categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2402.12656
| null | null |
http://arxiv.org/pdf/2402.12656v3
|
2024-05-21T12:41:52Z
|
2024-02-20T02:09:55Z
|
HyperMoE: Towards Better Mixture of Experts via Transferring Among
Experts
|
The Mixture of Experts (MoE) for language models has been proven effective in augmenting the capacity of models by dynamically routing each input token to a specific subset of experts for processing. Despite the success, most existing methods face a challenge for balance between sparsity and the availability of expert knowledge: enhancing performance through increased use of expert knowledge often results in diminishing sparsity during expert selection. To mitigate this contradiction, we propose HyperMoE, a novel MoE framework built upon Hypernetworks. This framework integrates the computational processes of MoE with the concept of knowledge transferring in multi-task learning. Specific modules generated based on the information of unselected experts serve as supplementary information, which allows the knowledge of experts not selected to be used while maintaining selection sparsity. Our comprehensive empirical evaluations across multiple datasets and backbones establish that HyperMoE significantly outperforms existing MoE methods under identical conditions concerning the number of experts.
|
[
"['Hao Zhao' 'Zihan Qiu' 'Huijia Wu' 'Zili Wang' 'Zhaofeng He' 'Jie Fu']"
] |
null | null |
2402.12663
| null | null |
http://arxiv.org/pdf/2402.12663v1
|
2024-02-20T02:23:15Z
|
2024-02-20T02:23:15Z
|
SoftQE: Learned Representations of Queries Expanded by LLMs
|
We investigate the integration of Large Language Models (LLMs) into query encoders to improve dense retrieval without increasing latency and cost, by circumventing the dependency on LLMs at inference time. SoftQE incorporates knowledge from LLMs by mapping embeddings of input queries to those of the LLM-expanded queries. While improvements over various strong baselines on in-domain MS-MARCO metrics are marginal, SoftQE improves performance by 2.83 absolute percentage points on average on five out-of-domain BEIR tasks.
|
[
"['Varad Pimpalkhute' 'John Heyer' 'Xusen Yin' 'Sameer Gupta']"
] |
null | null |
2402.12664
| null | null |
http://arxiv.org/pdf/2402.12664v1
|
2024-02-20T02:26:48Z
|
2024-02-20T02:26:48Z
|
Discriminant Distance-Aware Representation on Deterministic Uncertainty
Quantification Methods
|
Uncertainty estimation is a crucial aspect of deploying dependable deep learning models in safety-critical systems. In this study, we introduce a novel and efficient method for deterministic uncertainty estimation called Discriminant Distance-Awareness Representation (DDAR). Our approach involves constructing a DNN model that incorporates a set of prototypes in its latent representations, enabling us to analyze valuable feature information from the input data. By leveraging a distinction maximization layer over optimal trainable prototypes, DDAR can learn a discriminant distance-awareness representation. We demonstrate that DDAR overcomes feature collapse by relaxing the Lipschitz constraint that hinders the practicality of deterministic uncertainty methods (DUMs) architectures. Our experiments show that DDAR is a flexible and architecture-agnostic method that can be easily integrated as a pluggable layer with distance-sensitive metrics, outperforming state-of-the-art uncertainty estimation methods on multiple benchmark problems.
|
[
"['Jiaxin Zhang' 'Kamalika Das' 'Sricharan Kumar']"
] |
null | null |
2402.12668
| null | null |
http://arxiv.org/pdf/2402.12668v1
|
2024-02-20T02:36:26Z
|
2024-02-20T02:36:26Z
|
Randomization Can Reduce Both Bias and Variance: A Case Study in Random
Forests
|
We study the often overlooked phenomenon, first noted in cite{breiman2001random}, that random forests appear to reduce bias compared to bagging. Motivated by an interesting paper by cite{mentch2020randomization}, where the authors argue that random forests reduce effective degrees of freedom and only outperform bagging ensembles in low signal-to-noise ratio (SNR) settings, we explore how random forests can uncover patterns in the data missed by bagging. We empirically demonstrate that in the presence of such patterns, random forests reduce bias along with variance and increasingly outperform bagging ensembles when SNR is high. Our observations offer insights into the real-world success of random forests across a range of SNRs and enhance our understanding of the difference between random forests and bagging ensembles with respect to the randomization injected into each split. Our investigations also yield practical insights into the importance of tuning $mtry$ in random forests.
|
[
"['Brian Liu' 'Rahul Mazumder']"
] |
null | null |
2402.12673
| null | null |
http://arxiv.org/pdf/2402.12673v1
|
2024-02-20T02:45:20Z
|
2024-02-20T02:45:20Z
|
Beyond Worst-case Attacks: Robust RL with Adaptive Defense via
Non-dominated Policies
|
In light of the burgeoning success of reinforcement learning (RL) in diverse real-world applications, considerable focus has been directed towards ensuring RL policies are robust to adversarial attacks during test time. Current approaches largely revolve around solving a minimax problem to prepare for potential worst-case scenarios. While effective against strong attacks, these methods often compromise performance in the absence of attacks or the presence of only weak attacks. To address this, we study policy robustness under the well-accepted state-adversarial attack model, extending our focus beyond only worst-case attacks. We first formalize this task at test time as a regret minimization problem and establish its intrinsic hardness in achieving sublinear regret when the baseline policy is from a general continuous policy class, $Pi$. This finding prompts us to textit{refine} the baseline policy class $Pi$ prior to test time, aiming for efficient adaptation within a finite policy class $Tilde{Pi}$, which can resort to an adversarial bandit subroutine. In light of the importance of a small, finite $Tilde{Pi}$, we propose a novel training-time algorithm to iteratively discover textit{non-dominated policies}, forming a near-optimal and minimal $Tilde{Pi}$, thereby ensuring both robustness and test-time efficiency. Empirical validation on the Mujoco corroborates the superiority of our approach in terms of natural and robust performance, as well as adaptability to various attack scenarios.
|
[
"['Xiangyu Liu' 'Chenghao Deng' 'Yanchao Sun' 'Yongyuan Liang'\n 'Furong Huang']"
] |
null | null |
2402.12683
| null | null |
http://arxiv.org/pdf/2402.12683v1
|
2024-02-20T03:14:47Z
|
2024-02-20T03:14:47Z
|
TorchCP: A Library for Conformal Prediction based on PyTorch
|
TorchCP is a Python toolbox for conformal prediction research on deep learning models. It contains various implementations for posthoc and training methods for classification and regression tasks (including multi-dimension output). TorchCP is built on PyTorch (Paszke et al., 2019) and leverages the advantages of matrix computation to provide concise and efficient inference implementations. The code is licensed under the LGPL license and is open-sourced at $href{https://github.com/ml-stat-Sustech/TorchCP}{text{this https URL}}$.
|
[
"['Hongxin Wei' 'Jianguo Huang']"
] |
null | null |
2402.12687
| null | null |
http://arxiv.org/pdf/2402.12687v1
|
2024-02-20T03:27:53Z
|
2024-02-20T03:27:53Z
|
Learning on manifolds without manifold learning
|
Function approximation based on data drawn randomly from an unknown distribution is an important problem in machine learning. In contrast to the prevalent paradigm of solving this problem by minimizing a loss functional, we have given a direct one-shot construction together with optimal error bounds under the manifold assumption; i.e., one assumes that the data is sampled from an unknown sub-manifold of a high dimensional Euclidean space. A great deal of research deals with obtaining information about this manifold, such as the eigendecomposition of the Laplace-Beltrami operator or coordinate charts, and using this information for function approximation. This two step approach implies some extra errors in the approximation stemming from basic quantities of the data in addition to the errors inherent in function approximation. In Neural Networks, 132:253268, 2020, we have proposed a one-shot direct method to achieve function approximation without requiring the extraction of any information about the manifold other than its dimension. However, one cannot pin down the class of approximants used in that paper. In this paper, we view the unknown manifold as a sub-manifold of an ambient hypersphere and study the question of constructing a one-shot approximation using the spherical polynomials based on the hypersphere. Our approach does not require preprocessing of the data to obtain information about the manifold other than its dimension. We give optimal rates of approximation for relatively "rough" functions.
|
[
"['H. N. Mhaskar' \"Ryan O'Dowd\"]"
] |
null | null |
2402.12694
| null | null |
http://arxiv.org/pdf/2402.12694v5
|
2024-07-05T07:04:25Z
|
2024-02-20T03:45:59Z
|
Revitalizing Multivariate Time Series Forecasting: Learnable
Decomposition with Inter-Series Dependencies and Intra-Series Variations
Modeling
|
Predicting multivariate time series is crucial, demanding precise modeling of intricate patterns, including inter-series dependencies and intra-series variations. Distinctive trend characteristics in each time series pose challenges, and existing methods, relying on basic moving average kernels, may struggle with the non-linear structure and complex trends in real-world data. Given that, we introduce a learnable decomposition strategy to capture dynamic trend information more reasonably. Additionally, we propose a dual attention module tailored to capture inter-series dependencies and intra-series variations simultaneously for better time series forecasting, which is implemented by channel-wise self-attention and autoregressive self-attention. To evaluate the effectiveness of our method, we conducted experiments across eight open-source datasets and compared it with the state-of-the-art methods. Through the comparison results, our Leddam (LEarnable Decomposition and Dual Attention Module) not only demonstrates significant advancements in predictive performance, but also the proposed decomposition strategy can be plugged into other methods with a large performance-boosting, from 11.87% to 48.56% MSE error degradation.
|
[
"['Guoqi Yu' 'Jing Zou' 'Xiaowei Hu' 'Angelica I. Aviles-Rivero' 'Jing Qin'\n 'Shujun Wang']"
] |
null | null |
2402.12704
| null | null |
http://arxiv.org/pdf/2402.12704v1
|
2024-02-20T04:06:28Z
|
2024-02-20T04:06:28Z
|
Quantum Embedding with Transformer for High-dimensional Data
|
Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a challenging high-dimensional dataset. The study showcases and analyzes empirical evidence that our transformer-based architecture is a highly versatile and practical approach to modern quantum machine learning problems.
|
[
"['Hao-Yuan Chen' 'Yen-Jui Chang' 'Shih-Wei Liao' 'Ching-Ray Chang']"
] |
null | null |
2402.12710
| null | null |
http://arxiv.org/pdf/2402.12710v1
|
2024-02-20T04:13:59Z
|
2024-02-20T04:13:59Z
|
Integrating Active Learning in Causal Inference with Interference: A
Novel Approach in Online Experiments
|
In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one's outcomes, and (2) non-random treatment assignments influenced by confounders. To improve the efficiency of estimating potentially complex effects functions, we introduce an novel active learning approach: Active Learning in Causal Inference with Interference (ACI). This approach uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. The ACI framework sequentially identifies the experimental settings that demand further data. It further optimizes the treatment assignments under the network interference structure using genetic algorithms to achieve efficient learning outcome. By applying our method to simulation data and a Tencent game dataset, we demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements. This ACI approach marks a significant advancement in the realm of data efficiency for causal inference, offering a robust and efficient alternative to traditional methodologies, particularly in scenarios characterized by complex interference patterns.
|
[
"['Hongtao Zhu' 'Sizhe Zhang' 'Yang Su' 'Zhenyu Zhao' 'Nan Chen']"
] |
null | null |
2402.12711
| null | null |
http://arxiv.org/pdf/2402.12711v1
|
2024-02-20T04:21:13Z
|
2024-02-20T04:21:13Z
|
Achieving Near-Optimal Regret for Bandit Algorithms with Uniform
Last-Iterate Guarantee
|
Existing performance measures for bandit algorithms such as regret, PAC bounds, or uniform-PAC (Dann et al., 2017), typically evaluate the cumulative performance, while allowing the play of an arbitrarily bad arm at any finite time t. Such a behavior can be highly detrimental in high-stakes applications. This paper introduces a stronger performance measure, the uniform last-iterate (ULI) guarantee, capturing both cumulative and instantaneous performance of bandit algorithms. Specifically, ULI characterizes the instantaneous performance since it ensures that the per-round regret of the played arm is bounded by a function, monotonically decreasing w.r.t. (large) round t, preventing revisits to bad arms when sufficient samples are available. We demonstrate that a near-optimal ULI guarantee directly implies near-optimal cumulative performance across aforementioned performance measures. To examine the achievability of ULI in the finite arm setting, we first provide two positive results that some elimination-based algorithms and high-probability adversarial algorithms with stronger analysis or additional designs, can attain near-optimal ULI guarantees. Then, we also provide a negative result, indicating that optimistic algorithms cannot achieve a near-optimal ULI guarantee. Finally, we propose an efficient algorithm for linear bandits with infinitely many arms, which achieves the ULI guarantee, given access to an optimization oracle.
|
[
"['Junyan Liu' 'Yunfan Li' 'Lin Yang']"
] |
null | null |
2402.12714
| null | null |
http://arxiv.org/pdf/2402.12714v1
|
2024-02-20T04:40:00Z
|
2024-02-20T04:40:00Z
|
Equivariant Pretrained Transformer for Unified Geometric Learning on
Multi-Domain 3D Molecules
|
Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications. However, prior efforts typically focus on pretraining models on a specific domain, either proteins or small molecules, missing the opportunity to leverage the cross-domain knowledge. To mitigate this gap, we introduce Equivariant Pretrained Transformer (EPT), a novel pretraining framework designed to harmonize the geometric learning of small molecules and proteins. To be specific, EPT unifies the geometric modeling of multi-domain molecules via the block-enhanced representation that can attend a broader context of each atom. Upon transformer framework, EPT is further enhanced with E(3) equivariance to facilitate the accurate representation of 3D structures. Another key innovation of EPT is its block-level pretraining task, which allows for joint pretraining on datasets comprising both small molecules and proteins. Experimental evaluations on a diverse group of benchmarks, including ligand binding affinity prediction, molecular property prediction, and protein property prediction, show that EPT significantly outperforms previous SOTA methods for affinity prediction, and achieves the best or comparable performance with existing domain-specific pretraining models for other tasks.
|
[
"['Rui Jiao' 'Xiangzhe Kong' 'Ziyang Yu' 'Wenbing Huang' 'Yang Liu']"
] |
null | null |
2402.12715
| null | null |
http://arxiv.org/pdf/2402.12715v2
|
2024-05-16T20:55:38Z
|
2024-02-20T04:49:34Z
|
Spurious Correlations in Machine Learning: A Survey
|
Machine learning systems are known to be sensitive to spurious correlations between non-essential features of the inputs (e.g., background, texture, and secondary objects) and the corresponding labels. These features and their correlations with the labels are known as "spurious" because they tend to change with shifts in real-world data distributions, which can negatively impact the model's generalization and robustness. In this paper, we provide a review of this issue, along with a taxonomy of current state-of-the-art methods for addressing spurious correlations in machine learning models. Additionally, we summarize existing datasets, benchmarks, and metrics to aid future research. The paper concludes with a discussion of the recent advancements and future challenges in this field, aiming to provide valuable insights for researchers in the related domains.
|
[
"['Wenqian Ye' 'Guangtao Zheng' 'Xu Cao' 'Yunsheng Ma' 'Aidong Zhang']"
] |
null | null |
2402.12722
| null | null |
http://arxiv.org/pdf/2402.12722v1
|
2024-02-20T05:11:20Z
|
2024-02-20T05:11:20Z
|
Structural Knowledge Informed Continual Multivariate Time Series
Forecasting
|
Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling the hidden dependencies among different time series can yield promising forecasting performance and reliable explanations. However, modeling variable dependencies remains underexplored when MTS is continuously accumulated under different regimes (stages). Due to the potential distribution and dependency disparities, the underlying model may encounter the catastrophic forgetting problem, i.e., it is challenging to memorize and infer different types of variable dependencies across different regimes while maintaining forecasting performance. To address this issue, we propose a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS forecasting within a continual learning paradigm, which leverages structural knowledge to steer the forecasting model toward identifying and adapting to different regimes, and selects representative MTS samples from each regime for memory replay. Specifically, we develop a forecasting model based on graph structure learning, where a consistency regularization scheme is imposed between the learned variable dependencies and the structural knowledge while optimizing the forecasting objective over the MTS data. As such, MTS representations learned in each regime are associated with distinct structural knowledge, which helps the model memorize a variety of conceivable scenarios and results in accurate forecasts in the continual learning context. Meanwhile, we develop a representation-matching memory replay scheme that maximizes the temporal coverage of MTS data to efficiently preserve the underlying temporal dynamics and dependency structures of each regime. Thorough empirical studies on synthetic and real-world benchmarks validate SKI-CL's efficacy and advantages over the state-of-the-art for continual MTS forecasting tasks.
