categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.04419 | null | null | http://arxiv.org/pdf/2406.04419v1 | 2024-06-06T18:05:10Z | 2024-06-06T18:05:10Z | TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification | Time series classification (TSC) on multivariate time series is a critical problem. We propose a novel multi-view approach integrating frequency-domain and time-domain features to provide complementary contexts for TSC. Our method fuses continuous wavelet transform spectral features with temporal convolutional or multilayer perceptron features. We leverage the Mamba state space model for efficient and scalable sequence modeling. We also introduce a novel tango scanning scheme to better model sequence relationships. Experiments on 10 standard benchmark datasets demonstrate our approach achieves an average 6.45% accuracy improvement over state-of-the-art TSC models. | [
"['Md Atik Ahamed' 'Qiang Cheng']"
] |
null | null | 2406.04421 | null | null | http://arxiv.org/pdf/2406.04421v1 | 2024-06-06T18:06:50Z | 2024-06-06T18:06:50Z | Enhancing Supervised Visualization through Autoencoder and Random Forest
Proximities for Out-of-Sample Extension | The value of supervised dimensionality reduction lies in its ability to uncover meaningful connections between data features and labels. Common dimensionality reduction methods embed a set of fixed, latent points, but are not capable of generalizing to an unseen test set. In this paper, we provide an out-of-sample extension method for the random forest-based supervised dimensionality reduction method, RF-PHATE, combining information learned from the random forest model with the function-learning capabilities of autoencoders. Through quantitative assessment of various autoencoder architectures, we identify that networks that reconstruct random forest proximities are more robust for the embedding extension problem. Furthermore, by leveraging proximity-based prototypes, we achieve a 40% reduction in training time without compromising extension quality. Our method does not require label information for out-of-sample points, thus serving as a semi-supervised method, and can achieve consistent quality using only 10% of the training data. | [
"['Shuang Ni' 'Adrien Aumon' 'Guy Wolf' 'Kevin R. Moon' 'Jake S. Rhodes']"
] |
null | null | 2406.04424 | null | null | http://arxiv.org/pdf/2406.04424v1 | 2024-06-06T18:08:50Z | 2024-06-06T18:08:50Z | Improving Model Chain Approaches for Probabilistic Solar Energy
Forecasting through Post-processing and Machine Learning | Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting, where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production, using additional weather variables as auxiliary information. Ensemble weather forecasts aim to quantify uncertainty in the future development of the weather, and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions. However, ensemble prediction systems are known to exhibit systematic errors, and thus require post-processing to obtain accurate and reliable probabilistic forecasts. The overarching aim of our study is to systematically evaluate different strategies to apply post-processing methods in model chain approaches: Not applying any post-processing at all; post-processing only the irradiance predictions before the conversion; post-processing only the solar power predictions obtained from the model chain; or applying post-processing in both steps. In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S., we develop statistical and machine learning methods for post-processing ensemble predictions of global horizontal irradiance and solar power generation. Further, we propose a neural network-based model for direct solar power forecasting that bypasses the model chain. Our results indicate that post-processing substantially improves the solar power generation forecasts, in particular when post-processing is applied to the power predictions. The machine learning methods for post-processing yield slightly better probabilistic forecasts, and the direct forecasting approach performs comparable to the post-processing strategies. | [
"['Nina Horat' 'Sina Klerings' 'Sebastian Lerch']"
] |
null | null | 2406.04425 | null | null | http://arxiv.org/pdf/2406.04425v1 | 2024-06-06T18:10:51Z | 2024-06-06T18:10:51Z | On Regularization via Early Stopping for Least Squares Regression | A fundamental problem in machine learning is understanding the effect of early stopping on the parameters obtained and the generalization capabilities of the model. Even for linear models, the effect is not fully understood for arbitrary learning rates and data. In this paper, we analyze the dynamics of discrete full batch gradient descent for linear regression. With minimal assumptions, we characterize the trajectory of the parameters and the expected excess risk. Using this characterization, we show that when training with a learning rate schedule $eta_k$, and a finite time horizon $T$, the early stopped solution $beta_T$ is equivalent to the minimum norm solution for a generalized ridge regularized problem. We also prove that early stopping is beneficial for generic data with arbitrary spectrum and for a wide variety of learning rate schedules. We provide an estimate for the optimal stopping time and empirically demonstrate the accuracy of our estimate. | [
"['Rishi Sonthalia' 'Jackie Lok' 'Elizaveta Rebrova']"
] |
null | null | 2406.04426 | null | null | http://arxiv.org/pdf/2406.04426v2 | 2024-06-13T12:54:10Z | 2024-06-06T18:12:04Z | DeTra: A Unified Model for Object Detection and Trajectory Forecasting | The tasks of object detection and trajectory forecasting play a crucial role in understanding the scene for autonomous driving. These tasks are typically executed in a cascading manner, making them prone to compounding errors. Furthermore, there is usually a very thin interface between the two tasks, creating a lossy information bottleneck. To address these challenges, our approach formulates the union of the two tasks as a trajectory refinement problem, where the first pose is the detection (current time), and the subsequent poses are the waypoints of the multiple forecasts (future time). To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects directly from LiDAR point clouds and high-definition maps. We call this model DeTra, short for object Detection and Trajectory forecasting. In our experiments, we observe that ourmodel{} outperforms the state-of-the-art on Argoverse 2 Sensor and Waymo Open Dataset by a large margin, across a broad range of metrics. Last but not least, we perform extensive ablation studies that show the value of refinement for this task, that every proposed component contributes positively to its performance, and that key design choices were made. | [
"['Sergio Casas' 'Ben Agro' 'Jiageng Mao' 'Thomas Gilles' 'Alexander Cui'\n 'Thomas Li' 'Raquel Urtasun']"
] |
null | null | 2406.04443 | null | null | http://arxiv.org/pdf/2406.04443v1 | 2024-06-06T18:49:10Z | 2024-06-06T18:49:10Z | Gradient Clipping Improves AdaGrad when the Noise Is Heavy-Tailed | Methods with adaptive stepsizes, such as AdaGrad and Adam, are essential for training modern Deep Learning models, especially Large Language Models. Typically, the noise in the stochastic gradients is heavy-tailed for the later ones. Gradient clipping provably helps to achieve good high-probability convergence for such noises. However, despite the similarity between AdaGrad/Adam and Clip-SGD, the high-probability convergence of AdaGrad/Adam has not been studied in this case. In this work, we prove that AdaGrad (and its delayed version) can have provably bad high-probability convergence if the noise is heavy-tailed. To fix this issue, we propose a new version of AdaGrad called Clip-RAdaGradD (Clipped Reweighted AdaGrad with Delay) and prove its high-probability convergence bounds with polylogarithmic dependence on the confidence level for smooth convex/non-convex stochastic optimization with heavy-tailed noise. Our empirical evaluations, including NLP model fine-tuning, highlight the superiority of clipped versions of AdaGrad/Adam in handling the heavy-tailed noise. | [
