work has primarily focused on agents operating in the same or ``similar" environments. In contrast, our problem setup allows for arbitrarily large levels of environment heterogeneity. To obtain the optimal policy which maximizes the average performance across all potentially completely different environments, we propose two algorithms: FedSVRPG-M and FedHAPG-M. In contrast to existing results, we demonstrate that both FedSVRPG-M and FedHAPG-M, both of which leverage momentum mechanisms, can exactly converge to a stationary point of the average performance function, regardless of the magnitude of environment heterogeneity. Furthermore, by incorporating the benefits of variance-reduction techniques or Hessian approximation, both algorithms achieve state-of-the-art convergence results, characterized by a sample complexity of $mathcal{O}left(epsilon^{-frac{3}{2}}/Nright)$. Notably, our algorithms enjoy linear convergence speedups with respect to the number of agents, highlighting the benefit of collaboration among agents in finding a common policy. | [
"['Han Wang' 'Sihong He' 'Zhili Zhang' 'Fei Miao' 'James Anderson']"
] |
null | null | 2405.19513 | null | null | http://arxiv.org/pdf/2405.19513v1 | 2024-05-29T20:51:38Z | 2024-05-29T20:51:38Z | Decentralized Optimization in Time-Varying Networks with Arbitrary
Delays | We consider a decentralized optimization problem for networks affected by communication delays. Examples of such networks include collaborative machine learning, sensor networks, and multi-agent systems. To mimic communication delays, we add virtual non-computing nodes to the network, resulting in directed graphs. This motivates investigating decentralized optimization solutions on directed graphs. Existing solutions assume nodes know their out-degrees, resulting in limited applicability. To overcome this limitation, we introduce a novel gossip-based algorithm, called DT-GO, that does not need to know the out-degrees. The algorithm is applicable in general directed networks, for example networks with delays or limited acknowledgment capabilities. We derive convergence rates for both convex and non-convex objectives, showing that our algorithm achieves the same complexity order as centralized Stochastic Gradient Descent. In other words, the effects of the graph topology and delays are confined to higher-order terms. Additionally, we extend our analysis to accommodate time-varying network topologies. Numerical simulations are provided to support our theoretical findings. | [
"['Tomas Ortega' 'Hamid Jafarkhani']"
] |
null | null | 2405.19516 | null | null | http://arxiv.org/pdf/2405.19516v1 | 2024-05-29T20:52:59Z | 2024-05-29T20:52:59Z | Enabling Visual Recognition at Radio Frequency | This paper introduces PanoRadar, a novel RF imaging system that brings RF resolution close to that of LiDAR, while providing resilience against conditions challenging for optical signals. Our LiDAR-comparable 3D imaging results enable, for the first time, a variety of visual recognition tasks at radio frequency, including surface normal estimation, semantic segmentation, and object detection. PanoRadar utilizes a rotating single-chip mmWave radar, along with a combination of novel signal processing and machine learning algorithms, to create high-resolution 3D images of the surroundings. Our system accurately estimates robot motion, allowing for coherent imaging through a dense grid of synthetic antennas. It also exploits the high azimuth resolution to enhance elevation resolution using learning-based methods. Furthermore, PanoRadar tackles 3D learning via 2D convolutions and addresses challenges due to the unique characteristics of RF signals. Our results demonstrate PanoRadar's robust performance across 12 buildings. | [
"['Haowen Lai' 'Gaoxiang Luo' 'Yifei Liu' 'Mingmin Zhao']"
] |
null | null | 2405.19518 | null | null | http://arxiv.org/pdf/2405.19518v1 | 2024-05-29T20:56:44Z | 2024-05-29T20:56:44Z | Exploring the Potential of Hybrid Machine-Learning/Physics-Based
Modeling for Atmospheric/Oceanic Prediction Beyond the Medium Range | This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022), which tested the approach for short- and medium-range weather prediction, and the work of Arcomano et al. (2023), which investigated its potential for climate modeling. The hybrid model used for the forecast experiments of the paper is based on the low-resolution, simplified parameterization atmospheric general circulation model (AGCM) SPEEDY. In addition to the hybridized prognostic variables of SPEEDY, the current version of the model has three purely ML-based prognostic variables. One of these is 6~h cumulative precipitation, another is the sea surface temperature, while the third is the heat content of the top 300 m deep layer of the ocean. The model has skill in predicting the El Ni~no cycle and its global teleconnections with precipitation for 3-7 months depending on the season. The model captures equatorial variability of the precipitation associated with Kelvin and Rossby waves and MJO. Predictions of the precipitation in the equatorial region have skill for 15 days in the East Pacific and 11.5 days in the West Pacific. Though the model has low spatial resolution, for these tasks it has prediction skill comparable to what has been published for high-resolution, purely physics-based, conventional operational forecast models. | [
"['Dhruvit Patel' 'Troy Arcomano' 'Brian Hunt' 'Istvan Szunyogh'\n 'Edward Ott']"
] |
null | null | 2405.19521 | null | null | http://arxiv.org/pdf/2405.19521v1 | 2024-05-29T20:59:28Z | 2024-05-29T20:59:28Z | Crowdsourcing with Difficulty: A Bayesian Rating Model for Heterogeneous
Items | In applied statistics and machine learning, the "gold standards" used for training are often biased and almost always noisy. Dawid and Skene's justifiably popular crowdsourcing model adjusts for rater (coder, annotator) sensitivity and specificity, but fails to capture distributional properties of rating data gathered for training, which in turn biases training. In this study, we introduce a general purpose measurement-error model with which we can infer consensus categories by adding item-level effects for difficulty, discriminativeness, and guessability. We further show how to constrain the bimodal posterior of these models to avoid (or if necessary, allow) adversarial raters. We validate our model's goodness of fit with posterior predictive checks, the Bayesian analogue of $chi^2$ tests. Dawid and Skene's model is rejected by goodness of fit tests, whereas our new model, which adjusts for item heterogeneity, is not rejected. We illustrate our new model with two well-studied data sets, binary rating data for caries in dental X-rays and implication in natural language. | [
"['Seong Woo Han' 'Ozan Adıgüzel' 'Bob Carpenter']"
] |
null | null | 2405.19531 | null | null | http://arxiv.org/pdf/2405.19531v1 | 2024-05-29T21:20:16Z | 2024-05-29T21:20:16Z | Real-Time Dynamic Robot-Assisted Hand-Object Interaction via Motion
Primitives | Advances in artificial intelligence (AI) have been propelling the evolution of human-robot interaction (HRI) technologies. However, significant challenges remain in achieving seamless interactions, particularly in tasks requiring physical contact with humans. These challenges arise from the need for accurate real-time perception of human actions, adaptive control algorithms for robots, and the effective coordination between human and robotic movements. In this paper, we propose an approach to enhancing physical HRI with a focus on dynamic robot-assisted hand-object interaction (HOI). Our methodology integrates hand pose estimation, adaptive robot control, and motion primitives to facilitate human-robot collaboration. Specifically, we employ a transformer-based algorithm to perform real-time 3D modeling of human hands from single RGB images, based on which a motion primitives model (MPM) is designed to translate human hand motions into robotic actions. The robot's action implementation is dynamically fine-tuned using the continuously updated 3D hand models. Experimental validations, including a ring-wearing task, demonstrate the system's effectiveness in adapting to real-time movements and assisting in precise task executions. | [
"['Mingqi Yuan' 'Huijiang Wang' 'Kai-Fung Chu' 'Fumiya Iida' 'Bo Li'\n 'Wenjun Zeng']"
] |
null | null | 2405.19532 | null | null | http://arxiv.org/pdf/2405.19532v1 | 2024-05-29T21:24:44Z | 2024-05-29T21:24:44Z | Contrasting Multiple Representations with the Multi-Marginal Matching
Gap | Learning meaningful representations of complex objects that can be seen through multiple ($kgeq 3$) views or modalities is a core task in machine learning. Existing methods use losses originally intended for paired views, and extend them to $k$ views, either by instantiating $tfrac12k(k-1)$ loss-pairs, or by using reduced embeddings, following a textit{one vs. average-of-rest} strategy. We propose the multi-marginal matching gap (M3G), a loss that borrows tools from multi-marginal optimal transport (MM-OT) theory to simultaneously incorporate all $k$ views. Given a batch of $n$ points, each seen as a $k$-tuple of views subsequently transformed into $k$ embeddings, our loss contrasts the cost of matching these $n$ ground-truth $k$-tuples with the MM-OT polymatching cost, which seeks $n$ optimally arranged $k$-tuples chosen within these $ntimes k$ vectors. While the exponential complexity $O(n^k$) of the MM-OT problem may seem daunting, we show in experiments that a suitable generalization of the Sinkhorn algorithm for that problem can scale to, e.g., $k=3sim 6$ views using mini-batches of size $64~sim128$. Our experiments demonstrate improved performance over multiview extensions of pairwise losses, for both self-supervised and multimodal tasks. | [
"['Zoe Piran' 'Michal Klein' 'James Thornton' 'Marco Cuturi']"
] |
null | null | 2405.19534 | null | null | http://arxiv.org/pdf/2405.19534v1 | 2024-05-29T21:29:44Z | 2024-05-29T21:29:44Z | Preference Learning Algorithms Do Not Learn Preference Rankings | Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner workings is still limited. In this work, we study the conventional wisdom that preference learning trains models to assign higher likelihoods to more preferred outputs than less preferred outputs, measured via $textit{ranking accuracy}$. Surprisingly, we find that most state-of-the-art preference-tuned models achieve a ranking accuracy of less than 60% on common preference datasets. We furthermore derive the $textit{idealized ranking accuracy}$ that a preference-tuned LLM would achieve if it optimized the DPO or RLHF objective perfectly. We demonstrate that existing models exhibit a significant $textit{alignment gap}$ -- $textit{i.e.}$, a gap between the observed and idealized ranking accuracies. We attribute this discrepancy to the DPO objective, which is empirically and theoretically ill-suited to fix even mild ranking errors in the reference model, and derive a simple and efficient formula for quantifying the difficulty of learning a given preference datapoint. Finally, we demonstrate that ranking accuracy strongly correlates with the empirically popular win rate metric when the model is close to the reference model used in the objective, shedding further light on the differences between on-policy (e.g., RLHF) and off-policy (e.g., DPO) preference learning algorithms. | [