|
[
"['Zijie Pan' 'Yushan Jiang' 'Dongjin Song' 'Sahil Garg' 'Kashif Rasul'\n 'Anderson Schneider' 'Yuriy Nevmyvaka']"
] |
null | null |
2402.12727
| null | null |
http://arxiv.org/pdf/2402.12727v1
|
2024-02-20T05:28:13Z
|
2024-02-20T05:28:13Z
|
Diffusion Posterior Sampling is Computationally Intractable
|
Diffusion models are a remarkably effective way of learning and sampling from a distribution $p(x)$. In posterior sampling, one is also given a measurement model $p(y mid x)$ and a measurement $y$, and would like to sample from $p(x mid y)$. Posterior sampling is useful for tasks such as inpainting, super-resolution, and MRI reconstruction, so a number of recent works have given algorithms to heuristically approximate it; but none are known to converge to the correct distribution in polynomial time. In this paper we show that posterior sampling is emph{computationally intractable}: under the most basic assumption in cryptography -- that one-way functions exist -- there are instances for which emph{every} algorithm takes superpolynomial time, even though emph{unconditional} sampling is provably fast. We also show that the exponential-time rejection sampling algorithm is essentially optimal under the stronger plausible assumption that there are one-way functions that take exponential time to invert.
|
[
"['Shivam Gupta' 'Ajil Jalal' 'Aditya Parulekar' 'Eric Price' 'Zhiyang Xun']"
] |
null | null |
2402.12728
| null | null |
http://arxiv.org/pdf/2402.12728v2
|
2024-03-03T04:51:28Z
|
2024-02-20T05:32:24Z
|
Modality-Aware Integration with Large Language Models for
Knowledge-based Visual Question Answering
|
Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attempts have been proposed to leverage large language models (LLMs) as an implicit knowledge source, it remains challenging since LLMs may generate hallucinations. Moreover, multiple knowledge sources, e.g., images, KGs and LLMs, cannot be readily aligned for complex scenarios. To tackle these, we present a novel modality-aware integration with LLMs for KVQA (MAIL). It carefully leverages multimodal knowledge for both image understanding and knowledge reasoning. Specifically, (i) we propose a two-stage prompting strategy with LLMs to densely embody the image into a scene graph with detailed visual features; (ii) We construct a coupled concept graph by linking the mentioned entities with external facts. (iii) A tailored pseudo-siamese graph medium fusion is designed for sufficient multimodal fusion. We utilize the shared mentioned entities in two graphs as mediums to bridge a tight inter-modal exchange, while maximally preserving insightful intra-modal learning by constraining the fusion within mediums. Extensive experiments on two benchmark datasets show the superiority of MAIL with 24x less resources.
|
[
"['Junnan Dong' 'Qinggang Zhang' 'Huachi Zhou' 'Daochen Zha' 'Pai Zheng'\n 'Xiao Huang']"
] |
null | null |
2402.12729
| null | null |
http://arxiv.org/pdf/2402.12729v1
|
2024-02-20T05:39:32Z
|
2024-02-20T05:39:32Z
|
Scalable and reliable deep transfer learning for intelligent fault
detection via multi-scale neural processes embedded with knowledge
|
Deep transfer learning (DTL) is a fundamental method in the field of Intelligent Fault Detection (IFD). It aims to mitigate the degradation of method performance that arises from the discrepancies in data distribution between training set (source domain) and testing set (target domain). Considering the fact that fault data collection is challenging and certain faults are scarce, DTL-based methods face the limitation of available observable data, which reduces the detection performance of the methods in the target domain. Furthermore, DTL-based methods lack comprehensive uncertainty analysis that is essential for building reliable IFD systems. To address the aforementioned problems, this paper proposes a novel DTL-based method known as Neural Processes-based deep transfer learning with graph convolution network (GTNP). Feature-based transfer strategy of GTNP bridges the data distribution discrepancies of source domain and target domain in high-dimensional space. Both the joint modeling based on global and local latent variables and sparse sampling strategy reduce the demand of observable data in the target domain. The multi-scale uncertainty analysis is obtained by using the distribution characteristics of global and local latent variables. Global analysis of uncertainty enables GTNP to provide quantitative values that reflect the complexity of methods and the difficulty of tasks. Local analysis of uncertainty allows GTNP to model uncertainty (confidence of the fault detection result) at each sample affected by noise and bias. The validation of the proposed method is conducted across 3 IFD tasks, consistently showing the superior detection performance of GTNP compared to the other DTL-based methods.
|
[
"['Zhongzhi Li' 'Jingqi Tu' 'Jiacheng Zhu' 'Jianliang Ai' 'Yiqun Dong']"
] |
null | null |
2402.12730
| null | null |
http://arxiv.org/pdf/2402.12730v2
|
2024-04-12T00:53:29Z
|
2024-02-20T05:46:29Z
|
UMBCLU at SemEval-2024 Task 1A and 1C: Semantic Textual Relatedness with
and without machine translation
|
The aim of SemEval-2024 Task 1, "Semantic Textual Relatedness for African and Asian Languages" is to develop models for identifying semantic textual relatedness (STR) between two sentences using multiple languages (14 African and Asian languages) and settings (supervised, unsupervised, and cross-lingual). Large language models (LLMs) have shown impressive performance on several natural language understanding tasks such as multilingual machine translation (MMT), semantic similarity (STS), and encoding sentence embeddings. Using a combination of LLMs that perform well on these tasks, we developed two STR models, $textit{TranSem}$ and $textit{FineSem}$, for the supervised and cross-lingual settings. We explore the effectiveness of several training methods and the usefulness of machine translation. We find that direct fine-tuning on the task is comparable to using sentence embeddings and translating to English leads to better performance for some languages. In the supervised setting, our model performance is better than the official baseline for 3 languages with the remaining 4 performing on par. In the cross-lingual setting, our model performance is better than the baseline for 3 languages (leading to $1^{st}$ place for Africaans and $2^{nd}$ place for Indonesian), is on par for 2 languages and performs poorly on the remaining 7 languages. Our code is publicly available at https://github.com/dipta007/SemEval24-Task8.
|
[
"['Shubhashis Roy Dipta' 'Sai Vallurupalli']"
] |
null | null |
2402.12737
| null | null |
http://arxiv.org/pdf/2402.12737v1
|
2024-02-20T06:04:44Z
|
2024-02-20T06:04:44Z
|
Guarantee Regions for Local Explanations
|
Interpretability methods that utilise local surrogate models (e.g. LIME) are very good at describing the behaviour of the predictive model at a point of interest, but they are not guaranteed to extrapolate to the local region surrounding the point. However, overfitting to the local curvature of the predictive model and malicious tampering can significantly limit extrapolation. We propose an anchor-based algorithm for identifying regions in which local explanations are guaranteed to be correct by explicitly describing those intervals along which the input features can be trusted. Our method produces an interpretable feature-aligned box where the prediction of the local surrogate model is guaranteed to match the predictive model. We demonstrate that our algorithm can be used to find explanations with larger guarantee regions that better cover the data manifold compared to existing baselines. We also show how our method can identify misleading local explanations with significantly poorer guarantee regions.
|
[
"['Marton Havasi' 'Sonali Parbhoo' 'Finale Doshi-Velez']"
] |
null | null |
2402.12743
| null | null |
http://arxiv.org/pdf/2402.12743v1
|
2024-02-20T06:19:55Z
|
2024-02-20T06:19:55Z
|
APT-MMF: An advanced persistent threat actor attribution method based on
multimodal and multilevel feature fusion
|
Threat actor attribution is a crucial defense strategy for combating advanced persistent threats (APTs). Cyber threat intelligence (CTI), which involves analyzing multisource heterogeneous data from APTs, plays an important role in APT actor attribution. The current attribution methods extract features from different CTI perspectives and employ machine learning models to classify CTI reports according to their threat actors. However, these methods usually extract only one kind of feature and ignore heterogeneous information, especially the attributes and relations of indicators of compromise (IOCs), which form the core of CTI. To address these problems, we propose an APT actor attribution method based on multimodal and multilevel feature fusion (APT-MMF). First, we leverage a heterogeneous attributed graph to characterize APT reports and their IOC information. Then, we extract and fuse multimodal features, including attribute type features, natural language text features and topological relationship features, to construct comprehensive node representations. Furthermore, we design multilevel heterogeneous graph attention networks to learn the deep hidden features of APT report nodes; these networks integrate IOC type-level, metapath-based neighbor node-level, and metapath semantic-level attention. Utilizing multisource threat intelligence, we construct a heterogeneous attributed graph dataset for verification purposes. The experimental results show that our method not only outperforms the existing methods but also demonstrates its good interpretability for attribution analysis tasks.
|
[
"['Nan Xiao' 'Bo Lang' 'Ting Wang' 'Yikai Chen']"
] |
null | null |
2402.12756
| null | null |
http://arxiv.org/pdf/2402.12756v1
|
2024-02-20T06:49:43Z
|
2024-02-20T06:49:43Z
|
Static vs. Dynamic Databases for Indoor Localization based on Wi-Fi
Fingerprinting: A Discussion from a Data Perspective
|
Wi-Fi fingerprinting has emerged as the most popular approach to indoor localization. The use of ML algorithms has greatly improved the localization performance of Wi-Fi fingerprinting, but its success depends on the availability of fingerprint databases composed of a large number of RSSIs, the MAC addresses of access points, and the other measurement information. However, most fingerprint databases do not reflect well the time varying nature of electromagnetic interferences in complicated modern indoor environment. This could result in significant changes in statistical characteristics of training/validation and testing datasets, which are often constructed at different times, and even the characteristics of the testing datasets could be different from those of the data submitted by users during the operation of localization systems after their deployment. In this paper, we consider the implications of time-varying Wi-Fi fingerprints on indoor localization from a data-centric point of view and discuss the differences between static and dynamic databases. As a case study, we have constructed a dynamic database covering three floors of the IR building of XJTLU based on RSSI measurements, over 44 days, and investigated the differences between static and dynamic databases in terms of statistical characteristics and localization performance. The analyses based on variance calculations and Isolation Forest show the temporal shifts in RSSIs, which result in a noticeable trend of the increase in the localization error of a Gaussian process regression model with the maximum error of 6.65 m after 14 days of training without model adjustments. The results of the case study with the XJTLU dynamic database clearly demonstrate the limitations of static databases and the importance of the creation and adoption of dynamic databases for future indoor localization research and real-world deployment.
|
[
"['Zhe Tang' 'Ruocheng Gu' 'Sihao Li' 'Kyeong Soo Kim' 'Jeremy S. Smith']"
] |
null | null |
2402.12761
| null | null |
http://arxiv.org/pdf/2402.12761v1
|
2024-02-20T07:03:59Z
|
2024-02-20T07:03:59Z
|
FGAD: Self-boosted Knowledge Distillation for An Effective Federated
Graph Anomaly Detection Framework
|
Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones, which has raised growing attention due to the broad existence and complexity of graph-structured data in many real-world scenarios. However, existing GAD methods usually execute with centralized training, which may lead to privacy leakage risk in some sensitive cases, thereby impeding collaboration among organizations seeking to collectively develop robust GAD models. Although federated learning offers a promising solution, the prevalent non-IID problems and high communication costs present significant challenges, particularly pronounced in collaborations with graph data distributed among different participants. To tackle these challenges, we propose an effective federated graph anomaly detection framework (FGAD). We first introduce an anomaly generator to perturb the normal graphs to be anomalous, and train a powerful anomaly detector by distinguishing generated anomalous graphs from normal ones. Then, we leverage a student model to distill knowledge from the trained anomaly detector (teacher model), which aims to maintain the personality of local models and alleviate the adverse impact of non-IID problems. Moreover, we design an effective collaborative learning mechanism that facilitates the personalization preservation of local models and significantly reduces communication costs among clients. Empirical results of the GAD tasks on non-IID graphs compared with state-of-the-art baselines demonstrate the superiority and efficiency of the proposed FGAD method.
|
[
"['Jinyu Cai' 'Yunhe Zhang' 'Zhoumin Lu' 'Wenzhong Guo' 'See-kiong Ng']"
] |
null | null |
2402.12762
| null | null |
http://arxiv.org/pdf/2402.12762v2
|
2024-02-22T08:32:24Z
|
2024-02-20T07:09:39Z
|
Learning under Singularity: An Information Criterion improving WBIC and
sBIC
|
We introduce a novel Information Criterion (IC), termed Learning under Singularity (LS), designed to enhance the functionality of the Widely Applicable Bayes Information Criterion (WBIC) and the Singular Bayesian Information Criterion (sBIC). LS is effective without regularity constraints and demonstrates stability. Watanabe defined a statistical model or a learning machine as regular if the mapping from a parameter to a probability distribution is one-to-one and its Fisher information matrix is positive definite. In contrast, models not meeting these conditions are termed singular. Over the past decade, several information criteria for singular cases have been proposed, including WBIC and sBIC. WBIC is applicable in non-regular scenarios but faces challenges with large sample sizes and redundant estimation of known learning coefficients. Conversely, sBIC is limited in its broader application due to its dependence on maximum likelihood estimates. LS addresses these limitations by enhancing the utility of both WBIC and sBIC. It incorporates the empirical loss from the Widely Applicable Information Criterion (WAIC) to represent the goodness of fit to the statistical model, along with a penalty term similar to that of sBIC. This approach offers a flexible and robust method for model selection, free from regularity constraints.
|
[
"['Lirui Liu' 'Joe Suzuki']"
] |
null | null |
2402.12767
| null | null |
http://arxiv.org/pdf/2402.12767v3
|
2024-06-07T11:11:31Z
|
2024-02-20T07:16:12Z
|
When and How: Learning Identifiable Latent States for Nonstationary Time
Series Forecasting
|
Temporal distribution shifts are ubiquitous in time series data. One of the most popular methods assumes that the temporal distribution shift occurs uniformly to disentangle the stationary and nonstationary dependencies. But this assumption is difficult to meet, as we do not know when the distribution shifts occur. To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur. Beyond that, we further disentangle the stationary and nonstationary latent states via sufficient observation assumption to learn how the latent states change. Specifically, we formalize the causal process with environment-irrelated stationary and environment-related nonstationary variables. Under mild conditions, we show that latent environments and stationary/nonstationary variables are identifiable. Based on these theories, we devise the IDEA model, which incorporates an autoregressive hidden Markov model to estimate latent environments and modular prior networks to identify latent states. The IDEA model outperforms several latest nonstationary forecasting methods on various benchmark datasets, highlighting its advantages in real-world scenarios.