"['Savelii Chezhegov' 'Yaroslav Klyukin' 'Andrei Semenov'\n 'Aleksandr Beznosikov' 'Alexander Gasnikov' 'Samuel Horváth'\n 'Martin Takáč' 'Eduard Gorbunov']"
] |
null | null | 2406.04446 | null | null | http://arxiv.org/pdf/2406.04446v1 | 2024-06-06T19:01:42Z | 2024-06-06T19:01:42Z | Can Language Models Use Forecasting Strategies? | Advances in deep learning systems have allowed large models to match or surpass human accuracy on a number of skills such as image classification, basic programming, and standardized test taking. As the performance of the most capable models begin to saturate on tasks where humans already achieve high accuracy, it becomes necessary to benchmark models on increasingly complex abilities. One such task is forecasting the future outcome of events. In this work we describe experiments using a novel dataset of real world events and associated human predictions, an evaluation metric to measure forecasting ability, and the accuracy of a number of different LLM based forecasting designs on the provided dataset. Additionally, we analyze the performance of the LLM forecasters against human predictions and find that models still struggle to make accurate predictions about the future. Our follow-up experiments indicate this is likely due to models' tendency to guess that most events are unlikely to occur (which tends to be true for many prediction datasets, but does not reflect actual forecasting abilities). We reflect on next steps for developing a systematic and reliable approach to studying LLM forecasting. | [
"['Sarah Pratt' 'Seth Blumberg' 'Pietro Kreitlon Carolino'\n 'Meredith Ringel Morris']"
] |
null | null | 2406.04456 | null | null | http://arxiv.org/pdf/2406.04456v1 | 2024-06-06T19:29:33Z | 2024-06-06T19:29:33Z | Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN | We develop a graph neural network (GNN) to compute, within a time budget of 1 to 2 milliseconds required by practical systems, the optimal linear precoder (OLP) maximizing the minimal downlink user data rate for a Cell-Free Massive MIMO system - a key 6G wireless technology. The state-of-the-art method is a bisection search on second order cone programming feasibility test (B-SOCP) which is a magnitude too slow for practical systems. Our approach relies on representing OLP as a node-level prediction task on a graph. We construct a graph that accurately captures the interdependence relation between access points (APs) and user equipments (UEs), and the permutation equivariance of the Max-Min problem. Our neural network, named OLP-GNN, is trained on data obtained by B-SOCP. We tailor the OLP-GNN size, together with several artful data preprocessing and postprocessing methods to meet the runtime requirement. We show by extensive simulations that it achieves near optimal spectral efficiency in a range of scenarios with different number of APs and UEs, and for both line-of-sight and non-line-of-sight radio propagation environments. | [
"['Benjamin Parlier' 'Lou Salaün' 'Hong Yang']"
] |
null | null | 2406.04464 | null | null | http://arxiv.org/pdf/2406.04464v1 | 2024-06-06T19:44:17Z | 2024-06-06T19:44:17Z | On The Importance of Reasoning for Context Retrieval in Repository-Level
Code Editing | Recent advancements in code-fluent Large Language Models (LLMs) enabled the research on repository-level code editing. In such tasks, the model navigates and modifies the entire codebase of a project according to request. Hence, such tasks require efficient context retrieval, i.e., navigating vast codebases to gather relevant context. Despite the recognized importance of context retrieval, existing studies tend to approach repository-level coding tasks in an end-to-end manner, rendering the impact of individual components within these complicated systems unclear. In this work, we decouple the task of context retrieval from the other components of the repository-level code editing pipelines. We lay the groundwork to define the strengths and weaknesses of this component and the role that reasoning plays in it by conducting experiments that focus solely on context retrieval. We conclude that while the reasoning helps to improve the precision of the gathered context, it still lacks the ability to identify its sufficiency. We also outline the ultimate role of the specialized tools in the process of context gathering. The code supplementing this paper is available at https://github.com/JetBrains-Research/ai-agents-code-editing. | [
"['Alexander Kovrigin' 'Aleksandra Eliseeva' 'Yaroslav Zharov'\n 'Timofey Bryksin']"
] |
null | null | 2406.04472 | null | null | http://arxiv.org/pdf/2406.04472v1 | 2024-06-06T19:56:33Z | 2024-06-06T19:56:33Z | On the Hardness of Probabilistic Neurosymbolic Learning | The limitations of purely neural learning have sparked an interest in probabilistic neurosymbolic models, which combine neural networks with probabilistic logical reasoning. As these neurosymbolic models are trained with gradient descent, we study the complexity of differentiating probabilistic reasoning. We prove that although approximating these gradients is intractable in general, it becomes tractable during training. Furthermore, we introduce WeightME, an unbiased gradient estimator based on model sampling. Under mild assumptions, WeightME approximates the gradient with probabilistic guarantees using a logarithmic number of calls to a SAT solver. Lastly, we evaluate the necessity of these guarantees on the gradient. Our experiments indicate that the existing biased approximations indeed struggle to optimize even when exact solving is still feasible. | [
"['Jaron Maene' 'Vincent Derkinderen' 'Luc De Raedt']"
] |
null | null | 2406.04476 | null | null | http://arxiv.org/pdf/2406.04476v1 | 2024-06-06T20:02:49Z | 2024-06-06T20:02:49Z | Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving
Reachability Analysis | We propose a novel reachability analysis method tailored for neural networks with differentiable activations. Our idea hinges on a sound abstraction of the neural network map based on first-order Taylor expansion and bounding the remainder. To this end, we propose a method to compute analytical bounds on the network's first derivative (gradient) and second derivative (Hessian). A key aspect of our method is loop transformation on the activation functions to exploit their monotonicity effectively. The resulting end-to-end abstraction locally preserves the derivative information, yielding accurate bounds on small input sets. Finally, we employ a branch and bound framework for larger input sets to refine the abstraction recursively. We evaluate our method numerically via different examples and compare the results with relevant state-of-the-art methods. | [
"['Sina Sharifi' 'Mahyar Fazlyab']"
] |