"['Angelica Chen' 'Sadhika Malladi' 'Lily H. Zhang' 'Xinyi Chen'\n 'Qiuyi Zhang' 'Rajesh Ranganath' 'Kyunghyun Cho']"
] |
null | null | 2405.19538 | null | null | http://arxiv.org/pdf/2405.19538v2 | 2024-06-03T19:14:12Z | 2024-05-29T21:48:56Z | CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text
Radiology Reports, Patient Demographics and Additional Image Formats | Since the release of the original CheXpert paper five years ago, CheXpert has become one of the most widely used and cited clinical AI datasets. The emergence of vision language models has sparked an increase in demands for sharing reports linked to CheXpert images, along with a growing interest among AI fairness researchers in obtaining demographic data. To address this, CheXpert Plus serves as a new collection of radiology data sources, made publicly available to enhance the scaling, performance, robustness, and fairness of models for all subsequent machine learning tasks in the field of radiology. CheXpert Plus is the largest text dataset publicly released in radiology, with a total of 36 million text tokens, including 13 million impression tokens. To the best of our knowledge, it represents the largest text de-identification effort in radiology, with almost 1 million PHI spans anonymized. It is only the second time that a large-scale English paired dataset has been released in radiology, thereby enabling, for the first time, cross-institution training at scale. All reports are paired with high-quality images in DICOM format, along with numerous image and patient metadata covering various clinical and socio-economic groups, as well as many pathology labels and RadGraph annotations. We hope this dataset will boost research for AI models that can further assist radiologists and help improve medical care. Data is available at the following URL: https://stanfordaimi.azurewebsites.net/datasets/5158c524-d3ab-4e02-96e9-6ee9efc110a1 Models are available at the following URL: https://github.com/Stanford-AIMI/chexpert-plus | [
"['Pierre Chambon' 'Jean-Benoit Delbrouck' 'Thomas Sounack'\n 'Shih-Cheng Huang' 'Zhihong Chen' 'Maya Varma' 'Steven QH Truong'\n 'Chu The Chuong' 'Curtis P. Langlotz']"
] |
null | null | 2405.19542 | null | null | http://arxiv.org/pdf/2405.19542v2 | 2024-05-31T11:31:12Z | 2024-05-29T22:04:40Z | Anatomical Region Recognition and Real-time Bone Tracking Methods by
Dynamically Decoding A-Mode Ultrasound Signals | Accurate bone tracking is crucial for kinematic analysis in orthopedic surgery and prosthetic robotics. Traditional methods (e.g., skin markers) are subject to soft tissue artifacts, and the bone pins used in surgery introduce the risk of additional trauma and infection. For electromyography (EMG), its inability to directly measure joint angles requires complex algorithms for kinematic estimation. To address these issues, A-mode ultrasound-based tracking has been proposed as a non-invasive and safe alternative. However, this approach suffers from limited accuracy in peak detection when processing received ultrasound signals. To build a precise and real-time bone tracking approach, this paper introduces a deep learning-based method for anatomical region recognition and bone tracking using A-mode ultrasound signals, specifically focused on the knee joint. The algorithm is capable of simultaneously performing bone tracking and identifying the anatomical region where the A-mode ultrasound transducer is placed. It contains the fully connection between all encoding and decoding layers of the cascaded U-Nets to focus only on the signal region that is most likely to have the bone peak, thus pinpointing the exact location of the peak and classifying the anatomical region of the signal. The experiment showed a 97% accuracy in the classification of the anatomical regions and a precision of around 0.5$pm$1mm under dynamic tracking conditions for various anatomical areas surrounding the knee joint. In general, this approach shows great potential beyond the traditional method, in terms of the accuracy achieved and the recognition of the anatomical region where the ultrasound has been attached as an additional functionality. | [
"['Bangyu Lan' 'Stefano Stramigioli' 'Kenan Niu']"
] |
null | null | 2405.19544 | null | null | http://arxiv.org/pdf/2405.19544v1 | 2024-05-29T22:12:52Z | 2024-05-29T22:12:52Z | One-Shot Safety Alignment for Large Language Models via Optimal
Dualization | The growing safety concerns surrounding Large Language Models (LLMs) raise an urgent need to align them with diverse human preferences to simultaneously enhance their helpfulness and safety. A promising approach is to enforce safety constraints through Reinforcement Learning from Human Feedback (RLHF). For such constrained RLHF, common Lagrangian-based primal-dual policy optimization methods are computationally expensive and often unstable. This paper presents a dualization perspective that reduces constrained alignment to an equivalent unconstrained alignment problem. We do so by pre-optimizing a smooth and convex dual function that has a closed form. This shortcut eliminates the need for cumbersome primal-dual policy iterations, thus greatly reducing the computational burden and improving training stability. Our strategy leads to two practical algorithms in model-based and preference-based scenarios (MoCAN and PeCAN, respectively). A broad range of experiments demonstrate the effectiveness of our methods. | [
"['Xinmeng Huang' 'Shuo Li' 'Edgar Dobriban' 'Osbert Bastani'\n 'Hamed Hassani' 'Dongsheng Ding']"
] |
null | null | 2405.19547 | null | null | http://arxiv.org/pdf/2405.19547v1 | 2024-05-29T22:19:57Z | 2024-05-29T22:19:57Z | CLIPLoss and Norm-Based Data Selection Methods for Multimodal
Contrastive Learning | Data selection has emerged as a core issue for large-scale visual-language model pretaining (e.g., CLIP), particularly with noisy web-curated datasets. Three main data selection approaches are: (1) leveraging external non-CLIP models to aid data selection, (2) training new CLIP-style embedding models that are more effective at selecting high-quality data than the original OpenAI CLIP model, and (3) designing better metrics or strategies universally applicable to any CLIP embedding without requiring specific model properties (e.g., CLIPScore is one popular metric). While the first two approaches have been extensively studied, the third remains under-explored. In this paper, we advance the third approach by proposing two new methods. Firstly, instead of classical CLIP scores that only consider the alignment between two modalities from a single sample, we introduce negCLIPLoss, a CLIP loss-inspired method that adds the alignment between one sample and its contrastive pairs as an extra normalization term for better quality measurement. Secondly, when downstream tasks are known, we propose a new norm-based metric, NormSim, to measure the similarity between pretraining data and target data. We test our methods on the data selection benchmark, DataComp~cite{gadre2023datacomp}. Compared to the best baseline using only OpenAI's CLIP-L/14, our methods achieve a 5.3% improvement on ImageNet-1k and a 2.8% improvement on 38 downstream evaluation tasks. Moreover, both negCLIPLoss and NormSim are compatible with existing techniques. By combining our methods with the current best methods DFN~cite{fang2023data} and HYPE~cite{kim2024hype}, we can boost average performance on downstream tasks by 0.9%, achieving a new state-of-the-art. | [
"['Yiping Wang' 'Yifang Chen' 'Wendan Yan' 'Alex Fang' 'Wenjing Zhou'\n 'Kevin Jamieson' 'Simon Shaolei Du']"
] |
null | null | 2405.19548 | null | null | http://arxiv.org/pdf/2405.19548v1 | 2024-05-29T22:23:20Z | 2024-05-29T22:23:20Z | RLeXplore: Accelerating Research in Intrinsically-Motivated
Reinforcement Learning | Extrinsic rewards can effectively guide reinforcement learning (RL) agents in specific tasks. However, extrinsic rewards frequently fall short in complex environments due to the significant human effort needed for their design and annotation. This limitation underscores the necessity for intrinsic rewards, which offer auxiliary and dense signals and can enable agents to learn in an unsupervised manner. Although various intrinsic reward formulations have been proposed, their implementation and optimization details are insufficiently explored and lack standardization, thereby hindering research progress. To address this gap, we introduce RLeXplore, a unified, highly modularized, and plug-and-play framework offering reliable implementations of eight state-of-the-art intrinsic reward algorithms. Furthermore, we conduct an in-depth study that identifies critical implementation details and establishes well-justified standard practices in intrinsically-motivated RL. The source code for RLeXplore is available at https://github.com/RLE-Foundation/RLeXplore. | [
"['Mingqi Yuan' 'Roger Creus Castanyer' 'Bo Li' 'Xin Jin' 'Glen Berseth'\n 'Wenjun Zeng']"
] |
null | null | 2405.19550 | null | null | http://arxiv.org/pdf/2405.19550v1 | 2024-05-29T22:26:26Z | 2024-05-29T22:26:26Z | Stress-Testing Capability Elicitation With Password-Locked Models | To determine the safety of large language models (LLMs), AI developers must be able to assess their dangerous capabilities. But simple prompting strategies often fail to elicit an LLM's full capabilities. One way to elicit capabilities more robustly is to fine-tune the LLM to complete the task. In this paper, we investigate the conditions under which fine-tuning-based elicitation suffices to elicit capabilities. To do this, we introduce password-locked models, LLMs fine-tuned such that some of their capabilities are deliberately hidden. Specifically, these LLMs are trained to exhibit these capabilities only when a password is present in the prompt, and to imitate a much weaker LLM otherwise. Password-locked models enable a novel method of evaluating capabilities elicitation methods, by testing whether these password-locked capabilities can be elicited without using the password. We find that a few high-quality demonstrations are often sufficient to fully elicit password-locked capabilities. More surprisingly, fine-tuning can elicit other capabilities that have been locked using the same password, or even different passwords. Furthermore, when only evaluations, and not demonstrations, are available, approaches like reinforcement learning are still often able to elicit capabilities. Overall, our findings suggest that fine-tuning is an effective method of eliciting hidden capabilities of current models, but may be unreliable when high-quality demonstrations are not available, e.g. as may be the case when models' (hidden) capabilities exceed those of human demonstrators. | [
"['Ryan Greenblatt' 'Fabien Roger' 'Dmitrii Krasheninnikov' 'David Krueger']"
] |
null | null | 2405.19553 | null | null | http://arxiv.org/pdf/2405.19553v1 | 2024-05-29T22:43:45Z | 2024-05-29T22:43:45Z | Convergence Bounds for Sequential Monte Carlo on Multimodal