|
[
"['Zijian Li' 'Ruichu Cai' 'Zhenhui Yang' 'Haiqin Huang' 'Guangyi Chen'\n 'Yifan Shen' 'Zhengming Chen' 'Xiangchen Song' 'Kun Zhang']"
] |
null | null |
2402.12780
| null | null |
http://arxiv.org/pdf/2402.12780v2
|
2024-06-10T13:43:21Z
|
2024-02-20T07:40:11Z
|
Byzantine-Robust Federated Learning: Impact of Client Subsampling and
Local Updates
|
The possibility of adversarial (a.k.a., {em Byzantine}) clients makes federated learning (FL) prone to arbitrary manipulation. The natural approach to robustify FL against adversarial clients is to replace the simple averaging operation at the server in the standard $mathsf{FedAvg}$ algorithm by a emph{robust averaging rule}. While a significant amount of work has been devoted to studying the convergence of federated {em robust averaging} (which we denote by $mathsf{FedRo}$), prior work has largely ignored the impact of {em client subsampling} and {em local steps}, two fundamental FL characteristics. While client subsampling increases the effective fraction of Byzantine clients, local steps increase the drift between the local updates computed by honest (i.e., non-Byzantine) clients. Consequently, a careless deployment of $mathsf{FedRo}$ could yield poor performance. We validate this observation by presenting an in-depth analysis of $mathsf{FedRo}$ tightly analyzing the impact of client subsampling and local steps. Specifically, we present a sufficient condition on client subsampling for nearly-optimal convergence of $mathsf{FedRo}$ (for smooth non-convex loss). Also, we show that the rate of improvement in learning accuracy {em diminishes} with respect to the number of clients subsampled, as soon as the sample size exceeds a threshold value. Interestingly, we also observe that under a careful choice of step-sizes, the learning error due to Byzantine clients decreases with the number of local steps. We validate our theory by experiments on the FEMNIST and CIFAR-$10$ image classification tasks.
|
[
"['Youssef Allouah' 'Sadegh Farhadkhani' 'Rachid GuerraouI' 'Nirupam Gupta'\n 'Rafael Pinot' 'Geovani Rizk' 'Sasha Voitovych']"
] |
null | null |
2402.12789
| null | null |
http://arxiv.org/pdf/2402.12789v2
|
2024-05-31T21:11:10Z
|
2024-02-20T07:57:38Z
|
Fairness Without Harm: An Influence-Guided Active Sampling Approach
|
The pursuit of fairness in machine learning (ML), ensuring that the models do not exhibit biases toward protected demographic groups, typically results in a compromise scenario. This compromise can be explained by a Pareto frontier where given certain resources (e.g., data), reducing the fairness violations often comes at the cost of lowering the model accuracy. In this work, we aim to train models that mitigate group fairness disparity without causing harm to model accuracy. Intuitively, acquiring more data is a natural and promising approach to achieve this goal by reaching a better Pareto frontier of the fairness-accuracy tradeoff. The current data acquisition methods, such as fair active learning approaches, typically require annotating sensitive attributes. However, these sensitive attribute annotations should be protected due to privacy and safety concerns. In this paper, we propose a tractable active data sampling algorithm that does not rely on training group annotations, instead only requiring group annotations on a small validation set. Specifically, the algorithm first scores each new example by its influence on fairness and accuracy evaluated on the validation dataset, and then selects a certain number of examples for training. We theoretically analyze how acquiring more data can improve fairness without causing harm, and validate the possibility of our sampling approach in the context of risk disparity. We also provide the upper bound of generalization error and risk disparity as well as the corresponding connections. Extensive experiments on real-world data demonstrate the effectiveness of our proposed algorithm.
|
[
"['Jinlong Pang' 'Jialu Wang' 'Zhaowei Zhu' 'Yuanshun Yao' 'Chen Qian'\n 'Yang Liu']"
] |
null | null |
2402.12790
| null | null |
http://arxiv.org/pdf/2402.12790v1
|
2024-02-20T07:58:04Z
|
2024-02-20T07:58:04Z
|
From Movements to Metrics: Evaluating Explainable AI Methods in
Skeleton-Based Human Activity Recognition
|
The advancement of deep learning in human activity recognition (HAR) using 3D skeleton data is critical for applications in healthcare, security, sports, and human-computer interaction. This paper tackles a well-known gap in the field, which is the lack of testing in the applicability and reliability of XAI evaluation metrics in the skeleton-based HAR domain. We have tested established XAI metrics namely faithfulness and stability on Class Activation Mapping (CAM) and Gradient-weighted Class Activation Mapping (Grad-CAM) to address this problem. The study also introduces a perturbation method that respects human biomechanical constraints to ensure realistic variations in human movement. Our findings indicate that textit{faithfulness} may not be a reliable metric in certain contexts, such as with the EfficientGCN model. Conversely, stability emerges as a more dependable metric when there is slight input data perturbations. CAM and Grad-CAM are also found to produce almost identical explanations, leading to very similar XAI metric performance. This calls for the need for more diversified metrics and new XAI methods applied in skeleton-based HAR.
|
[
"['Kimji N. Pellano' 'Inga Strümke' 'Espen Alexander F. Ihlen']"
] |
null | null |
2402.12794
| null | null |
http://arxiv.org/pdf/2402.12794v1
|
2024-02-20T08:08:07Z
|
2024-02-20T08:08:07Z
|
Autonomous Reality Modelling for Cultural Heritage Sites employing
cooperative quadrupedal robots and unmanned aerial vehicles
|
Nowadays, the use of advanced sensors, such as terrestrial 3D laser scanners, mobile LiDARs and Unmanned Aerial Vehicles (UAV) photogrammetric imaging, has become the prevalent practice for 3D Reality Modeling and digitization of large-scale monuments of Cultural Heritage (CH). In practice, this process is heavily related to the expertise of the surveying team, handling the laborious planning and time-consuming execution of the 3D mapping process that is tailored to the specific requirements and constraints of each site. To minimize human intervention, this paper introduces a novel methodology for autonomous 3D Reality Modeling for CH monuments by employing au-tonomous biomimetic quadrupedal robotic agents and UAVs equipped with the appropriate sensors. These autonomous robotic agents carry out the 3D RM process in a systematic and repeatable ap-proach. The outcomes of this automated process may find applications in digital twin platforms, facilitating secure monitoring and management of cultural heritage sites and spaces, in both indoor and outdoor environments.
|
[
"['Nikolaos Giakoumidis' 'Christos-Nikolaos Anagnostopoulos']"
] |
null | null |
2402.12808
| null | null |
http://arxiv.org/pdf/2402.12808v1
|
2024-02-20T08:27:50Z
|
2024-02-20T08:27:50Z
|
Learning Generalization and Regularization of Nonhomogeneous Temporal
Poisson Processes
|
The Poisson process, especially the nonhomogeneous Poisson process (NHPP), is an essentially important counting process with numerous real-world applications. Up to date, almost all works in the literature have been on the estimation of NHPPs with infinite data using non-data driven binning methods. In this paper, we formulate the problem of estimation of NHPPs from finite and limited data as a learning generalization problem. We mathematically show that while binning methods are essential for the estimation of NHPPs, they pose a threat of overfitting when the amount of data is limited. We propose a framework for regularized learning of NHPPs with two new adaptive and data-driven binning methods that help to remove the ad-hoc tuning of binning parameters. Our methods are experimentally tested on synthetic and real-world datasets and the results show their effectiveness.
|
[
"['Son Nguyen Van' 'Hoai Nguyen Xuan']"
] |
null | null |
2402.12812
| null | null |
http://arxiv.org/pdf/2402.12812v3
|
2024-05-08T12:59:45Z
|
2024-02-20T08:30:46Z
|
Scalable Decentralized Algorithms for Online Personalized Mean
Estimation
|
In numerous settings, agents lack sufficient data to directly learn a model. Collaborating with other agents may help, but it introduces a bias-variance trade-off, when local data distributions differ. A key challenge is for each agent to identify clients with similar distributions while learning the model, a problem that remains largely unresolved. This study focuses on a simplified version of the overarching problem, where each agent collects samples from a real-valued distribution over time to estimate its mean. Existing algorithms face impractical space and time complexities (quadratic in the number of agents A). To address scalability challenges, we propose a framework where agents self-organize into a graph, allowing each agent to communicate with only a selected number of peers r. We introduce two collaborative mean estimation algorithms: one draws inspiration from belief propagation, while the other employs a consensus-based approach, with complexity of O( r |A| log |A|) and O(r |A|), respectively. We establish conditions under which both algorithms yield asymptotically optimal estimates and offer a theoretical characterization of their performance.
|
[
"['Franco Galante' 'Giovanni Neglia' 'Emilio Leonardi']"
] |
null | null |
2402.12817
| null | null |
http://arxiv.org/pdf/2402.12817v1
|
2024-02-20T08:38:19Z
|
2024-02-20T08:38:19Z
|
On Sensitivity of Learning with Limited Labelled Data to the Effects of
Randomness: Impact of Interactions and Systematic Choices
|
While learning with limited labelled data can improve performance when the labels are lacking, it is also sensitive to the effects of uncontrolled randomness introduced by so-called randomness factors (e.g., varying order of data). We propose a method to systematically investigate the effects of randomness factors while taking the interactions between them into consideration. To measure the true effects of an individual randomness factor, our method mitigates the effects of other factors and observes how the performance varies across multiple runs. Applying our method to multiple randomness factors across in-context learning and fine-tuning approaches on 7 representative text classification tasks and meta-learning on 3 tasks, we show that: 1) disregarding interactions between randomness factors in existing works caused inconsistent findings due to incorrect attribution of the effects of randomness factors, such as disproving the consistent sensitivity of in-context learning to sample order even with random sample selection; and 2) besides mutual interactions, the effects of randomness factors, especially sample order, are also dependent on more systematic choices unexplored in existing works, such as number of classes, samples per class or choice of prompt format.
|
[
"['Branislav Pecher' 'Ivan Srba' 'Maria Bielikova']"
] |
null | null |
2402.12819
| null | null |
http://arxiv.org/pdf/2402.12819v2
|
2024-04-26T08:20:40Z
|
2024-02-20T08:38:24Z
|
Comparing Specialised Small and General Large Language Models on Text
Classification: 100 Labelled Samples to Achieve Break-Even Performance
|
When solving NLP tasks with limited labelled data, researchers can either use a general large language model without further update, or use a small number of labelled examples to tune a specialised smaller model. In this work, we address the research gap of how many labelled samples are required for the specialised small models to outperform general large models, while taking the performance variance into consideration. By observing the behaviour of fine-tuning, instruction-tuning, prompting and in-context learning on 7 language models, we identify such performance break-even points across 8 representative text classification tasks of varying characteristics. We show that the specialised models often need only few samples (on average $10 - 1000$) to be on par or better than the general ones. At the same time, the number of required labels strongly depends on the dataset or task characteristics, with this number being significantly lower on multi-class datasets (up to $100$) than on binary datasets (up to $5000$). When performance variance is taken into consideration, the number of required labels increases on average by $100 - 200%$ and even up to $1500%$ in specific cases.
|
[
"['Branislav Pecher' 'Ivan Srba' 'Maria Bielikova']"
] |
null | null |
2402.12821
| null | null |
http://arxiv.org/pdf/2402.12821v2
|
2024-06-20T03:45:51Z
|
2024-02-20T08:41:23Z
|
Identifying Factual Inconsistencies in Summaries: Grounding Model
Inference via Task Taxonomy
|
Factual inconsistencies pose a significant hurdle for the faithful summarization by generative models. While a major direction to enhance inconsistency detection is to derive stronger Natural Language Inference (NLI) models, we propose an orthogonal aspect that underscores the importance of incorporating task-specific taxonomy into the inference. To this end, we consolidate key error types of inconsistent facts in summaries, and incorporate them to facilitate both the zero-shot and supervised paradigms of LLMs. Extensive experiments on ten datasets of five distinct domains suggest that, zero-shot LLM inference could benefit from the explicit solution space depicted by the error type taxonomy, and achieves state-of-the-art performance overall, surpassing specialized non-LLM baselines, as well as recent LLM baselines. We further distill models that fuse the taxonomy into parameters through our designed prompt completions and supervised training strategies, efficiently substituting state-of-the-art zero-shot inference with much larger LLMs.
|
[
"['Liyan Xu' 'Zhenlin Su' 'Mo Yu' 'Jin Xu' 'Jinho D. Choi' 'Jie Zhou'\n 'Fei Liu']"
] |
null | null |
2402.12828
| null | null |
http://arxiv.org/pdf/2402.12828v1
|
2024-02-20T08:54:07Z
|
2024-02-20T08:54:07Z
|
SGD with Clipping is Secretly Estimating the Median Gradient
|
There are several applications of stochastic optimization where one can benefit from a robust estimate of the gradient. For example, domains such as distributed learning with corrupted nodes, the presence of large outliers in the training data, learning under privacy constraints, or even heavy-tailed noise due to the dynamics of the algorithm itself. Here we study SGD with robust gradient estimators based on estimating the median. We first consider computing the median gradient across samples, and show that the resulting method can converge even under heavy-tailed, state-dependent noise. We then derive iterative methods based on the stochastic proximal point method for computing the geometric median and generalizations thereof. Finally we propose an algorithm estimating the median gradient across iterations, and find that several well known methods - in particular different forms of clipping - are particular cases of this framework.
|
[
"['Fabian Schaipp' 'Guillaume Garrigos' 'Umut Simsekli' 'Robert Gower']"
] |
null | null |
2402.12842
| null | null |
http://arxiv.org/pdf/2402.12842v2
|
2024-06-24T05:40:38Z
|
2024-02-20T09:10:08Z
|
PromptKD: Distilling Student-Friendly Knowledge for Generative Language
Models via Prompt Tuning
|
Recent advancements in large language models (LLMs) have raised concerns about inference costs, increasing the need for research into model compression. While knowledge distillation (KD) is a prominent method for this, research on KD for generative language models like LLMs is relatively sparse, and the approach of distilling student-friendly knowledge, which has shown promising performance in KD for classification models, remains unexplored in generative language models. To explore this approach, we propose PromptKD, a simple yet effective method that utilizes prompt tuning - for the first time in KD - to enable generative language models to transfer student-friendly knowledge. Unlike previous works in classification that require fine-tuning the entire teacher model for extracting student-friendly knowledge, PromptKD achieves similar effects by adding a small number of prompt tokens and tuning only the prompt with student guidance. Extensive experiments on instruction-following datasets show that PromptKD achieves state-of-the-art performance while adding only 0.0007% of the teacher's parameters as prompts. Further analysis suggests that distilling student-friendly knowledge alleviates exposure bias effectively throughout the entire training process, leading to performance enhancements.
|
[
"['Gyeongman Kim' 'Doohyuk Jang' 'Eunho Yang']"
] |
null | null |
2402.12847
| null | null |
http://arxiv.org/pdf/2402.12847v2
|
2024-05-26T03:19:48Z
|
2024-02-20T09:20:32Z
|
Instruction-tuned Language Models are Better Knowledge Learners
|
In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge through continued training on new data. The standard recipe for doing so involves continued pre-training on new documents followed by instruction-tuning on question-answer (QA) pairs. However, we find that LLMs trained with this recipe struggle to answer questions, even though the perplexity of documents is minimized. We found that QA pairs are generally straightforward, while documents are more complex, weaving many factual statements together in an intricate manner. Therefore, we hypothesize that it is beneficial to expose LLMs to QA pairs before continued pre-training on documents so that the process of encoding knowledge from complex documents takes into account how this knowledge is accessed through questions. Based on this, we propose pre-instruction-tuning (PIT), a method that instruction-tunes on questions prior to training on documents. This contrasts with standard instruction-tuning, which learns how to extract knowledge after training on documents. Extensive experiments and ablation studies demonstrate that pre-instruction-tuning significantly enhances the ability of LLMs to absorb knowledge from new documents, outperforming standard instruction-tuning by 17.8%.
|
[
"['Zhengbao Jiang' 'Zhiqing Sun' 'Weijia Shi' 'Pedro Rodriguez'\n 'Chunting Zhou' 'Graham Neubig' 'Xi Victoria Lin' 'Wen-tau Yih'\n 'Srinivasan Iyer']"
] |
null | null |
2402.12852
| null | null |
http://arxiv.org/pdf/2402.12852v1
|
2024-02-20T09:31:03Z
|
2024-02-20T09:31:03Z
|
CCFC++: Enhancing Federated Clustering through Feature Decorrelation
|
In federated clustering, multiple data-holding clients collaboratively group data without exchanging raw data. This field has seen notable advancements through its marriage with contrastive learning, exemplified by Cluster-Contrastive Federated Clustering (CCFC). However, CCFC suffers from heterogeneous data across clients, leading to poor and unrobust performance. Our study conducts both empirical and theoretical analyses to understand the impact of heterogeneous data on CCFC. Findings indicate that increased data heterogeneity exacerbates dimensional collapse in CCFC, evidenced by increased correlations across multiple dimensions of the learned representations. To address this, we introduce a decorrelation regularizer to CCFC. Benefiting from the regularizer, the improved method effectively mitigates the detrimental effects of data heterogeneity, and achieves superior performance, as evidenced by a marked increase in NMI scores, with the gain reaching as high as 0.32 in the most pronounced case.