null | null | 2406.04478 | null | null | http://arxiv.org/pdf/2406.04478v1 | 2024-06-06T20:06:42Z | 2024-06-06T20:06:42Z | PromptFix: Few-shot Backdoor Removal via Adversarial Prompt Tuning | Pre-trained language models (PLMs) have attracted enormous attention over the past few years with their unparalleled performances. Meanwhile, the soaring cost to train PLMs as well as their amazing generalizability have jointly contributed to few-shot fine-tuning and prompting as the most popular training paradigms for natural language processing (NLP) models. Nevertheless, existing studies have shown that these NLP models can be backdoored such that model behavior is manipulated when trigger tokens are presented. In this paper, we propose PromptFix, a novel backdoor mitigation strategy for NLP models via adversarial prompt-tuning in few-shot settings. Unlike existing NLP backdoor removal methods, which rely on accurate trigger inversion and subsequent model fine-tuning, PromptFix keeps the model parameters intact and only utilizes two extra sets of soft tokens which approximate the trigger and counteract it respectively. The use of soft tokens and adversarial optimization eliminates the need to enumerate possible backdoor configurations and enables an adaptive balance between trigger finding and preservation of performance. Experiments with various backdoor attacks validate the effectiveness of the proposed method and the performances when domain shift is present further shows PromptFix's applicability to models pretrained on unknown data source which is the common case in prompt tuning scenarios. | [
"['Tianrong Zhang' 'Zhaohan Xi' 'Ting Wang' 'Prasenjit Mitra'\n 'Jinghui Chen']"
] |
null | null | 2406.04487 | null | null | http://arxiv.org/pdf/2406.04487v1 | 2024-06-06T20:21:27Z | 2024-06-06T20:21:27Z | A multi-core periphery perspective: Ranking via relative centrality | Community and core-periphery are two widely studied graph structures, with their coexistence observed in real-world graphs (Rombach, Porter, Fowler & Mucha [SIAM J. App. Math. 2014, SIAM Review 2017]). However, the nature of this coexistence is not well understood and has been pointed out as an open problem (Yanchenko & Sengupta [Statistics Surveys, 2023]). Especially, the impact of inferring the core-periphery structure of a graph on understanding its community structure is not well utilized. In this direction, we introduce a novel quantification for graphs with ground truth communities, where each community has a densely connected part (the core), and the rest is more sparse (the periphery), with inter-community edges more frequent between the peripheries. Built on this structure, we propose a new algorithmic concept that we call relative centrality to detect the cores. We observe that core-detection algorithms based on popular centrality measures such as PageRank and degree centrality can show some bias in their outcome by selecting very few vertices from some cores. We show that relative centrality solves this bias issue and provide theoretical and simulation support, as well as experiments on real-world graphs. Core detection is known to have important applications with respect to core-periphery structures. In our model, we show a new application: relative-centrality-based algorithms can select a subset of the vertices such that it contains sufficient vertices from all communities, and points in this subset are better separable into their respective communities. We apply the methods to 11 biological datasets, with our methods resulting in a more balanced selection of vertices from all communities such that clustering algorithms have better performance on this set. | [
"['Chandra Sekhar Mukherjee' 'Jiapeng Zhang']"
] |
null | null | 2406.04488 | null | null | http://arxiv.org/pdf/2406.04488v1 | 2024-06-06T20:22:56Z | 2024-06-06T20:22:56Z | Negative Feedback for Music Personalization | Next-item recommender systems are often trained using only positive feedback with randomly-sampled negative feedback. We show the benefits of using real negative feedback both as inputs into the user sequence and also as negative targets for training a next-song recommender system for internet radio. In particular, using explicit negative samples during training helps reduce training time by ~60% while also improving test accuracy by ~6%; adding user skips as additional inputs also can considerably increase user coverage alongside slightly improving accuracy. We test the impact of using a large number of random negative samples to capture a 'harder' one and find that the test accuracy increases with more randomly-sampled negatives, but only to a point. Too many random negatives leads to false negatives that limits the lift, which is still lower than if using true negative feedback. We also find that the test accuracy is fairly robust with respect to the proportion of different feedback types, and compare the learned embeddings for different feedback types. | [
"['M. Jeffrey Mei' 'Oliver Bembom' 'Andreas F. Ehmann']"
] |
null | null | 2406.04490 | null | null | http://arxiv.org/pdf/2406.04490v1 | 2024-06-06T20:28:05Z | 2024-06-06T20:28:05Z | User Intent Recognition and Semantic Cache Optimization-Based Query
Processing Framework using CFLIS and MGR-LAU | Query Processing (QP) is optimized by a Cloud-based cache by storing the frequently accessed data closer to users. Nevertheless, the lack of focus on user intention type in queries affected the efficiency of QP in prevailing works. Thus, by using a Contextual Fuzzy Linguistic Inference System (CFLIS), this work analyzed the informational, navigational, and transactional-based intents in queries for enhanced QP. Primarily, the user query is parsed using tokenization, normalization, stop word removal, stemming, and POS tagging and then expanded using the WordNet technique. After expanding the queries, to enhance query understanding and to facilitate more accurate analysis and retrieval in query processing, the named entity is recognized using Bidirectional Encoder UnispecNorm Representations from Transformers (BEUNRT). Next, for efficient QP and retrieval of query information from the semantic cache database, the data is structured using Epanechnikov Kernel-Ordering Points To Identify the Clustering Structure (EK-OPTICS). The features are extracted from the structured data. Now, sentence type is identified and intent keywords are extracted from the parsed query. Next, the extracted features, detected intents and structured data are inputted to the Multi-head Gated Recurrent Learnable Attention Unit (MGR-LAU), which processes the query based on a semantic cache database (stores previously interpreted queries to expedite effective future searches). Moreover, the query is processed with a minimum latency of 12856ms. Lastly, the Semantic Similarity (SS) is analyzed between the retrieved query and the inputted user query, which continues until the similarity reaches 0.9 and above. Thus, the proposed work surpassed the previous methodologies. | [
"['Sakshi Mahendru']"
] |
null | null | 2406.04496 | null | null | http://arxiv.org/pdf/2406.04496v1 | 2024-06-06T20:41:36Z | 2024-06-06T20:41:36Z | Time Sensitive Knowledge Editing through Efficient Finetuning | Large Language Models (LLMs) have demonstrated impressive capability in different tasks and are bringing transformative changes to many domains. However, keeping the knowledge in LLMs up-to-date remains a challenge once pretraining is complete. It is thus essential to design effective methods to both update obsolete knowledge and induce new knowledge into LLMs. Existing locate-and-edit knowledge editing (KE) method suffers from two limitations. First, the post-edit LLMs by such methods generally have poor capability in answering complex queries that require multi-hop reasoning. Second, the long run-time of such locate-and-edit methods to perform knowledge edits make it infeasible for large scale KE in practice. In this paper, we explore Parameter-Efficient Fine-Tuning (PEFT) techniques as an alternative for KE. We curate a more comprehensive temporal KE dataset with both knowledge update and knowledge injection examples for KE performance benchmarking. We further probe the effect of fine-tuning on a range of layers in an LLM for the multi-hop QA task. We find that PEFT performs better than locate-and-edit techniques for time-sensitive knowledge edits. | [