Distributions using Soft Decomposition | We prove bounds on the variance of a function $f$ under the empirical measure of the samples obtained by the Sequential Monte Carlo (SMC) algorithm, with time complexity depending on local rather than global Markov chain mixing dynamics. SMC is a Markov Chain Monte Carlo (MCMC) method, which starts by drawing $N$ particles from a known distribution, and then, through a sequence of distributions, re-weights and re-samples the particles, at each instance applying a Markov chain for smoothing. In principle, SMC tries to alleviate problems from multi-modality. However, most theoretical guarantees for SMC are obtained by assuming global mixing time bounds, which are only efficient in the uni-modal setting. We show that bounds can be obtained in the truly multi-modal setting, with mixing times that depend only on local MCMC dynamics. | [
"['Holden Lee' 'Matheau Santana-Gijzen']"
] |
null | null | 2405.19559 | null | null | http://arxiv.org/pdf/2405.19559v1 | 2024-05-29T22:55:45Z | 2024-05-29T22:55:45Z | Clustering Mixtures of Discrete Distributions: A Note on Mitra's
Algorithm | In this note, we provide a refined analysis of Mitra's algorithm cite{mitra2008clustering} for classifying general discrete mixture distribution models. Built upon spectral clustering cite{mcsherry2001spectral}, this algorithm offers compelling conditions for probability distributions. We enhance this analysis by tailoring the model to bipartite stochastic block models, resulting in more refined conditions. Compared to those derived in cite{mitra2008clustering}, our improved separation conditions are obtained. | [
"['Mohamed Seif' 'Yanxi Chen']"
] |
null | null | 2405.19562 | null | null | http://arxiv.org/pdf/2405.19562v1 | 2024-05-29T23:08:31Z | 2024-05-29T23:08:31Z | Selective Explanations | Feature attribution methods explain black-box machine learning (ML) models by assigning importance scores to input features. These methods can be computationally expensive for large ML models. To address this challenge, there has been increasing efforts to develop amortized explainers, where a machine learning model is trained to predict feature attribution scores with only one inference. Despite their efficiency, amortized explainers can produce inaccurate predictions and misleading explanations. In this paper, we propose selective explanations, a novel feature attribution method that (i) detects when amortized explainers generate low-quality explanations and (ii) improves these explanations using a technique called explanations with initial guess. Our selective explanation method allows practitioners to specify the fraction of samples that receive explanations with initial guess, offering a principled way to bridge the gap between amortized explainers and their high-quality counterparts. | [
"['Lucas Monteiro Paes' 'Dennis Wei' 'Flavio P. Calmon']"
] |
null | null | 2405.19567 | null | null | http://arxiv.org/pdf/2405.19567v1 | 2024-05-29T23:19:28Z | 2024-05-29T23:19:28Z | Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding | Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions to assist in diagnostic and treatment tasks. However, VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. This challenge is particularly pronounced in the medical domain, where we do not only require VLM outputs to be accurate in single interactions but also to be consistent with clinical reasoning and diagnostic pathways throughout multi-turn conversations. For this purpose, we propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge. These representations are utilized to (i) generate GPT-4-guided visual instruction tuning data at scale, simulating clinician-VLM conversations with demonstrations of clinical reasoning, and (ii) create an automatic reward function that evaluates the clinical validity of VLM generations throughout clinician-VLM interactions. Our algorithm eliminates the need for human involvement in training data generation or reward model construction, reducing costs compared to standard reinforcement learning with human feedback (RLHF). We apply our alignment algorithm to develop Dr-LLaVA, a conversational VLM finetuned for analyzing bone marrow pathology slides, demonstrating strong performance in multi-turn medical conversations. | [
"['Shenghuan Sun' 'Gregory M. Goldgof' 'Alexander Schubert' 'Zhiqing Sun'\n 'Thomas Hartvigsen' 'Atul J. Butte' 'Ahmed Alaa']"
] |
null | null | 2405.19586 | null | null | http://arxiv.org/pdf/2405.19586v1 | 2024-05-30T00:32:51Z | 2024-05-30T00:32:51Z | SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for
Embodied Manipulation | Acquiring a multi-task imitation policy in 3D manipulation poses challenges in terms of scene understanding and action prediction. Current methods employ both 3D representation and multi-view 2D representation to predict the poses of the robot's end-effector. However, they still require a considerable amount of high-quality robot trajectories, and suffer from limited generalization in unseen tasks and inefficient execution in long-horizon reasoning. In this paper, we propose SAM-E, a novel architecture for robot manipulation by leveraging a vision-foundation model for generalizable scene understanding and sequence imitation for long-term action reasoning. Specifically, we adopt Segment Anything (SAM) pre-trained on a huge number of images and promptable masks as the foundation model for extracting task-relevant features, and employ parameter-efficient fine-tuning on robot data for a better understanding of embodied scenarios. To address long-horizon reasoning, we develop a novel multi-channel heatmap that enables the prediction of the action sequence in a single pass, notably enhancing execution efficiency. Experimental results from various instruction-following tasks demonstrate that SAM-E achieves superior performance with higher execution efficiency compared to the baselines, and also significantly improves generalization in few-shot adaptation to new tasks. | [
"['Junjie Zhang' 'Chenjia Bai' 'Haoran He' 'Wenke Xia' 'Zhigang Wang'\n 'Bin Zhao' 'Xiu Li' 'Xuelong Li']"
] |
null | null | 2405.19590 | null | null | http://arxiv.org/pdf/2405.19590v1 | 2024-05-30T00:57:06Z | 2024-05-30T00:57:06Z | Weights Augmentation: it has never ever ever ever let her model down | Weight play an essential role in deep learning network models. Unlike network structure design, this article proposes the concept of weight augmentation, focusing on weight exploration. The core of Weight Augmentation Strategy (WAS) is to adopt random transformed weight coefficients training and transformed coefficients, named Shadow Weight(SW), for networks that can be used to calculate loss function to affect parameter updates. However, stochastic gradient descent is applied to Plain Weight(PW), which is referred to as the original weight of the network before the random transformation. During training, numerous SW collectively form high-dimensional space, while PW is directly learned from the distribution of SW instead of the data. The weight of the accuracy-oriented mode(AOM) relies on PW, which guarantees the network is highly robust and accurate. The desire-oriented mode(DOM) weight uses SW, which is determined by the network model's unique functions based on WAT's performance desires, such as lower computational complexity, lower sensitivity to particular data, etc. The dual mode be switched at anytime if needed. WAT extends the augmentation technique from data augmentation to weight, and it is easy to understand and implement, but it can improve almost all networks amazingly. Our experimental results show that convolutional neural networks, such as VGG-16, ResNet-18, ResNet-34, GoogleNet, MobilementV2, and Efficientment-Lite, can benefit much at little or no cost. The accuracy of models is on the CIFAR100 and CIFAR10 datasets, which can be evaluated to increase by 7.32% and 9.28%, respectively, with the highest values being 13.42% and 18.93%, respectively. In addition, DOM can reduce floating point operations (FLOPs) by up to 36.33%. The code is available at https://github.com/zlearh/Weight-Augmentation-Technology. | [
"['Junbin Zhuang' 'Guiguang Din' 'Yunyi Yan']"
] |
null | null | 2405.19592 | null | null | http://arxiv.org/pdf/2405.19592v1 | 2024-05-30T01:11:35Z | 2024-05-30T01:11:35Z | Why Larger Language Models Do In-context Learning Differently? | Large language models (LLM) have emerged as a powerful tool for AI, with the key ability of in-context learning (ICL), where they can perform well on unseen tasks based on a brief series of task examples without necessitating any adjustments to the model parameters. One recent interesting mysterious observation is that models of different scales may have different ICL behaviors: larger models tend to be more sensitive to noise in the test context. This work studies this observation theoretically aiming to improve the understanding of LLM and ICL. We analyze two stylized settings: (1) linear regression with one-layer single-head linear transformers and (2) parity classification with two-layer multiple attention heads transformers (non-linear data and non-linear model). In both settings, we give closed-form optimal solutions and find that smaller models emphasize important hidden features while larger ones cover more hidden features; thus, smaller models are more robust to noise while larger ones are more easily distracted, leading to different ICL behaviors. This sheds light on where transformers pay attention to and how that affects ICL. Preliminary experimental results on large base and chat models provide positive support for our analysis. | [
"['Zhenmei Shi' 'Junyi Wei' 'Zhuoyan Xu' 'Yingyu Liang']"
] |
null | null | 2405.19597 | null | null | http://arxiv.org/pdf/2405.19597v1 | 2024-05-30T01:27:43Z | 2024-05-30T01:27:43Z | SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors | Popular parameter-efficient fine-tuning (PEFT) methods, such as LoRA and its variants, freeze pre-trained model weights (W) and inject learnable matrices (Delta W). These (Delta W) matrices are structured for efficient parameterization, often using techniques like low-rank approximations or scaling vectors. However, these methods typically show a performance gap compared to full fine-tuning. Although recent PEFT methods have narrowed this gap, they do so at the cost of additional learnable parameters. We propose SVFT, a simple approach that fundamentally differs from existing methods: the structure imposed on (Delta W) depends on the specific weight matrix (W). Specifically, SVFT updates (W) as a sparse combination of outer products of its singular vectors, training only the coefficients (scales) of these sparse combinations. This approach allows fine-grained control over expressivity through the number of coefficients. Extensive experiments on language and vision benchmarks show that SVFT recovers up to 96% of full fine-tuning performance while training only 0.006 to 0.25% of parameters, outperforming existing methods that only recover up to 85% performance using 0.03 to 0.8% of the trainable parameter budget. | [
"['Vijay Lingam' 'Atula Tejaswi' 'Aditya Vavre' 'Aneesh Shetty'\n 'Gautham Krishna Gudur' 'Joydeep Ghosh' 'Alex Dimakis' 'Eunsol Choi'\n 'Aleksandar Bojchevski' 'Sujay Sanghavi']"
] |
null | null | 2405.19600 | null | null | http://arxiv.org/pdf/2405.19600v1 | 2024-05-30T01:30:34Z | 2024-05-30T01:30:34Z | Do spectral cues matter in contrast-based graph self-supervised