|
[
"['Jie Yan' 'Jing Liu' 'Yi-Zi Ning' 'Zhong-Yuan Zhang']"
] |
null | null |
2402.12854
| null | null |
http://arxiv.org/pdf/2402.12854v1
|
2024-02-20T09:33:22Z
|
2024-02-20T09:33:22Z
|
Differentiable Mapper For Topological Optimization Of Data
Representation
|
Unsupervised data representation and visualization using tools from topology is an active and growing field of Topological Data Analysis (TDA) and data science. Its most prominent line of work is based on the so-called Mapper graph, which is a combinatorial graph whose topological structures (connected components, branches, loops) are in correspondence with those of the data itself. While highly generic and applicable, its use has been hampered so far by the manual tuning of its many parameters-among these, a crucial one is the so-called filter: it is a continuous function whose variations on the data set are the main ingredient for both building the Mapper representation and assessing the presence and sizes of its topological structures. However, while a few parameter tuning methods have already been investigated for the other Mapper parameters (i.e., resolution, gain, clustering), there is currently no method for tuning the filter itself. In this work, we build on a recently proposed optimization framework incorporating topology to provide the first filter optimization scheme for Mapper graphs. In order to achieve this, we propose a relaxed and more general version of the Mapper graph, whose convergence properties are investigated. Finally, we demonstrate the usefulness of our approach by optimizing Mapper graph representations on several datasets, and showcasing the superiority of the optimized representation over arbitrary ones.
|
[
"['Ziyad Oulhaj' 'Mathieu Carrière' 'Bertrand Michel']"
] |
null | null |
2402.12861
| null | null |
http://arxiv.org/pdf/2402.12861v1
|
2024-02-20T09:52:30Z
|
2024-02-20T09:52:30Z
|
Bounding Reconstruction Attack Success of Adversaries Without Data
Priors
|
Reconstruction attacks on machine learning (ML) models pose a strong risk of leakage of sensitive data. In specific contexts, an adversary can (almost) perfectly reconstruct training data samples from a trained model using the model's gradients. When training ML models with differential privacy (DP), formal upper bounds on the success of such reconstruction attacks can be provided. So far, these bounds have been formulated under worst-case assumptions that might not hold high realistic practicality. In this work, we provide formal upper bounds on reconstruction success under realistic adversarial settings against ML models trained with DP and support these bounds with empirical results. With this, we show that in realistic scenarios, (a) the expected reconstruction success can be bounded appropriately in different contexts and by different metrics, which (b) allows for a more educated choice of a privacy parameter.
|
[
"['Alexander Ziller' 'Anneliese Riess' 'Kristian Schwethelm'\n 'Tamara T. Mueller' 'Daniel Rueckert' 'Georgios Kaissis']"
] |
null | null |
2402.12865
| null | null |
http://arxiv.org/pdf/2402.12865v1
|
2024-02-20T09:57:08Z
|
2024-02-20T09:57:08Z
|
Backward Lens: Projecting Language Model Gradients into the Vocabulary
Space
|
Understanding how Transformer-based Language Models (LMs) learn and recall information is a key goal of the deep learning community. Recent interpretability methods project weights and hidden states obtained from the forward pass to the models' vocabularies, helping to uncover how information flows within LMs. In this work, we extend this methodology to LMs' backward pass and gradients. We first prove that a gradient matrix can be cast as a low-rank linear combination of its forward and backward passes' inputs. We then develop methods to project these gradients into vocabulary items and explore the mechanics of how new information is stored in the LMs' neurons.
|
[
"['Shahar Katz' 'Yonatan Belinkov' 'Mor Geva' 'Lior Wolf']"
] |
null | null |
2402.12867
| null | null |
http://arxiv.org/pdf/2402.12867v1
|
2024-02-20T09:57:49Z
|
2024-02-20T09:57:49Z
|
Towards MLOps: A DevOps Tools Recommender System for Machine Learning
System
|
Applying DevOps practices to machine learning system is termed as MLOps and machine learning systems evolve on new data unlike traditional systems on requirements. The objective of MLOps is to establish a connection between different open-source tools to construct a pipeline that can automatically perform steps to construct a dataset, train the machine learning model and deploy the model to the production as well as store different versions of model and dataset. Benefits of MLOps is to make sure the fast delivery of the new trained models to the production to have accurate results. Furthermore, MLOps practice impacts the overall quality of the software products and is completely dependent on open-source tools and selection of relevant open-source tools is considered as challenged while a generalized method to select an appropriate open-source tools is desirable. In this paper, we present a framework for recommendation system that processes the contextual information (e.g., nature of data, type of the data) of the machine learning project and recommends a relevant toolchain (tech-stack) for the operationalization of machine learning systems. To check the applicability of the proposed framework, four different approaches i.e., rule-based, random forest, decision trees and k-nearest neighbors were investigated where precision, recall and f-score is measured, the random forest out classed other approaches with highest f-score value of 0.66.
|
[
"['Pir Sami Ullah Shah' 'Naveed Ahmad' 'Mirza Omer Beg']"
] |
null | null |
2402.12868
| null | null |
http://arxiv.org/pdf/2402.12868v1
|
2024-02-20T09:59:33Z
|
2024-02-20T09:59:33Z
|
Fast Rates in Online Convex Optimization by Exploiting the Curvature of
Feasible Sets
|
In this paper, we explore online convex optimization (OCO) and introduce a new analysis that provides fast rates by exploiting the curvature of feasible sets. In online linear optimization, it is known that if the average gradient of loss functions is larger than a certain value, the curvature of feasible sets can be exploited by the follow-the-leader (FTL) algorithm to achieve a logarithmic regret. This paper reveals that algorithms adaptive to the curvature of loss functions can also leverage the curvature of feasible sets. We first prove that if an optimal decision is on the boundary of a feasible set and the gradient of an underlying loss function is non-zero, then the algorithm achieves a regret upper bound of $O(rho log T)$ in stochastic environments. Here, $rho > 0$ is the radius of the smallest sphere that includes the optimal decision and encloses the feasible set. Our approach, unlike existing ones, can work directly with convex loss functions, exploiting the curvature of loss functions simultaneously, and can achieve the logarithmic regret only with a local property of feasible sets. Additionally, it achieves an $O(sqrt{T})$ regret even in adversarial environments where FTL suffers an $Omega(T)$ regret, and attains an $O(rho log T + sqrt{C rho log T})$ regret bound in corrupted stochastic environments with corruption level $C$. Furthermore, by extending our analysis, we establish a regret upper bound of $OBig(T^{frac{q-2}{2(q-1)}} (log T)^{frac{q}{2(q-1)}}Big)$ for $q$-uniformly convex feasible sets, where uniformly convex sets include strongly convex sets and $ell_p$-balls for $p in [1,infty)$. This bound bridges the gap between the $O(log T)$ regret bound for strongly convex sets ($q=2$) and the $O(sqrt{T})$ regret bound for non-curved sets ($qtoinfty$).
|
[
"['Taira Tsuchiya' 'Shinji Ito']"
] |
null | null |
2402.12874
| null | null |
http://arxiv.org/pdf/2402.12874v1
|
2024-02-20T10:09:00Z
|
2024-02-20T10:09:00Z
|
Skill or Luck? Return Decomposition via Advantage Functions
|
Learning from off-policy data is essential for sample-efficient reinforcement learning. In the present work, we build on the insight that the advantage function can be understood as the causal effect of an action on the return, and show that this allows us to decompose the return of a trajectory into parts caused by the agent's actions (skill) and parts outside of the agent's control (luck). Furthermore, this decomposition enables us to naturally extend Direct Advantage Estimation (DAE) to off-policy settings (Off-policy DAE). The resulting method can learn from off-policy trajectories without relying on importance sampling techniques or truncating off-policy actions. We draw connections between Off-policy DAE and previous methods to demonstrate how it can speed up learning and when the proposed off-policy corrections are important. Finally, we use the MinAtar environments to illustrate how ignoring off-policy corrections can lead to suboptimal policy optimization performance.
|
[
"['Hsiao-Ru Pan' 'Bernhard Schölkopf']"
] |
null | null |
2402.12875
| null | null |
http://arxiv.org/pdf/2402.12875v3
|
2024-05-23T17:10:39Z
|
2024-02-20T10:11:03Z
|
Chain of Thought Empowers Transformers to Solve Inherently Serial
Problems
|
Instructing the model to generate a sequence of intermediate steps, a.k.a., a chain of thought (CoT), is a highly effective method to improve the accuracy of large language models (LLMs) on arithmetics and symbolic reasoning tasks. However, the mechanism behind CoT remains unclear. This work provides a theoretical understanding of the power of CoT for decoder-only transformers through the lens of expressiveness. Conceptually, CoT empowers the model with the ability to perform inherently serial computation, which is otherwise lacking in transformers, especially when depth is low. Given input length $n$, previous works have shown that constant-depth transformers with finite precision $mathsf{poly}(n)$ embedding size can only solve problems in $mathsf{TC}^0$ without CoT. We first show an even tighter expressiveness upper bound for constant-depth transformers with constant-bit precision, which can only solve problems in $mathsf{AC}^0$, a proper subset of $ mathsf{TC}^0$. However, with $T$ steps of CoT, constant-depth transformers using constant-bit precision and $O(log n)$ embedding size can solve any problem solvable by boolean circuits of size $T$. Empirically, enabling CoT dramatically improves the accuracy for tasks that are hard for parallel computation, including the composition of permutation groups, iterated squaring, and circuit value problems, especially for low-depth transformers.
|
[
"['Zhiyuan Li' 'Hong Liu' 'Denny Zhou' 'Tengyu Ma']"
] |
null | null |
2402.12876
| null | null |
http://arxiv.org/pdf/2402.12876v2
|
2024-04-16T03:48:17Z
|
2024-02-20T10:13:44Z
|
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental
Study
|
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model training on multi-task learning datasets. However, a comprehensive evaluation method, integrating the unique features of both FL and MTL, is currently absent in the field. This paper fills this void by introducing a novel framework, FMTL-Bench, for systematic evaluation of the FMTL paradigm. This benchmark covers various aspects at the data, model, and optimization algorithm levels, and comprises seven sets of comparative experiments, encapsulating a wide array of non-independent and identically distributed (Non-IID) data partitioning scenarios. We propose a systematic process for comparing baselines of diverse indicators and conduct a case study on communication expenditure, time, and energy consumption. Through our exhaustive experiments, we aim to provide valuable insights into the strengths and limitations of existing baseline methods, contributing to the ongoing discourse on optimal FMTL application in practical scenarios. The source code can be found on https://github.com/youngfish42/FMTL-Benchmark .
|
[
"['Yuwen Yang' 'Yuxiang Lu' 'Suizhi Huang' 'Shalayiding Sirejiding'\n 'Hongtao Lu' 'Yue Ding']"
] |
null | null |
2402.12885
| null | null |
http://arxiv.org/pdf/2402.12885v1
|
2024-02-20T10:25:44Z
|
2024-02-20T10:25:44Z
|
A Bound on the Maximal Marginal Degrees of Freedom
|
Common kernel ridge regression is expensive in memory allocation and computation time. This paper addresses low rank approximations and surrogates for kernel ridge regression, which bridge these difficulties. The fundamental contribution of the paper is a lower bound on the rank of the low dimensional approximation, which is required such that the prediction power remains reliable. The bound relates the effective dimension with the largest statistical leverage score. We characterize the effective dimension and its growth behavior with respect to the regularization parameter by involving the regularity of the kernel. This growth is demonstrated to be asymptotically logarithmic for suitably chosen kernels, justifying low-rank approximations as the Nystr"om method.
|
[
"['Paul Dommel']"
] |
null | null |
2402.12890
| null | null |
http://arxiv.org/pdf/2402.12890v1
|
2024-02-20T10:34:19Z
|
2024-02-20T10:34:19Z
|
More Discriminative Sentence Embeddings via Semantic Graph Smoothing
|
This paper explores an empirical approach to learn more discriminantive sentence representations in an unsupervised fashion. Leveraging semantic graph smoothing, we enhance sentence embeddings obtained from pretrained models to improve results for the text clustering and classification tasks. Our method, validated on eight benchmarks, demonstrates consistent improvements, showcasing the potential of semantic graph smoothing in improving sentence embeddings for the supervised and unsupervised document categorization tasks.
|
[
"['Chakib Fettal' 'Lazhar Labiod' 'Mohamed Nadif']"
] |
null | null |
2402.12908
| null | null |
http://arxiv.org/pdf/2402.12908v2
|
2024-05-24T08:26:46Z
|
2024-02-20T10:56:52Z
|
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image
Diffusion Models
|
Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositional generation. In this paper, we propose RealCompo, a new training-free and transferred-friendly text-to-image generation framework, which aims to leverage the respective advantages of text-to-image models and spatial-aware image diffusion models (e.g., layout, keypoints and segmentation maps) to enhance both realism and compositionality of the generated images. An intuitive and novel balancer is proposed to dynamically balance the strengths of the two models in denoising process, allowing plug-and-play use of any model without extra training. Extensive experiments show that our RealCompo consistently outperforms state-of-the-art text-to-image models and spatial-aware image diffusion models in multiple-object compositional generation while keeping satisfactory realism and compositionality of the generated images. Notably, our RealCompo can be seamlessly extended with a wide range of spatial-aware image diffusion models and stylized diffusion models. Our code is available at: https://github.com/YangLing0818/RealCompo
|
[
"['Xinchen Zhang' 'Ling Yang' 'Yaqi Cai' 'Zhaochen Yu' 'Kai-Ni Wang'\n 'Jiake Xie' 'Ye Tian' 'Minkai Xu' 'Yong Tang' 'Yujiu Yang' 'Bin Cui']"
] |
null | null |
2402.12916
| null | null |
http://arxiv.org/pdf/2402.12916v1
|
2024-02-20T11:06:42Z
|
2024-02-20T11:06:42Z
|
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of
Machine Learning Models
|
Data Pipeline plays an indispensable role in tasks such as modeling machine learning and developing data products. With the increasing diversification and complexity of Data sources, as well as the rapid growth of data volumes, building an efficient Data Pipeline has become crucial for improving work efficiency and solving complex problems. This paper focuses on exploring how to optimize data flow through automated machine learning methods by integrating AutoML with Data Pipeline. We will discuss how to leverage AutoML technology to enhance the intelligence of Data Pipeline, thereby achieving better results in machine learning tasks. By delving into the automation and optimization of Data flows, we uncover key strategies for constructing efficient data pipelines that can adapt to the ever-changing data landscape. This not only accelerates the modeling process but also provides innovative solutions to complex problems, enabling more significant outcomes in increasingly intricate data domains. Keywords- Data Pipeline Training;AutoML; Data environment; Machine learning
|
[
"['Jiang Wu' 'Hongbo Wang' 'Chunhe Ni' 'Chenwei Zhang' 'Wenran Lu']"
] |
null | null |
2402.12921
| null | null |
http://arxiv.org/pdf/2402.12921v3
|
2024-06-19T07:20:54Z
|
2024-02-20T11:15:13Z
|
Right on Time: Revising Time Series Models by Constraining their
Explanations
|
The reliability of deep time series models is often compromised by their tendency to rely on confounding factors, which may lead to incorrect outputs. Our newly recorded, naturally confounded dataset named P2S from a real mechanical production line emphasizes this. To avoid "Clever-Hans" moments in time series, i.e., to mitigate confounders, we introduce the method Right on Time (RioT). RioT enables, for the first time, interactions with model explanations across both the time and frequency domain. Feedback on explanations in both domains is then used to constrain the model, steering it away from the annotated confounding factors. The dual-domain interaction strategy is crucial for effectively addressing confounders in time series datasets. We empirically demonstrate that RioT can effectively guide models away from the wrong reasons in P2S as well as popular time series classification and forecasting datasets.