"['Xiou Ge' 'Ali Mousavi' 'Edouard Grave' 'Armand Joulin' 'Kun Qian'\n 'Benjamin Han' 'Mostafa Arefiyan' 'Yunyao Li']"
] |
null | null | 2406.04501 | null | null | http://arxiv.org/pdf/2406.04501v1 | 2024-06-06T20:55:40Z | 2024-06-06T20:55:40Z | FLUID-LLM: Learning Computational Fluid Dynamics with
Spatiotemporal-aware Large Language Models | Learning computational fluid dynamics (CFD) traditionally relies on computationally intensive simulations of the Navier-Stokes equations. Recently, large language models (LLMs) have shown remarkable pattern recognition and reasoning abilities in natural language processing (NLP) and computer vision (CV). However, these models struggle with the complex geometries inherent in fluid dynamics. We introduce FLUID-LLM, a novel framework combining pre-trained LLMs with spatiotemporal-aware encoding to predict unsteady fluid dynamics. Our approach leverages the temporal autoregressive abilities of LLMs alongside spatial-aware layers, bridging the gap between previous CFD prediction methods. Evaluations on standard benchmarks reveal significant performance improvements across various fluid datasets. Our results demonstrate that FLUID-LLM effectively integrates spatiotemporal information into pre-trained LLMs, enhancing CFD task performance. | [
"['Max Zhu' 'Adrián Bazaga' 'Pietro Liò']"
] |
null | null | 2406.04503 | null | null | http://arxiv.org/pdf/2406.04503v1 | 2024-06-06T20:56:53Z | 2024-06-06T20:56:53Z | Enhancing Precision in Tactile Internet-Enabled Remote Robotic Surgery:
Kalman Filter Approach | Accurately estimating the position of a patient's side robotic arm in real time in a remote surgery task is a significant challenge, particularly in Tactile Internet (TI) environments. This paper presents a Kalman Filter (KF) based computationally efficient position estimation method. The study also assume no prior knowledge of the dynamic system model of the robotic arm system. Instead, The JIGSAW dataset, which is a comprehensive collection of robotic surgical data, and the Master Tool Manipulator's (MTM) input are utilized to learn the system model using System Identification (SI) toolkit available in Matlab. We further investigate the effectiveness of KF to determine the position of the Patient Side Manipulator (PSM) under simulated network conditions that include delays, jitter, and packet loss. These conditions reflect the typical challenges encountered in real-world Tactile Internet applications. The results of the study highlight KF's resilience and effectiveness in achieving accurate state estimation despite network-induced uncertainties with over 90% estimation accuracy. | [
"['Muhammad Hanif Lashari' 'Wafa Batayneh' 'Ashfaq Khokhar']"
] |
null | null | 2406.04508 | null | null | http://arxiv.org/pdf/2406.04508v1 | 2024-06-06T21:05:39Z | 2024-06-06T21:05:39Z | OCCAM: Towards Cost-Efficient and Accuracy-Aware Image Classification
Inference | Image classification is a fundamental building block for a majority of computer vision applications. With the growing popularity and capacity of machine learning models, people can easily access trained image classifiers as a service online or offline. However, model use comes with a cost and classifiers of higher capacity usually incur higher inference costs. To harness the respective strengths of different classifiers, we propose a principled approach, OCCAM, to compute the best classifier assignment strategy over image classification queries (termed as the optimal model portfolio) so that the aggregated accuracy is maximized, under user-specified cost budgets. Our approach uses an unbiased and low-variance accuracy estimator and effectively computes the optimal solution by solving an integer linear programming problem. On a variety of real-world datasets, OCCAM achieves 40% cost reduction with little to no accuracy drop. | [
"['Dujian Ding' 'Bicheng Xu' 'Laks V. S. Lakshmanan']"
] |
null | null | 2406.04516 | null | null | http://arxiv.org/pdf/2406.04516v2 | 2024-06-11T01:08:53Z | 2024-06-06T21:21:03Z | Online Joint Fine-tuning of Multi-Agent Flows | A Flow is a collection of component models (``Agents'') which constructs the solution to a complex problem via iterative communication. Flows have emerged as state of the art architectures for code generation, and are the raison d'etre for frameworks like Autogen. However, flows are currently constructed via a combination of manual prompt engineering and stagewise supervised learning techniques; the latter is limited to acyclic flows with granular node supervision. In this writeup I describe a procedure for online joint fine-tuning of an entire flow inspired by the Learning to Search framework. The approach leverages simulator access to reduce preferences over entire episodes to preferences over individual node outputs; when the components are language models the latter is a well-studied problem. The approach is applicable to reward-free settings (e.g., text feedback) if an episode evaluator model is available. I apply to the multi-hop QA dataset Musique achieving a state-of-the-art result. | [
"['Paul Mineiro']"
] |
null | null | 2406.04519 | null | null | http://arxiv.org/pdf/2406.04519v2 | 2024-06-10T13:52:32Z | 2024-06-06T21:26:30Z | Multifidelity digital twin for real-time monitoring of structural
dynamics in aquaculture net cages | As the global population grows and climate change intensifies, sustainable food production is critical. Marine aquaculture offers a viable solution, providing a sustainable protein source. However, the industry's expansion requires novel technologies for remote management and autonomous operations. Digital twin technology can advance the aquaculture industry, but its adoption has been limited. Fish net cages, which are flexible floating structures, are critical yet vulnerable components of aquaculture farms. Exposed to harsh and dynamic marine environments, the cages experience significant loads and risk damage, leading to fish escapes, environmental impacts, and financial losses. We propose a multifidelity surrogate modeling framework for integration into a digital twin for real-time monitoring of aquaculture net cage structural dynamics under stochastic marine conditions. Central to this framework is the nonlinear autoregressive Gaussian process method, which learns complex, nonlinear cross-correlations between models of varying fidelity. It combines low-fidelity simulation data with a small set of high-fidelity field sensor measurements, which offer the real dynamics but are costly and spatially sparse. Validated at the SINTEF ACE fish farm in Norway, our digital twin receives online metocean data and accurately predicts net cage displacements and mooring line loads, aligning closely with field measurements. The proposed framework is beneficial where application-specific data are scarce, offering rapid predictions and real-time system representation. The developed digital twin prevents potential damages by assessing structural integrity and facilitates remote operations with unmanned underwater vehicles. Our work also compares GP and GCNs for predicting net cage deformation, highlighting the latter's effectiveness in complex structural applications. | [
"['Eirini Katsidoniotaki' 'Biao Su' 'Eleni Kelasidi'\n 'Themistoklis P. Sapsis']"
] |