learning? | The recent surge in contrast-based graph self-supervised learning has prominently featured an intensified exploration of spectral cues. However, an intriguing paradox emerges, as methods grounded in seemingly conflicting assumptions or heuristic approaches regarding the spectral domain demonstrate notable enhancements in learning performance. This paradox prompts a critical inquiry into the genuine contribution of spectral information to contrast-based graph self-supervised learning. This study undertakes an extensive investigation into this inquiry, conducting a thorough study of the relationship between spectral characteristics and the learning outcomes of contemporary methodologies. Based on this analysis, we claim that the effectiveness and significance of spectral information need to be questioned. Instead, we revisit simple edge perturbation: random edge dropping designed for node-level self-supervised learning and random edge adding intended for graph-level self-supervised learning. Compelling evidence is presented that these simple yet effective strategies consistently yield superior performance while demanding significantly fewer computational resources compared to all prior spectral augmentation methods. The proposed insights represent a significant leap forward in the field, potentially reshaping the understanding and implementation of graph self-supervised learning. | [
"['Xiangru Jian' 'Xinjian Zhao' 'Wei Pang' 'Chaolong Ying' 'Yimu Wang'\n 'Yaoyao Xu' 'Tianshu Yu']"
] |
null | null | 2405.19610 | null | null | http://arxiv.org/pdf/2405.19610v1 | 2024-05-30T01:56:49Z | 2024-05-30T01:56:49Z | Factor Augmented Tensor-on-Tensor Neural Networks | This paper studies the prediction task of tensor-on-tensor regression in which both covariates and responses are multi-dimensional arrays (a.k.a., tensors) across time with arbitrary tensor order and data dimension. Existing methods either focused on linear models without accounting for possibly nonlinear relationships between covariates and responses, or directly employed black-box deep learning algorithms that failed to utilize the inherent tensor structure. In this work, we propose a Factor Augmented Tensor-on-Tensor Neural Network (FATTNN) that integrates tensor factor models into deep neural networks. We begin with summarizing and extracting useful predictive information (represented by the ``factor tensor'') from the complex structured tensor covariates, and then proceed with the prediction task using the estimated factor tensor as input of a temporal convolutional neural network. The proposed methods effectively handle nonlinearity between complex data structures, and improve over traditional statistical models and conventional deep learning approaches in both prediction accuracy and computational cost. By leveraging tensor factor models, our proposed methods exploit the underlying latent factor structure to enhance the prediction, and in the meantime, drastically reduce the data dimensionality that speeds up the computation. The empirical performances of our proposed methods are demonstrated via simulation studies and real-world applications to three public datasets. Numerical results show that our proposed algorithms achieve substantial increases in prediction accuracy and significant reductions in computational time compared to benchmark methods. | [
"['Guanhao Zhou' 'Yuefeng Han' 'Xiufan Yu']"
] |
null | null | 2405.19616 | null | null | http://arxiv.org/pdf/2405.19616v2 | 2024-06-01T03:00:37Z | 2024-05-30T02:09:51Z | Easy Problems That LLMs Get Wrong | We introduce a comprehensive Linguistic Benchmark designed to evaluate the limitations of Large Language Models (LLMs) in domains such as logical reasoning, spatial intelligence, and linguistic understanding, among others. Through a series of straightforward questions, it uncovers the significant limitations of well-regarded models to perform tasks that humans manage with ease. It also highlights the potential of prompt engineering to mitigate some errors and underscores the necessity for better training methodologies. Our findings stress the importance of grounding LLMs with human reasoning and common sense, emphasising the need for human-in-the-loop for enterprise applications. We hope this work paves the way for future research to enhance the usefulness and reliability of new models. | [
"['Sean Williams' 'James Huckle']"
] |
null | null | 2405.19644 | null | null | http://arxiv.org/pdf/2405.19644v1 | 2024-05-30T02:53:19Z | 2024-05-30T02:53:19Z | EgoSurgery-Phase: A Dataset of Surgical Phase Recognition from
Egocentric Open Surgery Videos | Surgical phase recognition has gained significant attention due to its potential to offer solutions to numerous demands of the modern operating room. However, most existing methods concentrate on minimally invasive surgery (MIS), leaving surgical phase recognition for open surgery understudied. This discrepancy is primarily attributed to the scarcity of publicly available open surgery video datasets for surgical phase recognition. To address this issue, we introduce a new egocentric open surgery video dataset for phase recognition, named EgoSurgery-Phase. This dataset comprises 15 hours of real open surgery videos spanning 9 distinct surgical phases all captured using an egocentric camera attached to the surgeon's head. In addition to video, the EgoSurgery-Phase offers eye gaze. As far as we know, it is the first real open surgery video dataset for surgical phase recognition publicly available. Furthermore, inspired by the notable success of masked autoencoders (MAEs) in video understanding tasks (e.g., action recognition), we propose a gaze-guided masked autoencoder (GGMAE). Considering the regions where surgeons' gaze focuses are often critical for surgical phase recognition (e.g., surgical field), in our GGMAE, the gaze information acts as an empirical semantic richness prior to guiding the masking process, promoting better attention to semantically rich spatial regions. GGMAE significantly improves the previous state-of-the-art recognition method (6.4% in Jaccard) and the masked autoencoder-based method (3.1% in Jaccard) on EgoSurgery-Phase. The dataset will be released at https://github.com/Fujiry0/EgoSurgery. | [
"['Ryo Fujii' 'Masashi Hatano' 'Hideo Saito' 'Hiroki Kajita']"
] |
null | null | 2405.19647 | null | null | http://arxiv.org/pdf/2405.19647v1 | 2024-05-30T02:59:49Z | 2024-05-30T02:59:49Z | FTS: A Framework to Find a Faithful TimeSieve | The field of time series forecasting has garnered significant attention in recent years, prompting the development of advanced models like TimeSieve, which demonstrates impressive performance. However, an analysis reveals certain unfaithfulness issues, including high sensitivity to random seeds and minute input noise perturbations. Recognizing these challenges, we embark on a quest to define the concept of textbf{underline{F}aithful underline{T}imeunderline{S}ieve underline{(FTS)}}, a model that consistently delivers reliable and robust predictions. To address these issues, we propose a novel framework aimed at identifying and rectifying unfaithfulness in TimeSieve. Our framework is designed to enhance the model's stability and resilience, ensuring that its outputs are less susceptible to the aforementioned factors. Experimentation validates the effectiveness of our proposed framework, demonstrating improved faithfulness in the model's behavior. Looking forward, we plan to expand our experimental scope to further validate and optimize our algorithm, ensuring comprehensive faithfulness across a wide range of scenarios. Ultimately, we aspire to make this framework can be applied to enhance the faithfulness of not just TimeSieve but also other state-of-the-art temporal methods, thereby contributing to the reliability and robustness of temporal modeling as a whole. | [
"['Songning Lai' 'Ninghui Feng' 'Haochen Sui' 'Ze Ma' 'Hao Wang'\n 'Zichen Song' 'Hang Zhao' 'Yutao Yue']"
] |
null | null | 2405.19648 | null | null | http://arxiv.org/pdf/2405.19648v1 | 2024-05-30T03:00:47Z | 2024-05-30T03:00:47Z | Detecting Hallucinations in Large Language Model Generation: A Token
Probability Approach | Concerns regarding the propensity of Large Language Models (LLMs) to produce inaccurate outputs, also known as hallucinations, have escalated. Detecting them is vital for ensuring the reliability of applications relying on LLM-generated content. Current methods often demand substantial resources and rely on extensive LLMs or employ supervised learning with multidimensional features or intricate linguistic and semantic analyses difficult to reproduce and largely depend on using the same LLM that hallucinated. This paper introduces a supervised learning approach employing two simple classifiers utilizing only four numerical features derived from tokens and vocabulary probabilities obtained from other LLM evaluators, which are not necessarily the same. The method yields promising results, surpassing state-of-the-art outcomes in multiple tasks across three different benchmarks. Additionally, we provide a comprehensive examination of the strengths and weaknesses of our approach, highlighting the significance of the features utilized and the LLM employed as an evaluator. We have released our code publicly at https://github.com/Baylor-AI/HalluDetect. | [
"['Ernesto Quevedo' 'Jorge Yero' 'Rachel Koerner' 'Pablo Rivas'\n 'Tomas Cerny']"
] |
null | null | 2405.19649 | null | null | http://arxiv.org/abs/2405.19649v1 | 2024-05-30T03:02:23Z | 2024-05-30T03:02:23Z | Towards Deeper Understanding of PPR-based Embedding Approaches: A
Topological Perspective | Node embedding learns low-dimensional vectors for nodes in the graph. Recent state-of-the-art embedding approaches take Personalized PageRank (PPR) as the proximity measure and factorize the PPR matrix or its adaptation to generate embeddings. However, little previous work analyzes what information is encoded by these approaches, and how the information correlates with their superb performance in downstream tasks. In this work, we first show that state-of-the-art embedding approaches that factorize a PPR-related matrix can be unified into a closed-form framework. Then, we study whether the embeddings generated by this strategy can be inverted to better recover the graph topology information than random-walk based embeddings. To achieve this, we propose two methods for recovering graph topology via PPR-based embeddings, including the analytical method and the optimization method. Extensive experimental results demonstrate that the embeddings generated by factorizing a PPR-related matrix maintain more topological information, such as common edges and community structures, than that generated by random walks, paving a new way to systematically comprehend why PPR-based node embedding approaches outperform random walk-based alternatives in various downstream tasks. To the best of our knowledge, this is the first work that focuses on the interpretability of PPR-based node embedding approaches. | [
"['Xingyi Zhang' 'Zixuan Weng' 'Sibo Wang']"
] |
null | null | 2405.19650 | null | null | http://arxiv.org/pdf/2405.19650v1 | 2024-05-30T03:04:57Z | 2024-05-30T03:04:57Z | Few for Many: Tchebycheff Set Scalarization for Many-Objective