|
[
"['Maurice Kraus' 'David Steinmann' 'Antonia Wüst' 'Andre Kokozinski'\n 'Kristian Kersting']"
] |
null | null |
2402.12930
| null | null |
http://arxiv.org/pdf/2402.12930v1
|
2024-02-20T11:29:57Z
|
2024-02-20T11:29:57Z
|
Learning Exceptional Subgroups by End-to-End Maximizing KL-divergence
|
Finding and describing sub-populations that are exceptional regarding a target property has important applications in many scientific disciplines, from identifying disadvantaged demographic groups in census data to finding conductive molecules within gold nanoparticles. Current approaches to finding such subgroups require pre-discretized predictive variables, do not permit non-trivial target distributions, do not scale to large datasets, and struggle to find diverse results. To address these limitations, we propose Syflow, an end-to-end optimizable approach in which we leverage normalizing flows to model arbitrary target distributions, and introduce a novel neural layer that results in easily interpretable subgroup descriptions. We demonstrate on synthetic and real-world data, including a case study, that Syflow reliably finds highly exceptional subgroups accompanied by insightful descriptions.
|
[
"['Sascha Xu' 'Nils Philipp Walter' 'Janis Kalofolias' 'Jilles Vreeken']"
] |
null | null |
2402.12937
| null | null |
http://arxiv.org/pdf/2402.12937v1
|
2024-02-20T11:38:52Z
|
2024-02-20T11:38:52Z
|
GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural
Networks
|
We address the growing apprehension that GNNs, in the absence of fairness constraints, might produce biased decisions that disproportionately affect underprivileged groups or individuals. Departing from previous work, we introduce for the first time a method for incorporating the Gini coefficient as a measure of fairness to be used within the GNN framework. Our proposal, GRAPHGINI, works with the two different goals of individual and group fairness in a single system, while maintaining high prediction accuracy. GRAPHGINI enforces individual fairness through learnable attention scores that help in aggregating more information through similar nodes. A heuristic-based maximum Nash social welfare constraint ensures the maximum possible group fairness. Both the individual fairness constraint and the group fairness constraint are stated in terms of a differentiable approximation of the Gini coefficient. This approximation is a contribution that is likely to be of interest even beyond the scope of the problem studied in this paper. Unlike other state-of-the-art, GRAPHGINI automatically balances all three optimization objectives (utility, individual, and group fairness) of the GNN and is free from any manual tuning of weight parameters. Extensive experimentation on real-world datasets showcases the efficacy of GRAPHGINI in making significant improvements in individual fairness compared to all currently available state-of-the-art methods while maintaining utility and group equality.
|
[
"['Anuj Kumar Sirohi' 'Anjali Gupta' 'Sayan Ranu' 'Sandeep Kumar'\n 'Amitabha Bagchi']"
] |
null | null |
2402.12939
| null | null |
http://arxiv.org/pdf/2402.12939v1
|
2024-02-20T11:50:50Z
|
2024-02-20T11:50:50Z
|
Discovering Behavioral Modes in Deep Reinforcement Learning Policies
Using Trajectory Clustering in Latent Space
|
Understanding the behavior of deep reinforcement learning (DRL) agents is crucial for improving their performance and reliability. However, the complexity of their policies often makes them challenging to understand. In this paper, we introduce a new approach for investigating the behavior modes of DRL policies, which involves utilizing dimensionality reduction and trajectory clustering in the latent space of neural networks. Specifically, we use Pairwise Controlled Manifold Approximation Projection (PaCMAP) for dimensionality reduction and TRACLUS for trajectory clustering to analyze the latent space of a DRL policy trained on the Mountain Car control task. Our methodology helps identify diverse behavior patterns and suboptimal choices by the policy, thus allowing for targeted improvements. We demonstrate how our approach, combined with domain knowledge, can enhance a policy's performance in specific regions of the state space.
|
[
"['Sindre Benjamin Remman' 'Anastasios M. Lekkas']"
] |
null | null |
2402.12945
| null | null |
http://arxiv.org/pdf/2402.12945v1
|
2024-02-20T12:00:25Z
|
2024-02-20T12:00:25Z
|
Stochastic Approximation Approach to Federated Machine Learning
|
This paper examines Federated learning (FL) in a Stochastic Approximation (SA) framework. FL is a collaborative way to train neural network models across various participants or clients without centralizing their data. Each client will train a model on their respective data and send the weights across to a the server periodically for aggregation. The server aggregates these weights which are then used by the clients to re-initialize their neural network and continue the training. SA is an iterative algorithm that uses approximate sample gradients and tapering step size to locate a minimizer of a cost function. In this paper the clients use a stochastic approximation iterate to update the weights of its neural network. It is shown that the aggregated weights track an autonomous ODE. Numerical simulations are performed and the results are compared with standard algorithms like FedAvg and FedProx. It is observed that the proposed algorithm is robust and gives more reliable estimates of the weights, in particular when the clients data are not identically distributed.
|
[
"['Srihari P V' 'Bharath Bhikkaji']"
] |
null | null |
2402.12954
| null | null |
http://arxiv.org/pdf/2402.12954v1
|
2024-02-20T12:17:01Z
|
2024-02-20T12:17:01Z
|
Conditional Logical Message Passing Transformer for Complex Query
Answering
|
Complex Query Answering (CQA) over Knowledge Graphs (KGs) is a challenging task. Given that KGs are usually incomplete, neural models are proposed to solve CQA by performing multi-hop logical reasoning. However, most of them cannot perform well on both one-hop and multi-hop queries simultaneously. Recent work proposes a logical message passing mechanism based on the pre-trained neural link predictors. While effective on both one-hop and multi-hop queries, it ignores the difference between the constant and variable nodes in a query graph. In addition, during the node embedding update stage, this mechanism cannot dynamically measure the importance of different messages, and whether it can capture the implicit logical dependencies related to a node and received messages remains unclear. In this paper, we propose Conditional Logical Message Passing Transformer (CLMPT), which considers the difference between constants and variables in the case of using pre-trained neural link predictors and performs message passing conditionally on the node type. We empirically verified that this approach can reduce computational costs without affecting performance. Furthermore, CLMPT uses the transformer to aggregate received messages and update the corresponding node embedding. Through the self-attention mechanism, CLMPT can assign adaptive weights to elements in an input set consisting of received messages and the corresponding node and explicitly model logical dependencies between various elements. Experimental results show that CLMPT is a new state-of-the-art neural CQA model.
|
[
"['Chongzhi Zhang' 'Zhiping Peng' 'Junhao Zheng' 'Qianli Ma']"
] |
null | null |
2402.12971
| null | null |
http://arxiv.org/pdf/2402.12971v1
|
2024-02-20T12:40:31Z
|
2024-02-20T12:40:31Z
|
How Temporal Unrolling Supports Neural Physics Simulators
|
Unrolling training trajectories over time strongly influences the inference accuracy of neural network-augmented physics simulators. We analyze these effects by studying three variants of training neural networks on discrete ground truth trajectories. In addition to commonly used one-step setups and fully differentiable unrolling, we include a third, less widely used variant: unrolling without temporal gradients. Comparing networks trained with these three modalities makes it possible to disentangle the two dominant effects of unrolling, training distribution shift and long-term gradients. We present a detailed study across physical systems, network sizes, network architectures, training setups, and test scenarios. It provides an empirical basis for our main findings: A non-differentiable but unrolled training setup supported by a numerical solver can yield 4.5-fold improvements over a fully differentiable prediction setup that does not utilize this solver. We also quantify a difference in the accuracy of models trained in a fully differentiable setup compared to their non-differentiable counterparts. While differentiable setups perform best, the accuracy of unrolling without temporal gradients comes comparatively close. Furthermore, we empirically show that these behaviors are invariant to changes in the underlying physical system, the network architecture and size, and the numerical scheme. These results motivate integrating non-differentiable numerical simulators into training setups even if full differentiability is unavailable. We also observe that the convergence rate of common neural architectures is low compared to numerical algorithms. This encourages the use of hybrid approaches combining neural and numerical algorithms to utilize the benefits of both.
|
[
"['Bjoern List' 'Li-Wei Chen' 'Kartik Bali' 'Nils Thuerey']"
] |
null | null |
2402.12987
| null | null |
http://arxiv.org/pdf/2402.12987v1
|
2024-02-20T13:17:37Z
|
2024-02-20T13:17:37Z
|
Towards Robust Graph Incremental Learning on Evolving Graphs
|
Incremental learning is a machine learning approach that involves training a model on a sequence of tasks, rather than all tasks at once. This ability to learn incrementally from a stream of tasks is crucial for many real-world applications. However, incremental learning is a challenging problem on graph-structured data, as many graph-related problems involve prediction tasks for each individual node, known as Node-wise Graph Incremental Learning (NGIL). This introduces non-independent and non-identically distributed characteristics in the sample data generation process, making it difficult to maintain the performance of the model as new tasks are added. In this paper, we focus on the inductive NGIL problem, which accounts for the evolution of graph structure (structural shift) induced by emerging tasks. We provide a formal formulation and analysis of the problem, and propose a novel regularization-based technique called Structural-Shift-Risk-Mitigation (SSRM) to mitigate the impact of the structural shift on catastrophic forgetting of the inductive NGIL problem. We show that the structural shift can lead to a shift in the input distribution for the existing tasks, and further lead to an increased risk of catastrophic forgetting. Through comprehensive empirical studies with several benchmark datasets, we demonstrate that our proposed method, Structural-Shift-Risk-Mitigation (SSRM), is flexible and easy to adapt to improve the performance of state-of-the-art GNN incremental learning frameworks in the inductive setting.
|
[
"['Junwei Su' 'Difan Zou' 'Zijun Zhang' 'Chuan Wu']"
] |
null | null |
2402.12991
| null | null |
http://arxiv.org/pdf/2402.12991v2
|
2024-06-06T17:46:48Z
|
2024-02-20T13:20:39Z
|
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box
Identification
|
Large Language Model (LLM) services and models often come with legal rules on who can use them and how they must use them. Assessing the compliance of the released LLMs is crucial, as these rules protect the interests of the LLM contributor and prevent misuse. In this context, we describe the novel fingerprinting problem of Black-box Identity Verification (BBIV). The goal is to determine whether a third-party application uses a certain LLM through its chat function. We propose a method called Targeted Random Adversarial Prompt (TRAP) that identifies the specific LLM in use. We repurpose adversarial suffixes, originally proposed for jailbreaking, to get a pre-defined answer from the target LLM, while other models give random answers. TRAP detects the target LLMs with over 95% true positive rate at under 0.2% false positive rate even after a single interaction. TRAP remains effective even if the LLM has minor changes that do not significantly alter the original function.
|
[
"['Martin Gubri' 'Dennis Ulmer' 'Hwaran Lee' 'Sangdoo Yun' 'Seong Joon Oh']"
] |
null | null |
2402.12993
| null | null |
http://arxiv.org/pdf/2402.12993v1
|
2024-02-20T13:21:46Z
|
2024-02-20T13:21:46Z
|
An Autonomous Large Language Model Agent for Chemical Literature Data
Mining
|
Chemical synthesis, which is crucial for advancing material synthesis and drug discovery, impacts various sectors including environmental science and healthcare. The rise of technology in chemistry has generated extensive chemical data, challenging researchers to discern patterns and refine synthesis processes. Artificial intelligence (AI) helps by analyzing data to optimize synthesis and increase yields. However, AI faces challenges in processing literature data due to the unstructured format and diverse writing style of chemical literature. To overcome these difficulties, we introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature. This AI agent employs large language models (LLMs) for prompt generation and iterative optimization. It functions as a chemistry assistant, automating data collection and analysis, thereby saving manpower and enhancing performance. Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data, and we compared our method with human experts in terms of content correctness and time efficiency. The proposed approach marks a significant advancement in automating chemical literature extraction and demonstrates the potential for AI to revolutionize data management and utilization in chemistry.
|
[
"['Kexin Chen' 'Hanqun Cao' 'Junyou Li' 'Yuyang Du' 'Menghao Guo'\n 'Xin Zeng' 'Lanqing Li' 'Jiezhong Qiu' 'Pheng Ann Heng' 'Guangyong Chen']"
] |
null | null |
2402.13001
| null | null |
http://arxiv.org/pdf/2402.13001v2
|
2024-02-21T07:10:06Z
|
2024-02-20T13:32:00Z
|
A unifying primary framework for quantum graph neural networks from
quantum graph states
|
Graph states are used to represent mathematical graphs as quantum states on quantum computers. They can be formulated through stabilizer codes or directly quantum gates and quantum states. In this paper we show that a quantum graph neural network model can be understood and realized based on graph states. We show that they can be used either as a parameterized quantum circuits to represent neural networks or as an underlying structure to construct graph neural networks on quantum computers.
|
[
"['Ammar Daskin']"
] |
null | null |
2402.13005
| null | null |
http://arxiv.org/pdf/2402.13005v3
|
2024-03-08T09:21:14Z
|
2024-02-20T13:38:04Z
|
SzCORE: A Seizure Community Open-source Research Evaluation framework
for the validation of EEG-based automated seizure detection algorithms
|
The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine-learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open-source Research Evaluation) framework and benchmark are made publicly available along with an open-source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.
|
[
"['Jonathan Dan' 'Una Pale' 'Alireza Amirshahi' 'William Cappelletti'\n 'Thorir Mar Ingolfsson' 'Xiaying Wang' 'Andrea Cossettini'\n 'Adriano Bernini' 'Luca Benini' 'Sándor Beniczky' 'David Atienza'\n 'Philippe Ryvlin']"
] |
null | null |
2402.13006
| null | null |
http://arxiv.org/pdf/2402.13006v2
|
2024-06-04T11:18:03Z
|
2024-02-20T13:41:21Z
|
Investigating the Impact of Model Instability on Explanations and
Uncertainty
|
Explainable AI methods facilitate the understanding of model behaviour, yet, small, imperceptible perturbations to inputs can vastly distort explanations. As these explanations are typically evaluated holistically, before model deployment, it is difficult to assess when a particular explanation is trustworthy. Some studies have tried to create confidence estimators for explanations, but none have investigated an existing link between uncertainty and explanation quality. We artificially simulate epistemic uncertainty in text input by introducing noise at inference time. In this large-scale empirical study, we insert different levels of noise perturbations and measure the effect on the output of pre-trained language models and different uncertainty metrics. Realistic perturbations have minimal effect on performance and explanations, yet masking has a drastic effect. We find that high uncertainty doesn't necessarily imply low explanation plausibility; the correlation between the two metrics can be moderately positive when noise is exposed during the training process. This suggests that noise-augmented models may be better at identifying salient tokens when uncertain. Furthermore, when predictive and epistemic uncertainty measures are over-confident, the robustness of a saliency map to perturbation can indicate model stability issues. Integrated Gradients shows the overall greatest robustness to perturbation, while still showing model-specific patterns in performance; however, this phenomenon is limited to smaller Transformer-based language models.
|
[
"['Sara Vera Marjanović' 'Isabelle Augenstein' 'Christina Lioma']"
] |
null | null |
2402.13007
| null | null |
http://arxiv.org/pdf/2402.13007v1
|
2024-02-20T13:42:36Z
|
2024-02-20T13:42:36Z
|
Improve Cross-Architecture Generalization on Dataset Distillation
|
Dataset distillation, a pragmatic approach in machine learning, aims to create a smaller synthetic dataset from a larger existing dataset. However, existing distillation methods primarily adopt a model-based paradigm, where the synthetic dataset inherits model-specific biases, limiting its generalizability to alternative models. In response to this constraint, we propose a novel methodology termed "model pool". This approach involves selecting models from a diverse model pool based on a specific probability distribution during the data distillation process. Additionally, we integrate our model pool with the established knowledge distillation approach and apply knowledge distillation to the test process of the distilled dataset. Our experimental results validate the effectiveness of the model pool approach across a range of existing models while testing, demonstrating superior performance compared to existing methodologies.