null | null | 2406.04523 | null | null | http://arxiv.org/pdf/2406.04523v1 | 2024-06-06T21:38:08Z | 2024-06-06T21:38:08Z | Proofread: Fixes All Errors with One Tap | The impressive capabilities in Large Language Models (LLMs) provide a powerful approach to reimagine users' typing experience. This paper demonstrates Proofread, a novel Gboard feature powered by a server-side LLM in Gboard, enabling seamless sentence-level and paragraph-level corrections with a single tap. We describe the complete system in this paper, from data generation, metrics design to model tuning and deployment. To obtain models with sufficient quality, we implement a careful data synthetic pipeline tailored to online use cases, design multifaceted metrics, employ a two-stage tuning approach to acquire the dedicated LLM for the feature: the Supervised Fine Tuning (SFT) for foundational quality, followed by the Reinforcement Learning (RL) tuning approach for targeted refinement. Specifically, we find sequential tuning on Rewrite and proofread tasks yields the best quality in SFT stage, and propose global and direct rewards in the RL tuning stage to seek further improvement. Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56% good ratio. We launched the feature to Pixel 8 devices by serving the model on TPU v5 in Google Cloud, with thousands of daily active users. Serving latency was significantly reduced by quantization, bucket inference, text segmentation, and speculative decoding. Our demo could be seen in href{https://youtu.be/4ZdcuiwFU7I}{Youtube}. | [
"['Renjie Liu' 'Yanxiang Zhang' 'Yun Zhu' 'Haicheng Sun' 'Yuanbo Zhang'\n 'Michael Xuelin Huang' 'Shanqing Cai' 'Lei Meng' 'Shumin Zhai']"
] |
null | null | 2406.04527 | null | null | http://arxiv.org/pdf/2406.04527v1 | 2024-06-06T21:58:33Z | 2024-06-06T21:58:33Z | Generative Assignment Flows for Representing and Learning Joint
Distributions of Discrete Data | We introduce a novel generative model for the representation of joint probability distributions of a possibly large number of discrete random variables. The approach uses measure transport by randomized assignment flows on the statistical submanifold of factorizing distributions, which also enables to sample efficiently from the target distribution and to assess the likelihood of unseen data points. The embedding of the flow via the Segre map in the meta-simplex of all discrete joint distributions ensures that any target distribution can be represented in principle, whose complexity in practice only depends on the parametrization of the affinity function of the dynamical assignment flow system. Our model can be trained in a simulation-free manner without integration by conditional Riemannian flow matching, using the training data encoded as geodesics in closed-form with respect to the e-connection of information geometry. By projecting high-dimensional flow matching in the meta-simplex of joint distributions to the submanifold of factorizing distributions, our approach has strong motivation from first principles of modeling coupled discrete variables. Numerical experiments devoted to distributions of structured image labelings demonstrate the applicability to large-scale problems, which may include discrete distributions in other application areas. Performance measures show that our approach scales better with the increasing number of classes than recent related work. | [
"['Bastian Boll' 'Daniel Gonzalez-Alvarado' 'Stefania Petra'\n 'Christoph Schnörr']"
] |
null | null | 2406.04533 | null | null | http://arxiv.org/pdf/2406.04533v1 | 2024-06-06T22:09:43Z | 2024-06-06T22:09:43Z | Rare Class Prediction Model for Smart Industry in Semiconductor
Manufacturing | The evolution of industry has enabled the integration of physical and digital systems, facilitating the collection of extensive data on manufacturing processes. This integration provides a reliable solution for improving process quality and managing equipment health. However, data collected from real manufacturing processes often exhibit challenging properties, such as severe class imbalance, high rates of missing values, and noisy features, which hinder effective machine learning implementation. In this study, a rare class prediction approach is developed for in situ data collected from a smart semiconductor manufacturing process. The primary objective is to build a model that addresses issues of noise and class imbalance, enhancing class separation. The developed approach demonstrated promising results compared to existing literature, which would allow the prediction of new observations that could give insights into future maintenance plans and production quality. The model was evaluated using various performance metrics, with ROC curves showing an AUC of 0.95, a precision of 0.66, and a recall of 0.96 | [
"['Abdelrahman Farrag' 'Mohammed-Khalil Ghali' 'Yu Jin']"
] |
null | null | 2406.04534 | null | null | http://arxiv.org/pdf/2406.04534v1 | 2024-06-06T22:09:46Z | 2024-06-06T22:09:46Z | Strategically Conservative Q-Learning | Offline reinforcement learning (RL) is a compelling paradigm to extend RL's practical utility by leveraging pre-collected, static datasets, thereby avoiding the limitations associated with collecting online interactions. The major difficulty in offline RL is mitigating the impact of approximation errors when encountering out-of-distribution (OOD) actions; doing so ineffectively will lead to policies that prefer OOD actions, which can lead to unexpected and potentially catastrophic results. Despite the variety of works proposed to address this issue, they tend to excessively suppress the value function in and around OOD regions, resulting in overly pessimistic value estimates. In this paper, we propose a novel framework called Strategically Conservative Q-Learning (SCQ) that distinguishes between OOD data that is easy and hard to estimate, ultimately resulting in less conservative value estimates. Our approach exploits the inherent strengths of neural networks to interpolate, while carefully navigating their limitations in extrapolation, to obtain pessimistic yet still property calibrated value estimates. Theoretical analysis also shows that the value function learned by SCQ is still conservative, but potentially much less so than that of Conservative Q-learning (CQL). Finally, extensive evaluation on the D4RL benchmark tasks shows our proposed method outperforms state-of-the-art methods. Our code is available through url{https://github.com/purewater0901/SCQ}. | [
"['Yutaka Shimizu' 'Joey Hong' 'Sergey Levine' 'Masayoshi Tomizuka']"
] |
null | null | 2406.04535 | null | null | http://arxiv.org/pdf/2406.04535v1 | 2024-06-06T22:11:31Z | 2024-06-06T22:11:31Z | Tangent differential privacy | Differential privacy is a framework for protecting the identity of individual data points in the decision-making process. In this note, we propose a new form of differential privacy called tangent differential privacy. Compared with the usual differential privacy that is defined uniformly across data distributions, tangent differential privacy is tailored towards a specific data distribution of interest. It also allows for general distribution distances such as total variation distance and Wasserstein distance. In the case of risk minimization, we show that entropic regularization guarantees tangent differential privacy under rather general conditions on the risk function. | [
"['Lexing Ying']"
] |
null | null | 2406.04546 | null | null | http://arxiv.org/pdf/2406.04546v1 | 2024-06-06T23:08:03Z | 2024-06-06T23:08:03Z | FOOD: Facial Authentication and Out-of-Distribution Detection with
Short-Range FMCW Radar | This paper proposes a short-range FMCW radar-based facial authentication and out-of-distribution (OOD) detection framework. Our pipeline jointly estimates the correct classes for the in-distribution (ID) samples and detects the OOD samples to prevent their inaccurate prediction. Our reconstruction-based architecture consists of a main convolutional block with one encoder and multi-decoder configuration, and intermediate linear encoder-decoder parts. Together, these elements form an accurate human face classifier and a robust OOD detector. For our dataset, gathered using a 60 GHz short-range FMCW radar, our network achieves an average classification accuracy of 98.07% in identifying in-distribution human faces. As an OOD detector, it achieves an average Area Under the Receiver Operating Characteristic (AUROC) curve of 98.50% and an average False Positive Rate at 95% True Positive Rate (FPR95) of 6.20%. Also, our extensive experiments show that the proposed approach outperforms previous OOD detectors in terms of common OOD detection metrics. | [