Optimization | Multi-objective optimization can be found in many real-world applications where some conflicting objectives can not be optimized by a single solution. Existing optimization methods often focus on finding a set of Pareto solutions with different optimal trade-offs among the objectives. However, the required number of solutions to well approximate the whole Pareto optimal set could be exponentially large with respect to the number of objectives, which makes these methods unsuitable for handling many optimization objectives. In this work, instead of finding a dense set of Pareto solutions, we propose a novel Tchebycheff set scalarization method to find a few representative solutions (e.g., 5) to cover a large number of objectives (e.g., $>100$) in a collaborative and complementary manner. In this way, each objective can be well addressed by at least one solution in the small solution set. In addition, we further develop a smooth Tchebycheff set scalarization approach for efficient optimization with good theoretical guarantees. Experimental studies on different problems with many optimization objectives demonstrate the effectiveness of our proposed method. | [
"['Xi Lin' 'Yilu Liu' 'Xiaoyuan Zhang' 'Fei Liu' 'Zhenkun Wang'\n 'Qingfu Zhang']"
] |
null | null | 2405.19653 | null | null | http://arxiv.org/pdf/2405.19653v1 | 2024-05-30T03:12:04Z | 2024-05-30T03:12:04Z | SysCaps: Language Interfaces for Simulation Surrogates of Complex
Systems | Data-driven simulation surrogates help computational scientists study complex systems. They can also help inform impactful policy decisions. We introduce a learning framework for surrogate modeling where language is used to interface with the underlying system being simulated. We call a language description of a system a "system caption", or SysCap. To address the lack of datasets of paired natural language SysCaps and simulation runs, we use large language models (LLMs) to synthesize high-quality captions. Using our framework, we train multimodal text and timeseries regression models for two real-world simulators of complex energy systems. Our experiments demonstrate the feasibility of designing language interfaces for real-world surrogate models at comparable accuracy to standard baselines. We qualitatively and quantitatively show that SysCaps unlock text-prompt-style surrogate modeling and new generalization abilities beyond what was previously possible. We will release the generated SysCaps datasets and our code to support follow-on studies. | [
"['Patrick Emami' 'Zhaonan Li' 'Saumya Sinha' 'Truc Nguyen']"
] |
null | null | 2405.19661 | null | null | http://arxiv.org/pdf/2405.19661v1 | 2024-05-30T03:32:44Z | 2024-05-30T03:32:44Z | MGCP: A Multi-Grained Correlation based Prediction Network for
Multivariate Time Series | Multivariate time series prediction is widely used in daily life, which poses significant challenges due to the complex correlations that exist at multi-grained levels. Unfortunately, the majority of current time series prediction models fail to simultaneously learn the correlations of multivariate time series at multi-grained levels, resulting in suboptimal performance. To address this, we propose a Multi-Grained Correlations-based Prediction (MGCP) Network, which simultaneously considers the correlations at three granularity levels to enhance prediction performance. Specifically, MGCP utilizes Adaptive Fourier Neural Operators and Graph Convolutional Networks to learn the global spatiotemporal correlations and inter-series correlations, enabling the extraction of potential features from multivariate time series at fine-grained and medium-grained levels. Additionally, MGCP employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level, ensuring high fidelity between the generated forecast results and the actual data distribution. Finally, we compare MGCP with several state-of-the-art time series prediction algorithms on real-world benchmark datasets, and our results demonstrate the generality and effectiveness of the proposed model. | [
"['Zhicheng Chen' 'Xi Xiao' 'Ke Xu' 'Zhong Zhang' 'Yu Rong' 'Qing Li'\n 'Guojun Gan' 'Zhiqiang Xu' 'Peilin Zhao']"
] |
null | null | 2405.19665 | null | null | http://arxiv.org/pdf/2405.19665v1 | 2024-05-30T03:33:49Z | 2024-05-30T03:33:49Z | A novel fault localization with data refinement for hydroelectric units | Due to the scarcity of fault samples and the complexity of non-linear and non-smooth characteristics data in hydroelectric units, most of the traditional hydroelectric unit fault localization methods are difficult to carry out accurate localization. To address these problems, a sparse autoencoder (SAE)-generative adversarial network (GAN)-wavelet noise reduction (WNR)- manifold-boosted deep learning (SG-WMBDL) based fault localization method for hydroelectric units is proposed. To overcome the data scarcity, a SAE is embedded into the GAN to generate more high-quality samples in the data generation module. Considering the signals involving non-linear and non-smooth characteristics, the improved WNR which combining both soft and hard thresholding and local linear embedding (LLE) are utilized to the data preprocessing module in order to reduce the noise and effectively capture the local features. In addition, to seek higher performance, the novel Adaptive Boost (AdaBoost) combined with multi deep learning is proposed to achieve accurate fault localization. The experimental results show that the SG-WMBDL can locate faults for hydroelectric units under a small number of fault samples with non-linear and non-smooth characteristics on higher precision and accuracy compared to other frontier methods, which verifies the effectiveness and practicality of the proposed method. | [
"['Jialong Huang' 'Junlin Song' 'Penglong Lian' 'Mengjie Gan' 'Zhiheng Su'\n 'Benhao Wang' 'Wenji Zhu' 'Xiaomin Pu' 'Jianxiao Zou' 'Shicai Fan']"
] |
null | null | 2405.19667 | null | null | http://arxiv.org/pdf/2405.19667v1 | 2024-05-30T03:36:46Z | 2024-05-30T03:36:46Z | Reconciling Model Multiplicity for Downstream Decision Making | We consider the problem of model multiplicity in downstream decision-making, a setting where two predictive models of equivalent accuracy cannot agree on the best-response action for a downstream loss function. We show that even when the two predictive models approximately agree on their individual predictions almost everywhere, it is still possible for their induced best-response actions to differ on a substantial portion of the population. We address this issue by proposing a framework that calibrates the predictive models with regard to both the downstream decision-making problem and the individual probability prediction. Specifically, leveraging tools from multi-calibration, we provide an algorithm that, at each time-step, first reconciles the differences in individual probability prediction, then calibrates the updated models such that they are indistinguishable from the true probability distribution to the decision-maker. We extend our results to the setting where one does not have direct access to the true probability distribution and instead relies on a set of i.i.d data to be the empirical distribution. Finally, we provide a set of experiments to empirically evaluate our methods: compared to existing work, our proposed algorithm creates a pair of predictive models with both improved downstream decision-making losses and agrees on their best-response actions almost everywhere. | [
"['Ally Yalei Du' 'Dung Daniel Ngo' 'Zhiwei Steven Wu']"
] |
null | null | 2405.19672 | null | null | http://arxiv.org/pdf/2405.19672v1 | 2024-05-30T03:56:01Z | 2024-05-30T03:56:01Z | CRIS: Collaborative Refinement Integrated with Segmentation for Polyp
Segmentation | Accurate detection of colorectal cancer and early prevention heavily rely on precise polyp identification during gastrointestinal colonoscopy. Due to limited data, many current state-of-the-art deep learning methods for polyp segmentation often rely on post-processing of masks to reduce noise and enhance results. In this study, we propose an approach that integrates mask refinement and binary semantic segmentation, leveraging a novel collaborative training strategy that surpasses current widely-used refinement strategies. We demonstrate the superiority of our approach through comprehensive evaluation on established benchmark datasets and its successful application across various medical image segmentation architectures. | [
"['Ankush Gajanan Arudkar' 'Bernard J. E. Evans']"
] |
null | null | 2405.19673 | null | null | http://arxiv.org/pdf/2405.19673v2 | 2024-05-31T18:34:35Z | 2024-05-30T03:57:29Z | Bridging Model-Based Optimization and Generative Modeling via
Conservative Fine-Tuning of Diffusion Models | AI-driven design problems, such as DNA/protein sequence design, are commonly tackled from two angles: generative modeling, which efficiently captures the feasible design space (e.g., natural images or biological sequences), and model-based optimization, which utilizes reward models for extrapolation. To combine the strengths of both approaches, we adopt a hybrid method that fine-tunes cutting-edge diffusion models by optimizing reward models through RL. Although prior work has explored similar avenues, they primarily focus on scenarios where accurate reward models are accessible. In contrast, we concentrate on an offline setting where a reward model is unknown, and we must learn from static offline datasets, a common scenario in scientific domains. In offline scenarios, existing approaches tend to suffer from overoptimization, as they may be misled by the reward model in out-of-distribution regions. To address this, we introduce a conservative fine-tuning approach, BRAID, by optimizing a conservative reward model, which includes additional penalization outside of offline data distributions. Through empirical and theoretical analysis, we demonstrate the capability of our approach to outperform the best designs in offline data, leveraging the extrapolation capabilities of reward models while avoiding the generation of invalid designs through pre-trained diffusion models. | [
"['Masatoshi Uehara' 'Yulai Zhao' 'Ehsan Hajiramezanali' 'Gabriele Scalia'\n 'Gökcen Eraslan' 'Avantika Lal' 'Sergey Levine' 'Tommaso Biancalani']"
] |
null | null | 2405.19679 | null | null | http://arxiv.org/pdf/2405.19679v1 | 2024-05-30T04:19:20Z | 2024-05-30T04:19:20Z | Efficient Trajectory Inference in Wasserstein Space Using Consecutive
Averaging | Capturing data from dynamic processes through cross-sectional measurements is seen in many fields such as computational biology. Trajectory inference deals with the challenge of reconstructing continuous processes from such observations. In this work, we propose methods for B-spline approximation and interpolation of point clouds through consecutive averaging that is instrinsic to the Wasserstein space. Combining subdivision schemes with optimal transport-based geodesic, our methods carry out trajectory inference at a chosen level of precision and smoothness, and can automatically handle scenarios where particles undergo division over time. We rigorously evaluate our method by providing convergence guarantees and testing it on simulated cell data characterized by bifurcations and merges, comparing its performance against state-of-the-art trajectory inference and interpolation methods. The results not only underscore the effectiveness of our method in inferring trajectories, but also highlight the benefit of performing interpolation and approximation that respect the inherent geometric properties of the data. | [
"['Amartya Banerjee' 'Harlin Lee' 'Nir Sharon' 'Caroline Moosmüller']"
] |