|
[
"['Binglin Zhou' 'Linhao Zhong' 'Wentao Chen']"
] |
null | null |
2402.13019
| null | null |
http://arxiv.org/pdf/2402.13019v1
|
2024-02-20T14:01:26Z
|
2024-02-20T14:01:26Z
|
Improving Neural-based Classification with Logical Background Knowledge
|
Neurosymbolic AI is a growing field of research aiming to combine neural networks learning capabilities with the reasoning abilities of symbolic systems. This hybridization can take many shapes. In this paper, we propose a new formalism for supervised multi-label classification with propositional background knowledge. We introduce a new neurosymbolic technique called semantic conditioning at inference, which only constrains the system during inference while leaving the training unaffected. We discuss its theoritical and practical advantages over two other popular neurosymbolic techniques: semantic conditioning and semantic regularization. We develop a new multi-scale methodology to evaluate how the benefits of a neurosymbolic technique evolve with the scale of the network. We then evaluate experimentally and compare the benefits of all three techniques across model scales on several datasets. Our results demonstrate that semantic conditioning at inference can be used to build more accurate neural-based systems with fewer resources while guaranteeing the semantic consistency of outputs.
|
[
"['Arthur Ledaguenel' 'Céline Hudelot' 'Mostepha Khouadjia']"
] |
null | null |
2402.13033
| null | null |
http://arxiv.org/pdf/2402.13033v1
|
2024-02-20T14:18:43Z
|
2024-02-20T14:18:43Z
|
Enhancing Real-World Complex Network Representations with Hyperedge
Augmentation
|
Graph augmentation methods play a crucial role in improving the performance and enhancing generalisation capabilities in Graph Neural Networks (GNNs). Existing graph augmentation methods mainly perturb the graph structures and are usually limited to pairwise node relations. These methods cannot fully address the complexities of real-world large-scale networks that often involve higher-order node relations beyond only being pairwise. Meanwhile, real-world graph datasets are predominantly modelled as simple graphs, due to the scarcity of data that can be used to form higher-order edges. Therefore, reconfiguring the higher-order edges as an integration into graph augmentation strategies lights up a promising research path to address the aforementioned issues. In this paper, we present Hyperedge Augmentation (HyperAug), a novel graph augmentation method that constructs virtual hyperedges directly form the raw data, and produces auxiliary node features by extracting from the virtual hyperedge information, which are used for enhancing GNN performances on downstream tasks. We design three diverse virtual hyperedge construction strategies to accompany the augmentation scheme: (1) via graph statistics, (2) from multiple data perspectives, and (3) utilising multi-modality. Furthermore, to facilitate HyperAug evaluation, we provide 23 novel real-world graph datasets across various domains including social media, biology, and e-commerce. Our empirical study shows that HyperAug consistently and significantly outperforms GNN baselines and other graph augmentation methods, across a variety of application contexts, which clearly indicates that it can effectively incorporate higher-order node relations into graph augmentation methods for real-world complex networks.
|
[
"['Xiangyu Zhao' 'Zehui Li' 'Mingzhu Shen' 'Guy-Bart Stan' 'Pietro Liò'\n 'Yiren Zhao']"
] |
null | null |
2402.13037
| null | null |
http://arxiv.org/pdf/2402.13037v1
|
2024-02-20T14:24:00Z
|
2024-02-20T14:24:00Z
|
Align Your Intents: Offline Imitation Learning via Optimal Transport
|
Offline reinforcement learning (RL) addresses the problem of sequential decision-making by learning optimal policy through pre-collected data, without interacting with the environment. As yet, it has remained somewhat impractical, because one rarely knows the reward explicitly and it is hard to distill it retrospectively. Here, we show that an imitating agent can still learn the desired behavior merely from observing the expert, despite the absence of explicit rewards or action labels. In our method, AILOT (Aligned Imitation Learning via Optimal Transport), we involve special representation of states in a form of intents that incorporate pairwise spatial distances within the data. Given such representations, we define intrinsic reward function via optimal transport distance between the expert's and the agent's trajectories. We report that AILOT outperforms state-of-the art offline imitation learning algorithms on D4RL benchmarks and improves the performance of other offline RL algorithms in the sparse-reward tasks.
|
[
"['Maksim Bobrin' 'Nazar Buzun' 'Dmitrii Krylov' 'Dmitry V. Dylov']"
] |
null | null |
2402.13040
| null | null |
http://arxiv.org/pdf/2402.13040v1
|
2024-02-20T14:29:02Z
|
2024-02-20T14:29:02Z
|
Text-Guided Molecule Generation with Diffusion Language Model
|
Text-guided molecule generation is a task where molecules are generated to match specific textual descriptions. Recently, most existing SMILES-based molecule generation methods rely on an autoregressive architecture. In this work, we propose the Text-Guided Molecule Generation with Diffusion Language Model (TGM-DLM), a novel approach that leverages diffusion models to address the limitations of autoregressive methods. TGM-DLM updates token embeddings within the SMILES string collectively and iteratively, using a two-phase diffusion generation process. The first phase optimizes embeddings from random noise, guided by the text description, while the second phase corrects invalid SMILES strings to form valid molecular representations. We demonstrate that TGM-DLM outperforms MolT5-Base, an autoregressive model, without the need for additional data resources. Our findings underscore the remarkable effectiveness of TGM-DLM in generating coherent and precise molecules with specific properties, opening new avenues in drug discovery and related scientific domains. Code will be released at: https://github.com/Deno-V/tgm-dlm.
|
[
"['Haisong Gong' 'Qiang Liu' 'Shu Wu' 'Liang Wang']"
] |
null | null |
2402.13076
| null | null |
http://arxiv.org/pdf/2402.13076v1
|
2024-02-20T15:22:25Z
|
2024-02-20T15:22:25Z
|
Not All Weights Are Created Equal: Enhancing Energy Efficiency in
On-Device Streaming Speech Recognition
|
Power consumption plays an important role in on-device streaming speech recognition, as it has a direct impact on the user experience. This study delves into how weight parameters in speech recognition models influence the overall power consumption of these models. We discovered that the impact of weight parameters on power consumption varies, influenced by factors including how often they are invoked and their placement in memory. Armed with this insight, we developed design guidelines aimed at optimizing on-device speech recognition models. These guidelines focus on minimizing power use without substantially affecting accuracy. Our method, which employs targeted compression based on the varying sensitivities of weight parameters, demonstrates superior performance compared to state-of-the-art compression methods. It achieves a reduction in energy usage of up to 47% while maintaining similar model accuracy and improving the real-time factor.
|
[
"['Yang Li' 'Yuan Shangguan' 'Yuhao Wang' 'Liangzhen Lai' 'Ernie Chang'\n 'Changsheng Zhao' 'Yangyang Shi' 'Vikas Chandra']"
] |
null | null |
2402.13077
| null | null |
http://arxiv.org/pdf/2402.13077v1
|
2024-02-20T15:23:24Z
|
2024-02-20T15:23:24Z
|
Mechanistic Neural Networks for Scientific Machine Learning
|
This paper presents Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations, revealing the underlying dynamics of data and enhancing interpretability and efficiency in data modeling. Central to our approach is a novel Relaxed Linear Programming Solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs. This integrates well with neural networks and surpasses the limitations of traditional ODE solvers enabling scalable GPU parallel processing. Overall, Mechanistic Neural Networks demonstrate their versatility for scientific machine learning applications, adeptly managing tasks from equation discovery to dynamic systems modeling. We prove their comprehensive capabilities in analyzing and interpreting complex scientific data across various applications, showing significant performance against specialized state-of-the-art methods.
|
[
"['Adeel Pervez' 'Francesco Locatello' 'Efstratios Gavves']"
] |
null | null |
2402.13079
| null | null |
http://arxiv.org/pdf/2402.13079v1
|
2024-02-20T15:24:21Z
|
2024-02-20T15:24:21Z
|
Mode Estimation with Partial Feedback
|
The combination of lightly supervised pre-training and online fine-tuning has played a key role in recent AI developments. These new learning pipelines call for new theoretical frameworks. In this paper, we formalize core aspects of weakly supervised and active learning with a simple problem: the estimation of the mode of a distribution using partial feedback. We show how entropy coding allows for optimal information acquisition from partial feedback, develop coarse sufficient statistics for mode identification, and adapt bandit algorithms to our new setting. Finally, we combine those contributions into a statistically and computationally efficient solution to our problem.
|
[
"['Charles Arnal' 'Vivien Cabannes' 'Vianney Perchet']"
] |
null | null |
2402.13081
| null | null |
http://arxiv.org/pdf/2402.13081v1
|
2024-02-20T15:25:56Z
|
2024-02-20T15:25:56Z
|
IT Intrusion Detection Using Statistical Learning and Testbed
Measurements
|
We study automated intrusion detection in an IT infrastructure, specifically the problem of identifying the start of an attack, the type of attack, and the sequence of actions an attacker takes, based on continuous measurements from the infrastructure. We apply statistical learning methods, including Hidden Markov Model (HMM), Long Short-Term Memory (LSTM), and Random Forest Classifier (RFC) to map sequences of observations to sequences of predicted attack actions. In contrast to most related research, we have abundant data to train the models and evaluate their predictive power. The data comes from traces we generate on an in-house testbed where we run attacks against an emulated IT infrastructure. Central to our work is a machine-learning pipeline that maps measurements from a high-dimensional observation space to a space of low dimensionality or to a small set of observation symbols. Investigating intrusions in offline as well as online scenarios, we find that both HMM and LSTM can be effective in predicting attack start time, attack type, and attack actions. If sufficient training data is available, LSTM achieves higher prediction accuracy than HMM. HMM, on the other hand, requires less computational resources and less training data for effective prediction. Also, we find that the methods we study benefit from data produced by traditional intrusion detection systems like SNORT.
|
[
"['Xiaoxuan Wang' 'Rolf Stadler']"
] |
null | null |
2402.13087
| null | null |
http://arxiv.org/pdf/2402.13087v2
|
2024-06-04T09:49:34Z
|
2024-02-20T15:29:49Z
|
Revisiting Differentially Private Hyper-parameter Tuning
|
We study the application of differential privacy in hyper-parameter tuning, a crucial process in machine learning involving selecting the best hyper-parameter from several candidates. Unlike many private learning algorithms, including the prevalent DP-SGD, the privacy implications of tuning remain insufficiently understood or often totally ignored. Recent works propose a generic private selection solution for the tuning process, yet a fundamental question persists: is this privacy bound tight? This paper provides an in-depth examination of this question. Initially, we provide studies affirming the current privacy analysis for private selection is indeed tight in general. However, when we specifically study the hyper-parameter tuning problem in a white-box setting, such tightness no longer holds. This is first demonstrated by applying privacy audit on the tuning process. Our findings underscore a substantial gap between current theoretical privacy bound and the empirical bound derived even under strong audit setups. This gap motivates our subsequent investigations. Our further study provides improved privacy results for private hyper-parameter tuning due to its distinct properties. Our results demonstrate broader applicability compared to prior analyses, which are limited to specific parameter configurations.
|
[
"['Zihang Xiang' 'Tianhao Wang' 'Chenglong Wang' 'Di Wang']"
] |
null | null |
2402.13089
| null | null |
http://arxiv.org/pdf/2402.13089v1
|
2024-02-20T15:31:44Z
|
2024-02-20T15:31:44Z
|
Towards an empirical understanding of MoE design choices
|
In this study, we systematically evaluate the impact of common design choices in Mixture of Experts (MoEs) on validation performance, uncovering distinct influences at token and sequence levels. We also present empirical evidence showing comparable performance between a learned router and a frozen, randomly initialized router, suggesting that learned routing may not be essential. Our study further reveals that Sequence-level routing can result in topic-specific weak expert specialization, in contrast to syntax specialization observed with Token-level routing.
|
[
"['Dongyang Fan' 'Bettina Messmer' 'Martin Jaggi']"
] |
null | null |
2402.13101
| null | null |
http://arxiv.org/pdf/2402.13101v1
|
2024-02-20T15:54:24Z
|
2024-02-20T15:54:24Z
|
A Microstructure-based Graph Neural Network for Accelerating Multiscale
Simulations
|
Simulating the mechanical response of advanced materials can be done more accurately using concurrent multiscale models than with single-scale simulations. However, the computational costs stand in the way of the practical application of this approach. The costs originate from microscale Finite Element (FE) models that must be solved at every macroscopic integration point. A plethora of surrogate modeling strategies attempt to alleviate this cost by learning to predict macroscopic stresses from macroscopic strains, completely replacing the microscale models. In this work, we introduce an alternative surrogate modeling strategy that allows for keeping the multiscale nature of the problem, allowing it to be used interchangeably with an FE solver for any time step. Our surrogate provides all microscopic quantities, which are then homogenized to obtain macroscopic quantities of interest. We achieve this for an elasto-plastic material by predicting full-field microscopic strains using a graph neural network (GNN) while retaining the microscopic constitutive material model to obtain the stresses. This hybrid data-physics graph-based approach avoids the high dimensionality originating from predicting full-field responses while allowing non-locality to arise. By training the GNN on a variety of meshes, it learns to generalize to unseen meshes, allowing a single model to be used for a range of microstructures. The embedded microscopic constitutive model in the GNN implicitly tracks history-dependent variables and leads to improved accuracy. We demonstrate for several challenging scenarios that the surrogate can predict complex macroscopic stress-strain paths. As the computation time of our method scales favorably with the number of elements in the microstructure compared to the FE method, our method can significantly accelerate FE2 simulations.
|
[
"['J. Storm' 'I. B. C. M. Rocha' 'F. P. van der Meer']"
] |
null | null |
2402.13103
| null | null |
http://arxiv.org/pdf/2402.13103v1
|
2024-02-20T15:58:45Z
|
2024-02-20T15:58:45Z
|
Multivariate Functional Linear Discriminant Analysis for the
Classification of Short Time Series with Missing Data
|
Functional linear discriminant analysis (FLDA) is a powerful tool that extends LDA-mediated multiclass classification and dimension reduction to univariate time-series functions. However, in the age of large multivariate and incomplete data, statistical dependencies between features must be estimated in a computationally tractable way, while also dealing with missing data. There is a need for a computationally tractable approach that considers the statistical dependencies between features and can handle missing values. We here develop a multivariate version of FLDA (MUDRA) to tackle this issue and describe an efficient expectation/conditional-maximization (ECM) algorithm to infer its parameters. We assess its predictive power on the "Articulary Word Recognition" data set and show its improvement over the state-of-the-art, especially in the case of missing data. MUDRA allows interpretable classification of data sets with large proportions of missing data, which will be particularly useful for medical or psychological data sets.
|
[
"['Rahul Bordoloi' 'Clémence Réda' 'Orell Trautmann' 'Saptarshi Bej'\n 'Olaf Wolkenhauer']"
] |
null | null |
2402.13106
| null | null |
http://arxiv.org/pdf/2402.13106v1
|
2024-02-20T16:01:39Z
|
2024-02-20T16:01:39Z
|
On Generalization Bounds for Deep Compound Gaussian Neural Networks
|
Algorithm unfolding or unrolling is the technique of constructing a deep neural network (DNN) from an iterative algorithm. Unrolled DNNs often provide better interpretability and superior empirical performance over standard DNNs in signal estimation tasks. An important theoretical question, which has only recently received attention, is the development of generalization error bounds for unrolled DNNs. These bounds deliver theoretical and practical insights into the performance of a DNN on empirical datasets that are distinct from, but sampled from, the probability density generating the DNN training data. In this paper, we develop novel generalization error bounds for a class of unrolled DNNs that are informed by a compound Gaussian prior. These compound Gaussian networks have been shown to outperform comparative standard and unfolded deep neural networks in compressive sensing and tomographic imaging problems. The generalization error bound is formulated by bounding the Rademacher complexity of the class of compound Gaussian network estimates with Dudley's integral. Under realistic conditions, we show that, at worst, the generalization error scales $mathcal{O}(nsqrt{ln(n)})$ in the signal dimension and $mathcal{O}(($Network Size$)^{3/2})$ in network size.