"['Sabri Mustafa Kahya' 'Boran Hamdi Sivrikaya' 'Muhammet Sami Yavuz'\n 'Eckehard Steinbach']"
] |
null | null | 2406.04548 | null | null | http://arxiv.org/pdf/2406.04548v1 | 2024-06-06T23:09:54Z | 2024-06-06T23:09:54Z | GNNAnatomy: Systematic Generation and Evaluation of Multi-Level
Explanations for Graph Neural Networks | Graph Neural Networks (GNNs) have proven highly effective in various machine learning (ML) tasks involving graphs, such as node/graph classification and link prediction. However, explaining the decisions made by GNNs poses challenges because of the aggregated relational information based on graph structure, leading to complex data transformations. Existing methods for explaining GNNs often face limitations in systematically exploring diverse substructures and evaluating results in the absence of ground truths. To address this gap, we introduce GNNAnatomy, a model- and dataset-agnostic visual analytics system designed to facilitate the generation and evaluation of multi-level explanations for GNNs. In GNNAnatomy, we employ graphlets to elucidate GNN behavior in graph-level classification tasks. By analyzing the associations between GNN classifications and graphlet frequencies, we formulate hypothesized factual and counterfactual explanations. To validate a hypothesized graphlet explanation, we introduce two metrics: (1) the correlation between its frequency and the classification confidence, and (2) the change in classification confidence after removing this substructure from the original graph. To demonstrate the effectiveness of GNNAnatomy, we conduct case studies on both real-world and synthetic graph datasets from various domains. Additionally, we qualitatively compare GNNAnatomy with a state-of-the-art GNN explainer, demonstrating the utility and versatility of our design. | [
"['Hsiao-Ying Lu' 'Yiran Li'\n 'Ujwal Pratap Krishna Kaluvakolanu Thyagarajan' 'Kwan-Liu Ma']"
] |
null | null | 2406.04549 | null | null | http://arxiv.org/pdf/2406.04549v1 | 2024-06-06T23:19:57Z | 2024-06-06T23:19:57Z | Concurrent Training and Layer Pruning of Deep Neural Networks | We propose an algorithm capable of identifying and eliminating irrelevant layers of a neural network during the early stages of training. In contrast to weight or filter-level pruning, layer pruning reduces the harder to parallelize sequential computation of a neural network. We employ a structure using residual connections around nonlinear network sections that allow the flow of information through the network once a nonlinear section is pruned. Our approach is based on variational inference principles using Gaussian scale mixture priors on the neural network weights and allows for substantial cost savings during both training and inference. More specifically, the variational posterior distribution of scalar Bernoulli random variables multiplying a layer weight matrix of its nonlinear sections is learned, similarly to adaptive layer-wise dropout. To overcome challenges of concurrent learning and pruning such as premature pruning and lack of robustness with respect to weight initialization or the size of the starting network, we adopt the "flattening" hyper-prior on the prior parameters. We prove that, as a result of its usage, the solutions of the resulting optimization problem describe deterministic networks with parameters of the posterior distribution at either 0 or 1. We formulate a projected SGD algorithm and prove its convergence to such a solution using stochastic approximation results. In particular, we prove conditions that lead to a layer's weights converging to zero and derive practical pruning conditions from the theoretical results. The proposed algorithm is evaluated on the MNIST, CIFAR-10 and ImageNet datasets and common LeNet, VGG16 and ResNet architectures. The simulations demonstrate that our method achieves state-of the-art performance for layer pruning at reduced computational cost in distinction to competing methods due to the concurrent training and pruning. | [
"['Valentin Frank Ingmar Guenter' 'Athanasios Sideris']"
] |
null | null | 2406.04551 | null | null | http://arxiv.org/pdf/2406.04551v1 | 2024-06-06T23:35:51Z | 2024-06-06T23:35:51Z | Improving Geo-diversity of Generated Images with Contextualized Vendi
Score Guidance | With the growing popularity of text-to-image generative models, there has been increasing focus on understanding their risks and biases. Recent work has found that state-of-the-art models struggle to depict everyday objects with the true diversity of the real world and have notable gaps between geographic regions. In this work, we aim to increase the diversity of generated images of common objects such that per-region variations are representative of the real world. We introduce an inference time intervention, contextualized Vendi Score Guidance (c-VSG), that guides the backwards steps of latent diffusion models to increase the diversity of a sample as compared to a "memory bank" of previously generated images while constraining the amount of variation within that of an exemplar set of real-world contextualizing images. We evaluate c-VSG with two geographically representative datasets and find that it substantially increases the diversity of generated images, both for the worst performing regions and on average, while simultaneously maintaining or improving image quality and consistency. Additionally, qualitative analyses reveal that diversity of generated images is significantly improved, including along the lines of reductive region portrayals present in the original model. We hope that this work is a step towards text-to-image generative models that reflect the true geographic diversity of the world. | [
"['Reyhane Askari Hemmat' 'Melissa Hall' 'Alicia Sun' 'Candace Ross'\n 'Michal Drozdzal' 'Adriana Romero-Soriano']"
] |
null | null | 2406.04558 | null | null | http://arxiv.org/pdf/2406.04558v1 | 2024-06-07T00:13:31Z | 2024-06-07T00:13:31Z | On PI Controllers for Updating Lagrange Multipliers in Constrained
Optimization | Constrained optimization offers a powerful framework to prescribe desired behaviors in neural network models. Typically, constrained problems are solved via their min-max Lagrangian formulations, which exhibit unstable oscillatory dynamics when optimized using gradient descent-ascent. The adoption of constrained optimization techniques in the machine learning community is currently limited by the lack of reliable, general-purpose update schemes for the Lagrange multipliers. This paper proposes the $nu$PI algorithm and contributes an optimization perspective on Lagrange multiplier updates based on PI controllers, extending the work of Stooke, Achiam and Abbeel (2020). We provide theoretical and empirical insights explaining the inability of momentum methods to address the shortcomings of gradient descent-ascent, and contrast this with the empirical success of our proposed $nu$PI controller. Moreover, we prove that $nu$PI generalizes popular momentum methods for single-objective minimization. Our experiments demonstrate that $nu$PI reliably stabilizes the multiplier dynamics and its hyperparameters enjoy robust and predictable behavior. | [
"['Motahareh Sohrabi' 'Juan Ramirez' 'Tianyue H. Zhang'\n 'Simon Lacoste-Julien' 'Jose Gallego-Posada']"