null | null | 2405.19681 | null | null | http://arxiv.org/pdf/2405.19681v1 | 2024-05-30T04:27:36Z | 2024-05-30T04:27:36Z | Bayesian Online Natural Gradient (BONG) | We propose a novel approach to sequential Bayesian inference based on variational Bayes. The key insight is that, in the online setting, we do not need to add the KL term to regularize to the prior (which comes from the posterior at the previous timestep); instead we can optimize just the expected log-likelihood, performing a single step of natural gradient descent starting at the prior predictive. We prove this method recovers exact Bayesian inference if the model is conjugate, and empirically outperforms other online VB methods in the non-conjugate setting, such as online learning for neural networks, especially when controlling for computational costs. | [
"['Matt Jones' 'Peter Chang' 'Kevin Murphy']"
] |
null | null | 2405.19683 | null | null | http://arxiv.org/pdf/2405.19683v1 | 2024-05-30T04:40:13Z | 2024-05-30T04:40:13Z | Breaking Indistinguishability with Transfer Learning: A First Look at
SPECK32/64 Lightweight Block Ciphers | In this research, we introduce MIND-Crypt, a novel attack framework that uses deep learning (DL) and transfer learning (TL) to challenge the indistinguishability of block ciphers, specifically SPECK32/64 encryption algorithm in CBC mode (Cipher Block Chaining) against Known Plaintext Attacks (KPA). Our methodology includes training a DL model with ciphertexts of two messages encrypted using the same key. The selected messages have the same byte-length and differ by only one bit at the binary level. This DL model employs a residual network architecture. For the TL, we use the trained DL model as a feature extractor, and these features are then used to train a shallow machine learning, such as XGBoost. This dual strategy aims to distinguish ciphertexts of two encrypted messages, addressing traditional cryptanalysis challenges. Our findings demonstrate that the DL model achieves an accuracy of approximately 99% under consistent cryptographic conditions (Same Key or Rounds) with the SPECK32/64 cipher. However, performance degrades to random guessing levels (50%) when tested with ciphertext generated from different keys or different encryption rounds of SPECK32/64. To enhance the results, the DL model requires retraining with different keys or encryption rounds using larger datasets (10^7 samples). To overcome this limitation, we implement TL, achieving an accuracy of about 53% with just 10,000 samples, which is better than random guessing. Further training with 580,000 samples increases accuracy to nearly 99%, showing a substantial reduction in data requirements by over 94%. This shows that an attacker can utilize machine learning models to break indistinguishability by accessing pairs of plaintexts and their corresponding ciphertexts encrypted with the same key, without directly interacting with the communicating parties. | [
"['Jimmy Dani' 'Kalyan Nakka' 'Nitesh Saxena']"
] |
null | null | 2405.19690 | null | null | http://arxiv.org/pdf/2405.19690v2 | 2024-05-31T21:23:55Z | 2024-05-30T05:04:33Z | Diffusion Policies creating a Trust Region for Offline Reinforcement
Learning | Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressive policy class, significantly boosts the performance of offline RL. However, its reliance on iterative denoising sampling to generate actions slows down both training and inference. While several recent attempts have tried to accelerate diffusion-QL, the improvement in training and/or inference speed often results in degraded performance. In this paper, we introduce a dual policy approach, Diffusion Trusted Q-Learning (DTQL), which comprises a diffusion policy for pure behavior cloning and a practical one-step policy. We bridge the two polices by a newly introduced diffusion trust region loss. The diffusion policy maintains expressiveness, while the trust region loss directs the one-step policy to explore freely and seek modes within the region defined by the diffusion policy. DTQL eliminates the need for iterative denoising sampling during both training and inference, making it remarkably computationally efficient. We evaluate its effectiveness and algorithmic characteristics against popular Kullback-Leibler (KL) based distillation methods in 2D bandit scenarios and gym tasks. We then show that DTQL could not only outperform other methods on the majority of the D4RL benchmark tasks but also demonstrate efficiency in training and inference speeds. The PyTorch implementation is available at https://github.com/TianyuCodings/Diffusion_Trusted_Q_Learning. | [
"['Tianyu Chen' 'Zhendong Wang' 'Mingyuan Zhou']"
] |
null | null | 2405.19697 | null | null | http://arxiv.org/pdf/2405.19697v1 | 2024-05-30T05:24:20Z | 2024-05-30T05:24:20Z | Bilevel reinforcement learning via the development of hyper-gradient
without lower-level convexity | Bilevel reinforcement learning (RL), which features intertwined two-level problems, has attracted growing interest recently. The inherent non-convexity of the lower-level RL problem is, however, to be an impediment to developing bilevel optimization methods. By employing the fixed point equation associated with the regularized RL, we characterize the hyper-gradient via fully first-order information, thus circumventing the assumption of lower-level convexity. This, remarkably, distinguishes our development of hyper-gradient from the general AID-based bilevel frameworks since we take advantage of the specific structure of RL problems. Moreover, we propose both model-based and model-free bilevel reinforcement learning algorithms, facilitated by access to the fully first-order hyper-gradient. Both algorithms are provable to enjoy the convergence rate $mathcal{O}(epsilon^{-1})$. To the best of our knowledge, this is the first time that AID-based bilevel RL gets rid of additional assumptions on the lower-level problem. In addition, numerical experiments demonstrate that the hyper-gradient indeed serves as an integration of exploitation and exploration. | [
"['Yan Yang' 'Bin Gao' 'Ya-xiang Yuan']"
] |
null | null | 2405.19703 | null | null | http://arxiv.org/pdf/2405.19703v2 | 2024-06-02T10:24:49Z | 2024-05-30T05:27:46Z | Towards a Better Evaluation of Out-of-Domain Generalization | The objective of Domain Generalization (DG) is to devise algorithms and models capable of achieving high performance on previously unseen test distributions. In the pursuit of this objective, average measure has been employed as the prevalent measure for evaluating models and comparing algorithms in the existing DG studies. Despite its significance, a comprehensive exploration of the average measure has been lacking and its suitability in approximating the true domain generalization performance has been questionable. In this study, we carefully investigate the limitations inherent in the average measure and propose worst+gap measure as a robust alternative. We establish theoretical grounds of the proposed measure by deriving two theorems starting from two different assumptions. We conduct extensive experimental investigations to compare the proposed worst+gap measure with the conventional average measure. Given the indispensable need to access the true DG performance for studying measures, we modify five existing datasets to come up with SR-CMNIST, C-Cats&Dogs, L-CIFAR10, PACS-corrupted, and VLCS-corrupted datasets. The experiment results unveil an inferior performance of the average measure in approximating the true DG performance and confirm the robustness of the theoretically supported worst+gap measure. | [
"['Duhun Hwang' 'Suhyun Kang' 'Moonjung Eo' 'Jimyeong Kim' 'Wonjong Rhee']"
] |
null | null | 2405.19704 | null | null | http://arxiv.org/pdf/2405.19704v1 | 2024-05-30T05:29:12Z | 2024-05-30T05:29:12Z | Enhancing Sufficient Dimension Reduction via Hellinger Correlation | In this work, we develop a new theory and method for sufficient dimension reduction (SDR) in single-index models, where SDR is a sub-field of supervised dimension reduction based on conditional independence. Our work is primarily motivated by the recent introduction of the Hellinger correlation as a dependency measure. Utilizing this measure, we develop a method capable of effectively detecting the dimension reduction subspace, complete with theoretical justification. Through extensive numerical experiments, we demonstrate that our proposed method significantly enhances and outperforms existing SDR methods. This improvement is largely attributed to our proposed method's deeper understanding of data dependencies and the refinement of existing SDR techniques. | [
"['Seungbeom Hong' 'Ilmun Kim' 'Jun Song']"
] |
null | null | 2405.19705 | null | null | http://arxiv.org/pdf/2405.19705v1 | 2024-05-30T05:29:40Z | 2024-05-30T05:29:40Z | Universal Online Convex Optimization with $1$ Projection per Round | To address the uncertainty in function types, recent progress in online convex optimization (OCO) has spurred the development of universal algorithms that simultaneously attain minimax rates for multiple types of convex functions. However, for a $T$-round online problem, state-of-the-art methods typically conduct $O(log T)$ projections onto the domain in each round, a process potentially time-consuming with complicated feasible sets. In this paper, inspired by the black-box reduction of Cutkosky and Orabona (2018), we employ a surrogate loss defined over simpler domains to develop universal OCO algorithms that only require $1$ projection. Embracing the framework of prediction with expert advice, we maintain a set of experts for each type of functions and aggregate their predictions via a meta-algorithm. The crux of our approach lies in a uniquely designed expert-loss for strongly convex functions, stemming from an innovative decomposition of the regret into the meta-regret and the expert-regret. Our analysis sheds new light on the surrogate loss, facilitating a rigorous examination of the discrepancy between the regret of the original loss and that of the surrogate loss, and carefully controlling meta-regret under the strong convexity condition. In this way, with only $1$ projection per round, we establish optimal regret bounds for general convex, exponentially concave, and strongly convex functions simultaneously. Furthermore, we enhance the expert-loss to exploit the smoothness property, and demonstrate that our algorithm can attain small-loss regret for multiple types of convex and smooth functions. | [
"['Wenhao Yang' 'Yibo Wang' 'Peng Zhao' 'Lijun Zhang']"
] |