|
[
"['Carter Lyons' 'Raghu G. Raj' 'Margaret Cheney']"
] |
null | null |
2402.13108
| null | null |
http://arxiv.org/pdf/2402.13108v1
|
2024-02-20T16:01:42Z
|
2024-02-20T16:01:42Z
|
On the Stability of Gradient Descent for Large Learning Rate
|
There currently is a significant interest in understanding the Edge of Stability (EoS) phenomenon, which has been observed in neural networks training, characterized by a non-monotonic decrease of the loss function over epochs, while the sharpness of the loss (spectral norm of the Hessian) progressively approaches and stabilizes around 2/(learning rate). Reasons for the existence of EoS when training using gradient descent have recently been proposed -- a lack of flat minima near the gradient descent trajectory together with the presence of compact forward-invariant sets. In this paper, we show that linear neural networks optimized under a quadratic loss function satisfy the first assumption and also a necessary condition for the second assumption. More precisely, we prove that the gradient descent map is non-singular, the set of global minimizers of the loss function forms a smooth manifold, and the stable minima form a bounded subset in parameter space. Additionally, we prove that if the step-size is too big, then the set of initializations from which gradient descent converges to a critical point has measure zero.
|
[
"['Alexandru Crăciun' 'Debarghya Ghoshdastidar']"
] |
null | null |
2402.13114
| null | null |
http://arxiv.org/pdf/2402.13114v1
|
2024-02-20T16:11:59Z
|
2024-02-20T16:11:59Z
|
BuffGraph: Enhancing Class-Imbalanced Node Classification via Buffer
Nodes
|
Class imbalance in graph-structured data, where minor classes are significantly underrepresented, poses a critical challenge for Graph Neural Networks (GNNs). To address this challenge, existing studies generally generate new minority nodes and edges connecting new nodes to the original graph to make classes balanced. However, they do not solve the problem that majority classes still propagate information to minority nodes by edges in the original graph which introduces bias towards majority classes. To address this, we introduce BuffGraph, which inserts buffer nodes into the graph, modulating the impact of majority classes to improve minor class representation. Our extensive experiments across diverse real-world datasets empirically demonstrate that BuffGraph outperforms existing baseline methods in class-imbalanced node classification in both natural settings and imbalanced settings. Code is available at https://anonymous.4open.science/r/BuffGraph-730A.
|
[
"['Qian Wang' 'Zemin Liu' 'Zhen Zhang' 'Bingsheng He']"
] |
null | null |
2402.13126
| null | null |
http://arxiv.org/pdf/2402.13126v1
|
2024-02-20T16:39:23Z
|
2024-02-20T16:39:23Z
|
VGMShield: Mitigating Misuse of Video Generative Models
|
With the rapid advancement in video generation, people can conveniently utilize video generation models to create videos tailored to their specific desires. Nevertheless, there are also growing concerns about their potential misuse in creating and disseminating false information. In this work, we introduce VGMShield: a set of three straightforward but pioneering mitigations through the lifecycle of fake video generation. We start from textit{fake video detection} trying to understand whether there is uniqueness in generated videos and whether we can differentiate them from real videos; then, we investigate the textit{tracing} problem, which maps a fake video back to a model that generates it. Towards these, we propose to leverage pre-trained models that focus on {it spatial-temporal dynamics} as the backbone to identify inconsistencies in videos. Through experiments on seven state-of-the-art open-source models, we demonstrate that current models still cannot perfectly handle spatial-temporal relationships, and thus, we can accomplish detection and tracing with nearly perfect accuracy. Furthermore, anticipating future generative model improvements, we propose a {it prevention} method that adds invisible perturbations to images to make the generated videos look unreal. Together with fake video detection and tracing, our multi-faceted set of solutions can effectively mitigate misuse of video generative models.
|
[
"['Yan Pang' 'Yang Zhang' 'Tianhao Wang']"
] |
null | null |
2402.13144
| null | null |
http://arxiv.org/pdf/2402.13144v2
|
2024-05-28T08:44:11Z
|
2024-02-20T16:59:03Z
|
Neural Network Parameter Diffusion
|
Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also textit{generate high-performing neural network parameters}. Our approach is simple, utilizing an autoencoder and a standard latent diffusion model. The autoencoder extracts latent representations of a subset of the trained network parameters. A diffusion model is then trained to synthesize these latent parameter representations from random noise. It then generates new representations that are passed through the autoencoder's decoder, whose outputs are ready to use as new subsets of network parameters. Across various architectures and datasets, our diffusion process consistently generates models of comparable or improved performance over trained networks, with minimal additional cost. Notably, we empirically find that the generated models are not memorizing the trained networks. Our results encourage more exploration on the versatile use of diffusion models.
|
[
"['Kai Wang' 'Zhaopan Xu' 'Yukun Zhou' 'Zelin Zang' 'Trevor Darrell'\n 'Zhuang Liu' 'Yang You']"
] |
null | null |
2402.13147
| null | null |
http://arxiv.org/pdf/2402.13147v2
|
2024-05-23T07:22:40Z
|
2024-02-20T17:02:48Z
|
SPRINQL: Sub-optimal Demonstrations driven Offline Imitation Learning
|
We focus on offline imitation learning (IL), which aims to mimic an expert's behavior using demonstrations without any interaction with the environment. One of the main challenges in offline IL is the limited support of expert demonstrations, which typically cover only a small fraction of the state-action space. While it may not be feasible to obtain numerous expert demonstrations, it is often possible to gather a larger set of sub-optimal demonstrations. For example, in treatment optimization problems, there are varying levels of doctor treatments available for different chronic conditions. These range from treatment specialists and experienced general practitioners to less experienced general practitioners. Similarly, when robots are trained to imitate humans in routine tasks, they might learn from individuals with different levels of expertise and efficiency. In this paper, we propose an offline IL approach that leverages the larger set of sub-optimal demonstrations while effectively mimicking expert trajectories. Existing offline IL methods based on behavior cloning or distribution matching often face issues such as overfitting to the limited set of expert demonstrations or inadvertently imitating sub-optimal trajectories from the larger dataset. Our approach, which is based on inverse soft-Q learning, learns from both expert and sub-optimal demonstrations. It assigns higher importance (through learned weights) to aligning with expert demonstrations and lower importance to aligning with sub-optimal ones. A key contribution of our approach, called SPRINQL, is transforming the offline IL problem into a convex optimization over the space of Q functions. Through comprehensive experimental evaluations, we demonstrate that the SPRINQL algorithm achieves state-of-the-art (SOTA) performance on offline IL benchmarks. Code is available at https://github.com/hmhuy2000/SPRINQL.
|
[
"['Huy Hoang' 'Tien Mai' 'Pradeep Varakantham']"
] |
null | null |
2402.13148
| null | null |
http://arxiv.org/pdf/2402.13148v2
|
2024-07-05T00:03:24Z
|
2024-02-20T17:04:06Z
|
Defending Jailbreak Prompts via In-Context Adversarial Game
|
Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications. However, concerns regarding their security, particularly the vulnerability to jailbreak attacks, persist. Drawing inspiration from adversarial training in deep learning and LLM agent learning processes, we introduce the In-Context Adversarial Game (ICAG) for defending against jailbreaks without the need for fine-tuning. ICAG leverages agent learning to conduct an adversarial game, aiming to dynamically extend knowledge to defend against jailbreaks. Unlike traditional methods that rely on static datasets, ICAG employs an iterative process to enhance both the defense and attack agents. This continuous improvement process strengthens defenses against newly generated jailbreak prompts. Our empirical studies affirm ICAG's efficacy, where LLMs safeguarded by ICAG exhibit significantly reduced jailbreak success rates across various attack scenarios. Moreover, ICAG demonstrates remarkable transferability to other LLMs, indicating its potential as a versatile defense mechanism.
|
[
"['Yujun Zhou' 'Yufei Han' 'Haomin Zhuang' 'Kehan Guo' 'Zhenwen Liang'\n 'Hongyan Bao' 'Xiangliang Zhang']"
] |
null | null |
2402.13181
| null | null |
http://arxiv.org/pdf/2402.13181v1
|
2024-02-20T17:48:11Z
|
2024-02-20T17:48:11Z
|
DINOBot: Robot Manipulation via Retrieval and Alignment with Vision
Foundation Models
|
We propose DINOBot, a novel imitation learning framework for robot manipulation, which leverages the image-level and pixel-level capabilities of features extracted from Vision Transformers trained with DINO. When interacting with a novel object, DINOBot first uses these features to retrieve the most visually similar object experienced during human demonstrations, and then uses this object to align its end-effector with the novel object to enable effective interaction. Through a series of real-world experiments on everyday tasks, we show that exploiting both the image-level and pixel-level properties of vision foundation models enables unprecedented learning efficiency and generalisation. Videos and code are available at https://www.robot-learning.uk/dinobot.
|
[
"['Norman Di Palo' 'Edward Johns']"
] |
null | null |
2402.13182
| null | null |
http://arxiv.org/pdf/2402.13182v1
|
2024-02-20T17:49:10Z
|
2024-02-20T17:49:10Z
|
Order-Optimal Regret in Distributed Kernel Bandits using Uniform
Sampling with Shared Randomness
|
We consider distributed kernel bandits where $N$ agents aim to collaboratively maximize an unknown reward function that lies in a reproducing kernel Hilbert space. Each agent sequentially queries the function to obtain noisy observations at the query points. Agents can share information through a central server, with the objective of minimizing regret that is accumulating over time $T$ and aggregating over agents. We develop the first algorithm that achieves the optimal regret order (as defined by centralized learning) with a communication cost that is sublinear in both $N$ and $T$. The key features of the proposed algorithm are the uniform exploration at the local agents and shared randomness with the central server. Working together with the sparse approximation of the GP model, these two key components make it possible to preserve the learning rate of the centralized setting at a diminishing rate of communication.
|
[
"['Nikola Pavlovic' 'Sudeep Salgia' 'Qing Zhao']"
] |
null | null |
2402.13187
| null | null |
http://arxiv.org/pdf/2402.13187v2
|
2024-06-21T17:27:22Z
|
2024-02-20T17:53:24Z
|
Testing Calibration in Nearly-Linear Time
|
In the recent literature on machine learning and decision making, calibration has emerged as a desirable and widely-studied statistical property of the outputs of binary prediction models. However, the algorithmic aspects of measuring model calibration have remained relatively less well-explored. Motivated by [BGHN23], which proposed a rigorous framework for measuring distances to calibration, we initiate the algorithmic study of calibration through the lens of property testing. We define the problem of calibration testing from samples where given $n$ draws from a distribution $mathcal{D}$ on $(predictions, binary outcomes)$, our goal is to distinguish between the case where $mathcal{D}$ is perfectly calibrated, and the case where $mathcal{D}$ is $varepsilon$-far from calibration. We make the simple observation that the empirical smooth calibration linear program can be reformulated as an instance of minimum-cost flow on a highly-structured graph, and design an exact dynamic programming-based solver for it which runs in time $O(nlog^2(n))$, and solves the calibration testing problem information-theoretically optimally in the same time. This improves upon state-of-the-art black-box linear program solvers requiring $Omega(n^omega)$ time, where $omega > 2$ is the exponent of matrix multiplication. We also develop algorithms for tolerant variants of our testing problem improving upon black-box linear program solvers, and give sample complexity lower bounds for alternative calibration measures to the one considered in this work. Finally, we present experiments showing the testing problem we define faithfully captures standard notions of calibration, and that our algorithms scale efficiently to accommodate large sample sizes.
|
[
"['Lunjia Hu' 'Arun Jambulapati' 'Kevin Tian' 'Chutong Yang']"
] |
null | null |
2402.13196
| null | null |
http://arxiv.org/pdf/2402.13196v1
|
2024-02-20T18:07:59Z
|
2024-02-20T18:07:59Z
|
Practical Kernel Tests of Conditional Independence
|
We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing, absent in tests of unconditional independence, is to obtain the correct test level (the specified upper bound on the rate of false positives), while still attaining competitive test power. Excess false positives arise due to bias in the test statistic, which is obtained using nonparametric kernel ridge regression. We propose three methods for bias control to correct the test level, based on data splitting, auxiliary data, and (where possible) simpler function classes. We show these combined strategies are effective both for synthetic and real-world data.
|
[
"['Roman Pogodin' 'Antonin Schrab' 'Yazhe Li' 'Danica J. Sutherland'\n 'Arthur Gretton']"
] |
null | null |
2402.13201
| null | null |
http://arxiv.org/pdf/2402.13201v1
|
2024-02-20T18:10:39Z
|
2024-02-20T18:10:39Z
|
Tiny Reinforcement Learning for Quadruped Locomotion using Decision
Transformers
|
Resource-constrained robotic platforms are particularly useful for tasks that require low-cost hardware alternatives due to the risk of losing the robot, like in search-and-rescue applications, or the need for a large number of devices, like in swarm robotics. For this reason, it is crucial to find mechanisms for adapting reinforcement learning techniques to the constraints imposed by lower computational power and smaller memory capacities of these ultra low-cost robotic platforms. We try to address this need by proposing a method for making imitation learning deployable onto resource-constrained robotic platforms. Here we cast the imitation learning problem as a conditional sequence modeling task and we train a decision transformer using expert demonstrations augmented with a custom reward. Then, we compress the resulting generative model using software optimization schemes, including quantization and pruning. We test our method in simulation using Isaac Gym, a realistic physics simulation environment designed for reinforcement learning. We empirically demonstrate that our method achieves natural looking gaits for Bittle, a resource-constrained quadruped robot. We also run multiple simulations to show the effects of pruning and quantization on the performance of the model. Our results show that quantization (down to 4 bits) and pruning reduce model size by around 30% while maintaining a competitive reward, making the model deployable in a resource-constrained system.
|
[
"['Orhan Eren Akgün' 'Néstor Cuevas' 'Matheus Farias' 'Daniel Garces']"
] |
null | null |
2402.13204
| null | null |
http://arxiv.org/pdf/2402.13204v1
|
2024-02-20T18:15:11Z
|
2024-02-20T18:15:11Z
|
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural
Architecture Search
|
Recent advancements in Artificial Intelligence (AI), driven by Neural Networks (NN), demand innovative neural architecture designs, particularly within the constrained environments of Internet of Things (IoT) systems, to balance performance and efficiency. HW-aware Neural Architecture Search (HW-aware NAS) emerges as an attractive strategy to automate the design of NN using multi-objective optimization approaches, such as evolutionary algorithms. However, the intricate relationship between NN design parameters and HW-aware NAS optimization objectives remains an underexplored research area, overlooking opportunities to effectively leverage this knowledge to guide the search process accordingly. Furthermore, the large amount of evaluation data produced during the search holds untapped potential for refining the optimization strategy and improving the approximation of the Pareto front. Addressing these issues, we propose SONATA, a self-adaptive evolutionary algorithm for HW-aware NAS. Our method leverages adaptive evolutionary operators guided by the learned importance of NN design parameters. Specifically, through tree-based surrogate models and a Reinforcement Learning agent, we aspire to gather knowledge on 'How' and 'When' to evolve NN architectures. Comprehensive evaluations across various NAS search spaces and hardware devices on the ImageNet-1k dataset have shown the merit of SONATA with up to 0.25% improvement in accuracy and up to 2.42x gains in latency and energy. Our SONATA has seen up to sim$93.6% Pareto dominance over the native NSGA-II, further stipulating the importance of self-adaptive evolution operators in HW-aware NAS.