] |
null | null | 2406.04562 | null | null | http://arxiv.org/pdf/2406.04562v1 | 2024-06-07T00:38:51Z | 2024-06-07T00:38:51Z | A Unified View of Group Fairness Tradeoffs Using Partial Information
Decomposition | This paper introduces a novel information-theoretic perspective on the relationship between prominent group fairness notions in machine learning, namely statistical parity, equalized odds, and predictive parity. It is well known that simultaneous satisfiability of these three fairness notions is usually impossible, motivating practitioners to resort to approximate fairness solutions rather than stringent satisfiability of these definitions. However, a comprehensive analysis of their interrelations, particularly when they are not exactly satisfied, remains largely unexplored. Our main contribution lies in elucidating an exact relationship between these three measures of (un)fairness by leveraging a body of work in information theory called partial information decomposition (PID). In this work, we leverage PID to identify the granular regions where these three measures of (un)fairness overlap and where they disagree with each other leading to potential tradeoffs. We also include numerical simulations to complement our results. | [
"['Faisal Hamman' 'Sanghamitra Dutta']"
] |
null | null | 2406.04566 | null | null | http://arxiv.org/pdf/2406.04566v1 | 2024-06-07T01:06:34Z | 2024-06-07T01:06:34Z | SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation
for Understanding Spatial Reasoning Capability of Large Language Models | Spatial reasoning is a crucial component of both biological and artificial intelligence. In this work, we present a comprehensive study of the capability of current state-of-the-art large language models (LLMs) on spatial reasoning. To support our study, we created and contribute a novel Spatial Reasoning Characterization (SpaRC) framework and Spatial Reasoning Paths (SpaRP) datasets, to enable an in-depth understanding of the spatial relations and compositions as well as the usefulness of spatial reasoning chains. We found that all the state-of-the-art LLMs do not perform well on the datasets -- their performances are consistently low across different setups. The spatial reasoning capability improves substantially as model sizes scale up. Finetuning both large language models (e.g., Llama-2-70B) and smaller ones (e.g., Llama-2-13B) can significantly improve their F1-scores by 7--32 absolute points. We also found that the top proprietary LLMs still significantly outperform their open-source counterparts in topological spatial understanding and reasoning. | [
"['Md Imbesat Hassan Rizvi' 'Xiaodan Zhu' 'Iryna Gurevych']"
] |
null | null | 2406.04567 | null | null | http://arxiv.org/pdf/2406.04567v2 | 2024-06-27T04:47:43Z | 2024-06-07T01:07:35Z | Error Bounds of Supervised Classification from Information-Theoretic
Perspective | There remains a list of unanswered research questions on deep learning (DL), including the remarkable generalization power of overparametrized neural networks, the efficient optimization performance despite the non-convexity, and the mechanisms behind flat minima in generalization. In this paper, we adopt an information-theoretic perspective to explore the theoretical foundations of supervised classification using deep neural networks (DNNs). Our analysis introduces the concepts of fitting error and model risk, which, together with generalization error, constitute an upper bound on the expected risk. We demonstrate that the generalization errors are bounded by the complexity, influenced by both the smoothness of distribution and the sample size. Consequently, task complexity serves as a reliable indicator of the dataset's quality, guiding the setting of regularization hyperparameters. Furthermore, the derived upper bound fitting error links the back-propagated gradient, Neural Tangent Kernel (NTK), and the model's parameter count with the fitting error. Utilizing the triangle inequality, we establish an upper bound on the expected risk. This bound offers valuable insights into the effects of overparameterization, non-convex optimization, and the flat minima in DNNs.Finally, empirical verification confirms a significant positive correlation between the derived theoretical bounds and the practical expected risk, confirming the practical relevance of the theoretical findings. | [
"['Binchuan Qi' 'Wei Gong' 'Li Li']"
] |
null | null | 2406.04568 | null | null | http://arxiv.org/pdf/2406.04568v1 | 2024-06-07T01:08:17Z | 2024-06-07T01:08:17Z | StackSight: Unveiling WebAssembly through Large Language Models and
Neurosymbolic Chain-of-Thought Decompilation | WebAssembly enables near-native execution in web applications and is increasingly adopted for tasks that demand high performance and robust security. However, its assembly-like syntax, implicit stack machine, and low-level data types make it extremely difficult for human developers to understand, spurring the need for effective WebAssembly reverse engineering techniques. In this paper, we propose StackSight, a novel neurosymbolic approach that combines Large Language Models (LLMs) with advanced program analysis to decompile complex WebAssembly code into readable C++ snippets. StackSight visualizes and tracks virtual stack alterations via a static analysis algorithm and then applies chain-of-thought prompting to harness LLM's complex reasoning capabilities. Evaluation results show that StackSight significantly improves WebAssembly decompilation. Our user study also demonstrates that code snippets generated by StackSight have significantly higher win rates and enable a better grasp of code semantics. | [
"['Weike Fang' 'Zhejian Zhou' 'Junzhou He' 'Weihang Wang']"
] |
null | null | 2406.04575 | null | null | http://arxiv.org/pdf/2406.04575v1 | 2024-06-07T01:30:21Z | 2024-06-07T01:30:21Z | Optimization of geological carbon storage operations with multimodal
latent dynamic model and deep reinforcement learning | Maximizing storage performance in geological carbon storage (GCS) is crucial for commercial deployment, but traditional optimization demands resource-intensive simulations, posing computational challenges. This study introduces the multimodal latent dynamic (MLD) model, a deep learning framework for fast flow prediction and well control optimization in GCS. The MLD model includes a representation module for compressed latent representations, a transition module for system state evolution, and a prediction module for flow responses. A novel training strategy combining regression loss and joint-embedding consistency loss enhances temporal consistency and multi-step prediction accuracy. Unlike existing models, the MLD supports diverse input modalities, allowing comprehensive data interactions. The MLD model, resembling a Markov decision process (MDP), can train deep reinforcement learning agents, specifically using the soft actor-critic (SAC) algorithm, to maximize net present value (NPV) through continuous interactions. The approach outperforms traditional methods, achieving the highest NPV while reducing computational resources by over 60%. It also demonstrates strong generalization performance, providing improved decisions for new scenarios based on knowledge from previous ones. | [
"['Zhongzheng Wang' 'Yuntian Chen' 'Guodong Chen' 'Dongxiao Zhang']"
] |