null | null | 2405.19715 | null | null | http://arxiv.org/pdf/2405.19715v2 | 2024-06-21T01:01:42Z | 2024-05-30T05:49:38Z | SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths | Speculative decoding reduces the inference latency of a target large language model via utilizing a smaller and faster draft model. Its performance depends on a hyperparameter K -- the candidate length, i.e., the number of candidate tokens for the target model to verify in each round. However, previous methods often use simple heuristics to choose K, which may result in sub-optimal performance. We study the choice of the candidate length K and formulate it as a Markov Decision Process. We theoretically show that the optimal policy of this Markov decision process takes the form of a threshold policy, i.e., the current speculation should stop and be verified when the probability of getting a rejection exceeds a threshold value. Motivated by this theory, we propose SpecDec++, an enhanced version of speculative decoding that adaptively determines the candidate length on the fly. We augment the draft model with a trained acceptance prediction head to predict the conditional acceptance probability of the candidate tokens. SpecDec++ will stop the current speculation when the predicted probability that at least one token gets rejected exceeds a threshold. We implement SpecDec++ and apply it to the llama-2-chat 7B & 70B model pair. Our adaptive method achieves a 2.04x speedup on the Alpaca dataset (an additional 7.2% improvement over the baseline speculative decoding). On the GSM8K and HumanEval datasets, our method achieves a 2.26x speedup (9.4% improvement) and 2.23x speedup (11.1% improvement), respectively. | [
"['Kaixuan Huang' 'Xudong Guo' 'Mengdi Wang']"
] |
null | null | 2405.19729 | null | null | http://arxiv.org/pdf/2405.19729v1 | 2024-05-30T06:21:11Z | 2024-05-30T06:21:11Z | Dynamic feature selection in medical predictive monitoring by
reinforcement learning | In this paper, we investigate dynamic feature selection within multivariate time-series scenario, a common occurrence in clinical prediction monitoring where each feature corresponds to a bio-test result. Many existing feature selection methods fall short in effectively leveraging time-series information, primarily because they are designed for static data. Our approach addresses this limitation by enabling the selection of time-varying feature subsets for each patient. Specifically, we employ reinforcement learning to optimize a policy under maximum cost restrictions. The prediction model is subsequently updated using synthetic data generated by trained policy. Our method can seamlessly integrate with non-differentiable prediction models. We conducted experiments on a sizable clinical dataset encompassing regression and classification tasks. The results demonstrate that our approach outperforms strong feature selection baselines, particularly when subjected to stringent cost limitations. Code will be released once paper is accepted. | [
"['Yutong Chen' 'Jiandong Gao' 'Ji Wu']"
] |
null | null | 2405.19730 | null | null | http://arxiv.org/pdf/2405.19730v2 | 2024-06-29T04:31:52Z | 2024-05-30T06:21:34Z | Research on Foundation Model for Spatial Data Intelligence: China's 2024
White Paper on Strategic Development of Spatial Data Intelligence | This report focuses on spatial data intelligent large models, delving into the principles, methods, and cutting-edge applications of these models. It provides an in-depth discussion on the definition, development history, current status, and trends of spatial data intelligent large models, as well as the challenges they face. The report systematically elucidates the key technologies of spatial data intelligent large models and their applications in urban environments, aerospace remote sensing, geography, transportation, and other scenarios. Additionally, it summarizes the latest application cases of spatial data intelligent large models in themes such as urban development, multimodal systems, remote sensing, smart transportation, and resource environments. Finally, the report concludes with an overview and outlook on the development prospects of spatial data intelligent large models. | [
"['Shaohua Wang' 'Xing Xie' 'Yong Li' 'Danhuai Guo' 'Zhi Cai' 'Yu Liu'\n 'Yang Yue' 'Xiao Pan' 'Feng Lu' 'Huayi Wu' 'Zhipeng Gui' 'Zhiming Ding'\n 'Bolong Zheng' 'Fuzheng Zhang' 'Tao Qin' 'Jingyuan Wang' 'Chuang Tao'\n 'Zhengchao Chen' 'Hao Lu' 'Jiayi Li' 'Hongyang Chen' 'Peng Yue'\n 'Wenhao Yu' 'Yao Yao' 'Leilei Sun' 'Yong Zhang' 'Longbiao Chen'\n 'Xiaoping Du' 'Xiang Li' 'Xueying Zhang' 'Kun Qin' 'Zhaoya Gong'\n 'Weihua Dong' 'Xiaofeng Meng']"
] |
null | null | 2405.19732 | null | null | http://arxiv.org/pdf/2405.19732v3 | 2024-06-06T04:59:27Z | 2024-05-30T06:24:14Z | Two Optimizers Are Better Than One: LLM Catalyst Empowers Gradient-Based
Optimization for Prompt Tuning | Learning a skill generally relies on both practical experience by doer and insightful high-level guidance by instructor. Will this strategy also work well for solving complex non-convex optimization problems? Here, a common gradient-based optimizer acts like a disciplined doer, making locally optimal update at each step. Recent methods utilize large language models (LLMs) to optimize solutions for concrete problems by inferring from natural language instructions, akin to a high-level instructor. In this paper, we show that these two optimizers are complementary to each other, suggesting a collaborative optimization approach. The gradient-based optimizer and LLM-based optimizer are combined in an interleaved manner. We instruct LLMs using task descriptions and timely optimization trajectories recorded during gradient-based optimization. Inferred results from LLMs are used as restarting points for the next stage of gradient optimization. By leveraging both the locally rigorous gradient-based optimizer and the high-level deductive LLM-based optimizer, our combined optimization method consistently yields improvements over competitive baseline prompt tuning methods. Our results demonstrate the synergistic effect of conventional gradient-based optimization and the inference ability of LLMs. The code is released at https://github.com/guozix/LLM-catalyst. | [
"['Zixian Guo' 'Ming Liu' 'Zhilong Ji' 'Jinfeng Bai' 'Yiwen Guo'\n 'Wangmeng Zuo']"
] |
null | null | 2405.19747 | null | null | http://arxiv.org/pdf/2405.19747v1 | 2024-05-30T06:50:28Z | 2024-05-30T06:50:28Z | Understanding and mitigating difficulties in posterior predictive
evaluation | Predictive posterior densities (PPDs) are of interest in approximate Bayesian inference. Typically, these are estimated by simple Monte Carlo (MC) averages using samples from the approximate posterior. We observe that the signal-to-noise ratio (SNR) of such estimators can be extremely low. An analysis for exact inference reveals SNR decays exponentially as there is an increase in (a) the mismatch between training and test data, (b) the dimensionality of the latent space, or (c) the size of the test data relative to the training data. Further analysis extends these results to approximate inference. To remedy the low SNR problem, we propose replacing simple MC sampling with importance sampling using a proposal distribution optimized at test time on a variational proxy for the SNR and demonstrate that this yields greatly improved estimates. | [
"['Abhinav Agrawal' 'Justin Domke']"
] |
null | null | 2405.19752 | null | null | http://arxiv.org/pdf/2405.19752v2 | 2024-07-06T13:43:21Z | 2024-05-30T06:56:48Z | Understanding Memory-Regret Trade-Off for Streaming Stochastic
Multi-Armed Bandits | We study the stochastic multi-armed bandit problem in the $P$-pass streaming model. In this problem, the $n$ arms are present in a stream and at most $m<n$ arms and their statistics can be stored in the memory. We give a complete characterization of the optimal regret in terms of $m, n$ and $P$. Specifically, we design an algorithm with $tilde Oleft((n-m)^{1+frac{2^{P}-2}{2^{P+1}-1}} n^{frac{2-2^{P+1}}{2^{P+1}-1}} T^{frac{2^P}{2^{P+1}-1}}right)$ regret and complement it with an $tilde Omegaleft((n-m)^{1+frac{2^{P}-2}{2^{P+1}-1}} n^{frac{2-2^{P+1}}{2^{P+1}-1}} T^{frac{2^P}{2^{P+1}-1}}right)$ lower bound when the number of rounds $T$ is sufficiently large. Our results are tight up to a logarithmic factor in $n$ and $P$. | [
"['Yuchen He' 'Zichun Ye' 'Chihao Zhang']"
] |
null | null | 2405.19757 | null | null | http://arxiv.org/pdf/2405.19757v1 | 2024-05-30T07:06:02Z | 2024-05-30T07:06:02Z | Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise
Filtering | Recent advances in a generative neural network model extend the development of data augmentation methods. However, the augmentation methods based on the modern generative models fail to achieve notable performance for class imbalance data compared to the conventional model, the SMOTE. We investigate the problem of the generative model for imbalanced classification and introduce a framework to enhance the SMOTE algorithm using Variational Autoencoders (VAE). Our approach systematically quantifies the density of data points in a low-dimensional latent space using the VAE, simultaneously incorporating information on class labels and classification difficulty. Then, the data points potentially degrading the augmentation are systematically excluded, and the neighboring observations are directly augmented on the data space. Empirical studies on several imbalanced datasets represent that this simple process innovatively improves the conventional SMOTE algorithm over the deep learning models. Consequently, we conclude that the selection of minority data and the interpolation in the data space are beneficial for imbalanced classification problems with a relatively small number of data points. | [
"['Sungchul Hong' 'Seunghwan An' 'Jong-June Jeon']"
] |
null | null | 2405.19760 | null | null | http://arxiv.org/pdf/2405.19760v1 | 2024-05-30T07:11:20Z | 2024-05-30T07:11:20Z | Identifiability of a statistical model with two latent vectors:
Importance of the dimensionality relation and application to graph embedding | Identifiability of statistical models is a key notion in unsupervised representation learning. Recent work of nonlinear independent component analysis (ICA) employs auxiliary data and has established identifiable conditions. This paper proposes a statistical model of two latent vectors with single auxiliary data generalizing nonlinear ICA, and establishes various identifiability conditions. Unlike previous work, the two latent vectors in the proposed model can have arbitrary dimensions, and this property enables us to reveal an insightful dimensionality relation among two latent vectors and auxiliary data in identifiability conditions. Furthermore, surprisingly, we prove that the indeterminacies of the proposed model has the same as emph{linear} ICA under certain conditions: The elements in the latent vector can be recovered up to their permutation and scales. Next, we apply the identifiability theory to a statistical model for graph data. As a result, one of the identifiability conditions includes an appealing implication: Identifiability of the statistical model could depend on the maximum value of link weights in graph data. Then, we propose a practical method for identifiable graph embedding. Finally, we numerically demonstrate that the proposed method well-recovers the latent vectors and model identifiability clearly depends on the maximum value of link weights, which supports the implication of our theoretical results | [
"['Hiroaki Sasaki']"
] |