|
[
"['Halima Bouzidi' 'Smail Niar' 'Hamza Ouarnoughi' 'El-Ghazali Talbi']"
] |
null | null |
2402.13210
| null | null |
http://arxiv.org/pdf/2402.13210v2
|
2024-07-03T00:23:41Z
|
2024-02-20T18:20:59Z
|
Bayesian Reward Models for LLM Alignment
|
To ensure that large language model (LLM) responses are helpful and non-toxic, a reward model trained on human preference data is usually used. LLM responses with high rewards are then selected through best-of-$n$ (BoN) sampling or the LLM is further optimized to produce responses with high rewards through reinforcement learning from human feedback (RLHF). However, these processes are susceptible to reward overoptimization or `hacking', where responses receive high rewards due to imperfections in the reward model rather than true preference, particularly as prompts or responses deviate from the training data. To address these challenges, we propose to train a Bayesian reward model, which signals higher uncertainty further from the training data distribution. We trained Bayesian reward models using Laplace approximation on LoRA weights, and found that the resulting uncertainty estimates can effectively mitigate reward overoptimization in BoN sampling.
|
[
"['Adam X. Yang' 'Maxime Robeyns' 'Thomas Coste' 'Zhengyan Shi' 'Jun Wang'\n 'Haitham Bou-Ammar' 'Laurence Aitchison']"
] |
null | null |
2402.13212
| null | null |
http://arxiv.org/pdf/2402.13212v2
|
2024-06-05T19:50:19Z
|
2024-02-20T18:22:38Z
|
Soft Self-Consistency Improves Language Model Agents
|
Generations from large language models (LLMs) can be improved by sampling and scoring multiple solutions to select a final answer. Current "sample and select" methods such as self-consistency (SC) rely on majority voting to score answers. However, when tasks have many distinct and valid answers, selection by voting requires a large number of samples. This makes SC prohibitively expensive for interactive tasks that involve generating multiple actions (answers) sequentially. After establishing that majority voting fails to provide consistent gains on such tasks, we demonstrate how to increase success rates by softening the scoring criterion. We introduce Soft Self-Consistency (SOFT-SC), which replaces SC's discontinuous scoring with a continuous score computed from model likelihoods, allowing for selection even when actions are sparsely distributed. SOFT-SC improves both performance and efficiency on long-horizon interactive tasks, requiring half as many samples as SC for comparable or better performance. For a fixed number of samples, SOFT-SC leads to a 1.3% increase over SC in absolute success rate on writing bash programs, a 6.6% increase on online shopping (WebShop), and a 4.7% increase for an interactive household game (ALFWorld). Finally, we show that SOFT-SC can be applied to both open-source and black-box models.
|
[
"['Han Wang' 'Archiki Prasad' 'Elias Stengel-Eskin' 'Mohit Bansal']"
] |
null | null |
2402.13213
| null | null |
http://arxiv.org/pdf/2402.13213v1
|
2024-02-20T18:24:47Z
|
2024-02-20T18:24:47Z
|
Softmax Probabilities (Mostly) Predict Large Language Model Correctness
on Multiple-Choice Q&A
|
Although large language models (LLMs) perform impressively on many tasks, overconfidence remains a problem. We hypothesized that on multiple-choice Q&A tasks, wrong answers would be associated with smaller maximum softmax probabilities (MSPs) compared to correct answers. We comprehensively evaluate this hypothesis on ten open-source LLMs and five datasets, and find strong evidence for our hypothesis among models which perform well on the original Q&A task. For the six LLMs with the best Q&A performance, the AUROC derived from the MSP was better than random chance with p < 10^{-4} in 59/60 instances. Among those six LLMs, the average AUROC ranged from 60% to 69%. Leveraging these findings, we propose a multiple-choice Q&A task with an option to abstain and show that performance can be improved by selectively abstaining based on the MSP of the initial model response. We also run the same experiments with pre-softmax logits instead of softmax probabilities and find similar (but not identical) results.
|
[
"['Benjamin Plaut' 'Khanh Nguyen' 'Tu Trinh']"
] |
null | null |
2402.13219
| null | null |
http://arxiv.org/pdf/2402.13219v1
|
2024-02-20T18:31:27Z
|
2024-02-20T18:31:27Z
|
Analyzing Operator States and the Impact of AI-Enhanced Decision Support
in Control Rooms: A Human-in-the-Loop Specialized Reinforcement Learning
Framework for Intervention Strategies
|
In complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide valuable insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time. These predictions enable the development of more effective intervention strategies.
|
[
"['Ammar N. Abbas' 'Chidera W. Amazu' 'Joseph Mietkiewicz' 'Houda Briwa'\n 'Andres Alonzo Perez' 'Gabriele Baldissone' 'Micaela Demichela'\n 'Georgios G. Chasparis' 'John D. Kelleher' 'Maria Chiara Leva']"
] |
null | null |
2402.13221
| null | null |
http://arxiv.org/pdf/2402.13221v2
|
2024-02-21T08:07:13Z
|
2024-02-20T18:32:27Z
|
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset
for Advancing Graph Machine Learning
|
Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML methods focused primarily on small organic molecules, recently, the scope of graph ML has expanded to include inorganic materials. Modelling the periodicity and symmetry of inorganic crystalline materials poses unique challenges, which existing graph ML methods are unable to address. Moving to inorganic nanomaterials increases complexity as the scale of number of nodes within each graph can be broad ($10$ to $10^5$). The bulk of existing graph ML focuses on characterising molecules and materials by predicting target properties with graphs as input. However, the most exciting applications of graph ML will be in their generative capabilities, which is currently not at par with other domains such as images or text. We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall >183M nodes, >1.2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K). We define 11 property prediction tasks and 6 structure prediction tasks, which are of special interest for nanomaterial research. We benchmark the performance of a wide array of baseline methods and use these benchmarking results to highlight areas which need future work. To the best of our knowledge, CHILI-3K and CHILI-100K are the first open-source nanomaterial datasets of this scale -- both on the individual graph level and of the dataset as a whole -- and the only nanomaterials datasets with high structural and elemental diversity.
|
[
"['Ulrik Friis-Jensen' 'Frederik L. Johansen' 'Andy S. Anker' 'Erik B. Dam'\n 'Kirsten M. Ø. Jensen' 'Raghavendra Selvan']"
] |
null | null |
2402.13224
| null | null |
http://arxiv.org/pdf/2402.13224v3
|
2024-03-19T12:28:13Z
|
2024-02-20T18:37:11Z
|
Controlling Large Electric Vehicle Charging Stations via User Behavior
Modeling and Stochastic Programming
|
This paper introduces an Electric Vehicle Charging Station (EVCS) model that incorporates real-world constraints, such as slot power limitations, contract threshold overruns penalties, or early disconnections of electric vehicles (EVs). We propose a formulation of the problem of EVCS control under uncertainty, and implement two Multi-Stage Stochastic Programming approaches that leverage user-provided information, namely, Model Predictive Control and Two-Stage Stochastic Programming. The model addresses uncertainties in charging session start and end times, as well as in energy demand. A user's behavior model based on a sojourn-time-dependent stochastic process enhances cost reduction while maintaining customer satisfaction. The benefits of the two proposed methods are showcased against two baselines over a 22-day simulation using a real-world dataset. The two-stage approach demonstrates robustness against early disconnections by considering a wider range of uncertainty scenarios for optimization. The algorithm prioritizing user satisfaction over electricity cost achieves a 20% and 36% improvement in two user satisfaction metrics compared to an industry-standard baseline. Additionally, the algorithm striking the best balance between cost and user satisfaction exhibits a mere 3% relative cost increase compared to the theoretically optimal baseline - for which the nonanticipativity constraint is relaxed - while attaining 94% and 84% of the user satisfaction performance in the two used satisfaction metrics.
|
[
"['Alban Puech' 'Tristan Rigaut' 'William Templier' 'Maud Tournoud']"
] |
null | null |
2402.13228
| null | null |
http://arxiv.org/pdf/2402.13228v2
|
2024-07-03T13:46:33Z
|
2024-02-20T18:42:34Z
|
Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive
|
Direct Preference Optimisation (DPO) is effective at significantly improving the performance of large language models (LLMs) on downstream tasks such as reasoning, summarisation, and alignment. Using pairs of preferred and dispreferred data, DPO models the relative probability of picking one response over another. In this work, first we show theoretically that the standard DPO loss can lead to a reduction of the model's likelihood of the preferred examples, as long as the relative probability between the preferred and dispreferred classes increases. We then show empirically that this phenomenon occurs when fine-tuning LLMs on common datasets, especially datasets in which the edit distance between pairs of completions is low. Using these insights, we design DPO-Positive (DPOP), a new loss function and training procedure which avoids this failure mode. Surprisingly, we find that DPOP outperforms DPO and other fine-tuning procedures across a wide variety of datasets and downstream tasks, including datasets with high edit distances between completions. Furthermore, we find that the DPOP-tuned model outperforms the DPO-tuned model (all else equal) on benchmarks independent of the fine-tuning data, such as MT-Bench. Finally, using DPOP, we create and open-source Smaug-34B and Smaug-72B, with the latter becoming the first open-source LLM to surpass an average accuracy of 80% on the HuggingFace Open LLM Leaderboard.
|
[
"['Arka Pal' 'Deep Karkhanis' 'Samuel Dooley' 'Manley Roberts'\n 'Siddartha Naidu' 'Colin White']"
] |
null | null |
2402.13233
| null | null |
http://arxiv.org/pdf/2402.13233v2
|
2024-02-27T00:25:25Z
|
2024-02-20T18:48:49Z
|
SMORE: Similarity-based Hyperdimensional Domain Adaptation for
Multi-Sensor Time Series Classification
|
Many real-world applications of the Internet of Things (IoT) employ machine learning (ML) algorithms to analyze time series information collected by interconnected sensors. However, distribution shift, a fundamental challenge in data-driven ML, arises when a model is deployed on a data distribution different from the training data and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) are required to capture intricate spatial and temporal dependencies in multi-sensor time series data, often exceeding the capabilities of today's edge devices. In this paper, we propose SMORE, a novel resource-efficient domain adaptation (DA) algorithm for multi-sensor time series classification, leveraging the efficient and parallel operations of hyperdimensional computing. SMORE dynamically customizes test-time models with explicit consideration of the domain context of each sample to mitigate the negative impacts of domain shifts. Our evaluation on a variety of multi-sensor time series classification tasks shows that SMORE achieves on average 1.98% higher accuracy than state-of-the-art (SOTA) DNN-based DA algorithms with 18.81x faster training and 4.63x faster inference.
|
[
"['Junyao Wang' 'Mohammad Abdullah Al Faruque']"
] |
null | null |
2402.13241
| null | null |
http://arxiv.org/pdf/2402.13241v2
|
2024-02-27T04:45:47Z
|
2024-02-20T18:53:53Z
|
Federated Causal Discovery from Heterogeneous Data
|
Conventional causal discovery methods rely on centralized data, which is inconsistent with the decentralized nature of data in many real-world situations. This discrepancy has motivated the development of federated causal discovery (FCD) approaches. However, existing FCD methods may be limited by their potentially restrictive assumptions of identifiable functional causal models or homogeneous data distributions, narrowing their applicability in diverse scenarios. In this paper, we propose a novel FCD method attempting to accommodate arbitrary causal models and heterogeneous data. We first utilize a surrogate variable corresponding to the client index to account for the data heterogeneity across different clients. We then develop a federated conditional independence test (FCIT) for causal skeleton discovery and establish a federated independent change principle (FICP) to determine causal directions. These approaches involve constructing summary statistics as a proxy of the raw data to protect data privacy. Owing to the nonparametric properties, FCIT and FICP make no assumption about particular functional forms, thereby facilitating the handling of arbitrary causal models. We conduct extensive experiments on synthetic and real datasets to show the efficacy of our method. The code is available at https://github.com/lokali/FedCDH.git.
|
[
"['Loka Li' 'Ignavier Ng' 'Gongxu Luo' 'Biwei Huang' 'Guangyi Chen'\n 'Tongliang Liu' 'Bin Gu' 'Kun Zhang']"
] |
null | null |
2402.13251
| null | null |
http://arxiv.org/pdf/2402.13251v2
|
2024-04-22T20:35:38Z
|
2024-02-20T18:59:00Z
|
FlashTex: Fast Relightable Mesh Texturing with LightControlNet
|
Manually creating textures for 3D meshes is time-consuming, even for expert visual content creators. We propose a fast approach for automatically texturing an input 3D mesh based on a user-provided text prompt. Importantly, our approach disentangles lighting from surface material/reflectance in the resulting texture so that the mesh can be properly relit and rendered in any lighting environment. We introduce LightControlNet, a new text-to-image model based on the ControlNet architecture, which allows the specification of the desired lighting as a conditioning image to the model. Our text-to-texture pipeline then constructs the texture in two stages. The first stage produces a sparse set of visually consistent reference views of the mesh using LightControlNet. The second stage applies a texture optimization based on Score Distillation Sampling (SDS) that works with LightControlNet to increase the texture quality while disentangling surface material from lighting. Our algorithm is significantly faster than previous text-to-texture methods, while producing high-quality and relightable textures.
|
[
"['Kangle Deng' 'Timothy Omernick' 'Alexander Weiss' 'Deva Ramanan'\n 'Jun-Yan Zhu' 'Tinghui Zhou' 'Maneesh Agrawala']"
] |
null | null |
2402.13254
| null | null |
http://arxiv.org/pdf/2402.13254v4
|
2024-06-12T17:59:55Z
|
2024-02-20T18:59:55Z
|
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic
Compositional Reasoning via Counterfactual Examples
|
We propose CounterCurate, a framework to comprehensively improve the visio-linguistic compositional reasoning capability for both contrastive and generative multimodal models. In particular, we identify two critical under-explored problems: the neglect of the physically grounded reasoning (counting and position understanding) and the potential of using highly capable text and image generation models for semantic counterfactual fine-tuning. Our work pioneers an approach that addresses these gaps. We first spotlight the near-chance performance of multimodal models like CLIP and LLaVA in physically grounded compositional reasoning. We then apply simple data augmentation using grounded image generation model GLIGEN to generate fine-tuning data, resulting in significant performance improvements: +33% and +37% for CLIP and LLaVA, respectively, on our newly curated Flickr30k-Positions benchmark. Moreover, we exploit the capabilities of high-performing text generation and image generation models, specifically GPT-4V and DALLE-3, to curate challenging semantic counterfactuals, thereby further enhancing compositional reasoning capabilities on benchmarks such as SugarCrepe, where CounterCurate outperforms GPT-4V. To facilitate future research, we release our code, dataset, benchmark, and checkpoints at https://countercurate.github.io.
|
[
"['Jianrui Zhang' 'Mu Cai' 'Tengyang Xie' 'Yong Jae Lee']"
] |
null | null |
2402.13270
| null | null |
http://arxiv.org/pdf/2402.13270v1
|
2024-02-16T15:26:33Z
|
2024-02-16T15:26:33Z
|
Global Tropical Cyclone Intensity Forecasting with Multi-modal
Multi-scale Causal Autoregressive Model
|
Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies. Current methods predominantly rely on limited spatiotemporal information from ERA5 data and neglect the causal relationships between these physical variables, failing to fully capture the spatial and temporal patterns required for intensity forecasting. To address this issue, we propose a Multi-modal multi-Scale Causal AutoRegressive model (MSCAR), which is the first model that combines causal relationships with large-scale multi-modal data for global TC intensity autoregressive forecasting. Furthermore, given the current absence of a TC dataset that offers a wide range of spatial variables, we present the Satellite and ERA5-based Tropical Cyclone Dataset (SETCD), which stands as the longest and most comprehensive global dataset related to TCs. Experiments on the dataset show that MSCAR outperforms the state-of-the-art methods, achieving maximum reductions in global and regional forecast errors of 9.52% and 6.74%, respectively. The code and dataset are publicly available at https://anonymous.4open.science/r/MSCAR.
|
[
"['Xinyu Wang' 'Kang Chen' 'Lei Liu' 'Tao Han' 'Bin Li' 'Lei Bai']"
] |
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