null | null | 2405.19779 | null | null | http://arxiv.org/pdf/2405.19779v1 | 2024-05-30T07:44:31Z | 2024-05-30T07:44:31Z | Automatic Graph Topology-Aware Transformer | Existing efforts are dedicated to designing many topologies and graph-aware strategies for the graph Transformer, which greatly improve the model's representation capabilities. However, manually determining the suitable Transformer architecture for a specific graph dataset or task requires extensive expert knowledge and laborious trials. This paper proposes an evolutionary graph Transformer architecture search framework (EGTAS) to automate the construction of strong graph Transformers. We build a comprehensive graph Transformer search space with the micro-level and macro-level designs. EGTAS evolves graph Transformer topologies at the macro level and graph-aware strategies at the micro level. Furthermore, a surrogate model based on generic architectural coding is proposed to directly predict the performance of graph Transformers, substantially reducing the evaluation cost of evolutionary search. We demonstrate the efficacy of EGTAS across a range of graph-level and node-level tasks, encompassing both small-scale and large-scale graph datasets. Experimental results and ablation studies show that EGTAS can construct high-performance architectures that rival state-of-the-art manual and automated baselines. | [
"['Chao Wang' 'Jiaxuan Zhao' 'Lingling Li' 'Licheng Jiao' 'Fang Liu'\n 'Shuyuan Yang']"
] |
null | null | 2405.19783 | null | null | http://arxiv.org/pdf/2405.19783v1 | 2024-05-30T07:48:32Z | 2024-05-30T07:48:32Z | Instruction-Guided Visual Masking | Instruction following is crucial in contemporary LLM. However, when extended to multimodal setting, it often suffers from misalignment between specific textual instruction and targeted local region of an image. To achieve more accurate and nuanced multimodal instruction following, we introduce Instruction-guided Visual Masking (IVM), a new versatile visual grounding model that is compatible with diverse multimodal models, such as LMM and robot model. By constructing visual masks for instruction-irrelevant regions, IVM-enhanced multimodal models can effectively focus on task-relevant image regions to better align with complex instructions. Specifically, we design a visual masking data generation pipeline and create an IVM-Mix-1M dataset with 1 million image-instruction pairs. We further introduce a new learning technique, Discriminator Weighted Supervised Learning (DWSL) for preferential IVM training that prioritizes high-quality data samples. Experimental results on generic multimodal tasks such as VQA and embodied robotic control demonstrate the versatility of IVM, which as a plug-and-play tool, significantly boosts the performance of diverse multimodal models, yielding new state-of-the-art results across challenging multimodal benchmarks. Code is available at https://github.com/2toinf/IVM. | [
"['Jinliang Zheng' 'Jianxiong Li' 'Sijie Cheng' 'Yinan Zheng' 'Jiaming Li'\n 'Jihao Liu' 'Yu Liu' 'Jingjing Liu' 'Xianyuan Zhan']"
] |
null | null | 2405.19784 | null | null | http://arxiv.org/pdf/2405.19784v1 | 2024-05-30T07:48:43Z | 2024-05-30T07:48:43Z | PixelsDB: Serverless and Natural-Language-Aided Data Analytics with
Flexible Service Levels and Prices | Serverless query processing has become increasingly popular due to its advantages, including automated hardware and software management, high elasticity, and pay-as-you-go pricing. For users who are not system experts, serverless query processing greatly reduces the cost of owning a data analytic system. However, it is still a significant challenge for non-expert users to transform their complex and evolving data analytic needs into proper SQL queries and select a serverless query engine that delivers satisfactory performance and price for each type of query. This paper presents PixelsDB, an open-source data analytic system that allows users who lack system or SQL expertise to explore data efficiently. It allows users to generate and debug SQL queries using a natural language interface powered by fine-tuned language models. The queries are then executed by a serverless query engine that offers varying prices for different service levels on query urgency. The service levels are natively supported by dedicated architecture design and heterogeneous resource scheduling that can apply cost-efficient resources to process non-urgent queries. We envision that the combination of a serverless paradigm, a natural-language-aided interface, and flexible service levels and prices will substantially improve the user experience in data analysis. | [
"['Haoqiong Bian' 'Dongyang Geng' 'Haoyang Li' 'Anastasia Ailamaki']"
] |
null | null | 2405.19785 | null | null | http://arxiv.org/pdf/2405.19785v1 | 2024-05-30T07:49:02Z | 2024-05-30T07:49:02Z | Recurrent Deep Kernel Learning of Dynamical Systems | Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets. However, constructing ROMs from noisy high-dimensional data is challenging. In this work, we propose a data-driven, non-intrusive method that utilizes stochastic variational deep kernel learning (SVDKL) to discover low-dimensional latent spaces from data and a recurrent version of SVDKL for representing and predicting the evolution of latent dynamics. The proposed method is demonstrated with two challenging examples -- a double pendulum and a reaction-diffusion system. Results show that our framework is capable of (i) denoising and reconstructing measurements, (ii) learning compact representations of system states, (iii) predicting system evolution in low-dimensional latent spaces, and (iv) quantifying modeling uncertainties. | [
"['Nicolò Botteghi' 'Paolo Motta' 'Andrea Manzoni' 'Paolo Zunino'\n 'Mengwu Guo']"
] |
null | null | 2405.19787 | null | null | http://arxiv.org/pdf/2405.19787v2 | 2024-05-31T01:23:41Z | 2024-05-30T07:54:07Z | From Symbolic Tasks to Code Generation: Diversification Yields Better
Task Performers | Instruction tuning -- tuning large language models on instruction-output pairs -- is a promising technique for making models better adapted to the real world. Yet, the key factors driving the model's capability to understand and follow instructions not seen during training remain under-explored. Our investigation begins with a series of synthetic experiments within the theoretical framework of a Turing-complete algorithm called Markov algorithm, which allows fine-grained control over the instruction-tuning data. Generalization and robustness with respect to the training distribution emerge once a diverse enough set of tasks is provided, even though very few examples are provided for each task. We extend these initial results to a real-world application scenario of code generation and find that a more diverse instruction set, extending beyond code-related tasks, improves the performance of code generation. Our observations suggest that a more diverse semantic space for instruction-tuning sets greatly improves the model's ability to follow instructions and perform tasks. | [
"['Dylan Zhang' 'Justin Wang' 'Francois Charton']"
] |
null | null | 2405.19789 | null | null | http://arxiv.org/pdf/2405.19789v1 | 2024-05-30T07:58:01Z | 2024-05-30T07:58:01Z | Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias
in Federated Semi-Supervised Learning | Federated Semi-Supervised Learning (FSSL) leverages both labeled and unlabeled data on clients to collaboratively train a model.In FSSL, the heterogeneous data can introduce prediction bias into the model, causing the model's prediction to skew towards some certain classes. Existing FSSL methods primarily tackle this issue by enhancing consistency in model parameters or outputs. However, as the models themselves are biased, merely constraining their consistency is not sufficient to alleviate prediction bias. In this paper, we explore this bias from a Bayesian perspective and demonstrate that it principally originates from label prior bias within the training data. Building upon this insight, we propose a debiasing method for FSSL named FedDB. FedDB utilizes the Average Prediction Probability of Unlabeled Data (APP-U) to approximate the biased prior.During local training, FedDB employs APP-U to refine pseudo-labeling through Bayes' theorem, thereby significantly reducing the label prior bias. Concurrently, during the model aggregation, FedDB uses APP-U from participating clients to formulate unbiased aggregate weights, thereby effectively diminishing bias in the global model. Experimental results show that FedDB can surpass existing FSSL methods. The code is available at https://github.com/GuogangZhu/FedDB. | [
"['Guogang Zhu' 'Xuefeng Liu' 'Xinghao Wu' 'Shaojie Tang' 'Chao Tang'\n 'Jianwei Niu' 'Hao Su']"
] |
null | null | 2405.19804 | null | null | http://arxiv.org/pdf/2405.19804v1 | 2024-05-30T08:12:51Z | 2024-05-30T08:12:51Z | Exploring Key Factors for Long-Term Vessel Incident Risk Prediction | Factor analysis acts a pivotal role in enhancing maritime safety. Most previous studies conduct factor analysis within the framework of incident-related label prediction, where the developed models can be categorized into short-term and long-term prediction models. The long-term models offer a more strategic approach, enabling more proactive risk management, compared to the short-term ones. Nevertheless, few studies have devoted to rigorously identifying the key factors for the long-term prediction and undertaking comprehensive factor analysis. Hence, this study aims to delve into the key factors for predicting the incident risk levels in the subsequent year given a specific datestamp. The majority of candidate factors potentially contributing to the incident risk are collected from vessels' historical safety performance data spanning up to five years. An improved embedded feature selection, which integrates Random Forest classifier with a feature filtering process is proposed to identify key risk-contributing factors from the candidate pool. The results demonstrate superior performance of the proposed method in incident prediction and factor interpretability. Comprehensive analysis is conducted upon the key factors, which could help maritime stakeholders formulate management strategies for incident prevenion. | [
"['Tianyi Chen' 'Hua Wang' 'Yutong Cai' 'Maohan Liang' 'Qiang Meng']"
] |
null | null | 2405.19805 | null | null | http://arxiv.org/pdf/2405.19805v1 | 2024-05-30T08:14:34Z | 2024-05-30T08:14:34Z | Complexity of Deciding Injectivity and Surjectivity of ReLU Neural
Networks | Neural networks with ReLU activation play a key role in modern machine learning. In view of safety-critical applications, the verification of trained networks is of great importance and necessitates a thorough understanding of essential properties of the function computed by a ReLU network, including characteristics like injectivity and surjectivity. Recently, Puthawala et al. [JMLR 2022] came up with a characterization for injectivity of a ReLU layer, which implies an exponential time algorithm. However, the exact computational complexity of deciding injectivity remained open. We answer this question by proving coNP-completeness of deciding injectivity of a ReLU layer. On the positive side, as our main result, we present a parameterized algorithm which yields fixed-parameter tractability of the problem with respect to the input dimension. In addition, we also characterize surjectivity for two-layer ReLU networks with one-dimensional output. Remarkably, the decision problem turns out to be the complement of a basic network verification task. We prove NP-hardness for surjectivity, implying a stronger hardness result than previously known for the network verification problem. Finally, we reveal interesting connections to computational convexity by formulating the surjectivity problem as a zonotope containment problem | [
"['Vincent Froese' 'Moritz Grillo' 'Martin Skutella']"
] |
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