null | null | 2406.04584 | null | null | http://arxiv.org/pdf/2406.04584v1 | 2024-06-07T02:12:29Z | 2024-06-07T02:12:29Z | CLoG: Benchmarking Continual Learning of Image Generation Models | Continual Learning (CL) poses a significant challenge in Artificial Intelligence, aiming to mirror the human ability to incrementally acquire knowledge and skills. While extensive research has focused on CL within the context of classification tasks, the advent of increasingly powerful generative models necessitates the exploration of Continual Learning of Generative models (CLoG). This paper advocates for shifting the research focus from classification-based CL to CLoG. We systematically identify the unique challenges presented by CLoG compared to traditional classification-based CL. We adapt three types of existing CL methodologies, replay-based, regularization-based, and parameter-isolation-based methods to generative tasks and introduce comprehensive benchmarks for CLoG that feature great diversity and broad task coverage. Our benchmarks and results yield intriguing insights that can be valuable for developing future CLoG methods. Additionally, we will release a codebase designed to facilitate easy benchmarking and experimentation in CLoG publicly at https://github.com/linhaowei1/CLoG. We believe that shifting the research focus to CLoG will benefit the continual learning community and illuminate the path for next-generation AI-generated content (AIGC) in a lifelong learning paradigm. | [
"['Haotian Zhang' 'Junting Zhou' 'Haowei Lin' 'Hang Ye' 'Jianhua Zhu'\n 'Zihao Wang' 'Liangcai Gao' 'Yizhou Wang' 'Yitao Liang']"
] |
null | null | 2406.04592 | null | null | http://arxiv.org/pdf/2406.04592v1 | 2024-06-07T02:55:57Z | 2024-06-07T02:55:57Z | Convergence Analysis of Adaptive Gradient Methods under Refined
Smoothness and Noise Assumptions | Adaptive gradient methods are arguably the most successful optimization algorithms for neural network training. While it is well-known that adaptive gradient methods can achieve better dimensional dependence than stochastic gradient descent (SGD) under favorable geometry for stochastic convex optimization, the theoretical justification for their success in stochastic non-convex optimization remains elusive. In this paper, we aim to close this gap by analyzing the convergence rates of AdaGrad measured by the $ell_1$-norm of the gradient. Specifically, when the objective has $L$-Lipschitz gradient and the stochastic gradient variance is bounded by $sigma^2$, we prove a worst-case convergence rate of $tilde{mathcal{O}}(frac{sqrt{d}L}{sqrt{T}} + frac{sqrt{d} sigma}{T^{1/4}})$, where $d$ is the dimension of the problem.We also present a lower bound of ${Omega}(frac{sqrt{d}}{sqrt{T}})$ for minimizing the gradient $ell_1$-norm in the deterministic setting, showing the tightness of our upper bound in the noiseless case. Moreover, under more fine-grained assumptions on the smoothness structure of the objective and the gradient noise and under favorable gradient $ell_1/ell_2$ geometry, we show that AdaGrad can potentially shave a factor of $sqrt{d}$ compared to SGD. To the best of our knowledge, this is the first result for adaptive gradient methods that demonstrates a provable gain over SGD in the non-convex setting. | [
"['Devyani Maladkar' 'Ruichen Jiang' 'Aryan Mokhtari']"
] |
null | null | 2406.04594 | null | null | http://arxiv.org/pdf/2406.04594v1 | 2024-06-07T02:58:35Z | 2024-06-07T02:58:35Z | Boosting Large-scale Parallel Training Efficiency with C4: A
Communication-Driven Approach | The emergence of Large Language Models (LLMs) has necessitated the adoption of parallel training techniques, involving the deployment of thousands of GPUs to train a single model. Unfortunately, we have found that the efficiency of current parallel training is often suboptimal, largely due to the following two main issues. Firstly, hardware failures are inevitable, leading to interruptions in the training tasks. The inability to quickly identify the faulty components results in a substantial waste of GPU resources. Secondly, since GPUs must wait for parameter synchronization to complete before proceeding to the next round of computation, network congestions can greatly increase the waiting time for GPUs. To address these challenges, this paper introduces a communication-driven solution, namely the C4. The key insights of C4 are two folds. First, in parallel training, collective communication exhibits periodic and homogeneous characteristics, so any anomalies are certainly due to some form of hardware malfunction. By leveraging this feature, C4 can rapidly identify the faulty components, swiftly isolate the anomaly, and restart the task, thereby avoiding resource wastage caused by delays in anomaly detection. Second, the predictable communication model of collective communication, involving few large flows, allows C4 to efficiently execute traffic planning, substantially reducing network congestion. C4 has been extensively implemented across our production systems, cutting error-induced overhead by roughly 30% and enhancing runtime performance by about 15% for certain applications with moderate communication costs. | [
"['Jianbo Dong' 'Bin Luo' 'Jun Zhang' 'Pengcheng Zhang' 'Fei Feng'\n 'Yikai Zhu' 'Ang Liu' 'Zian Chen' 'Yi Shi' 'Hairong Jiao' 'Gang Lu'\n 'Yu Guan' 'Ennan Zhai' 'Wencong Xiao' 'Hanyu Zhao' 'Man Yuan'\n 'Siran Yang' 'Xiang Li' 'Jiamang Wang' 'Rui Men' 'Jianwei Zhang'\n 'Huang Zhong' 'Dennis Cai' 'Yuan Xie' 'Binzhang Fu']"
] |
null | null | 2406.04596 | null | null | http://arxiv.org/pdf/2406.04596v3 | 2024-06-11T19:51:26Z | 2024-06-07T03:00:07Z | Federated Representation Learning in the Under-Parameterized Regime | Federated representation learning (FRL) is a popular personalized federated learning (FL) framework where clients work together to train a common representation while retaining their personalized heads. Existing studies, however, largely focus on the over-parameterized regime. In this paper, we make the initial efforts to investigate FRL in the under-parameterized regime, where the FL model is insufficient to express the variations in all ground-truth models. We propose a novel FRL algorithm FLUTE, and theoretically characterize its sample complexity and convergence rate for linear models in the under-parameterized regime. To the best of our knowledge, this is the first FRL algorithm with provable performance guarantees in this regime. FLUTE features a data-independent random initialization and a carefully designed objective function that aids the distillation of subspace spanned by the global optimal representation from the misaligned local representations. On the technical side, we bridge low-rank matrix approximation techniques with the FL analysis, which may be of broad interest. We also extend FLUTE beyond linear representations. Experimental results demonstrate that FLUTE outperforms state-of-the-art FRL solutions in both synthetic and real-world tasks. | [
"['Renpu Liu' 'Cong Shen' 'Jing Yang']"
] |
null | null | 2406.04601 | null | null | http://arxiv.org/pdf/2406.04601v3 | 2024-06-11T21:10:58Z | 2024-06-07T03:19:24Z | Enhancing Size Generalization in Graph Neural Networks through
Disentangled Representation Learning | Although most graph neural networks (GNNs) can operate on graphs of any size, their classification performance often declines on graphs larger than those encountered during training. Existing methods insufficiently address the removal of size information from graph representations, resulting in sub-optimal performance and reliance on backbone models. In response, we propose DISGEN, a novel and model-agnostic framework designed to disentangle size factors from graph representations. DISGEN employs size- and task-invariant augmentations and introduces a decoupling loss that minimizes shared information in hidden representations, with theoretical guarantees for its effectiveness. Our empirical results show that DISGEN outperforms the state-of-the-art models by up to 6% on real-world datasets, underscoring its effectiveness in enhancing the size generalizability of GNNs. Our codes are available at: https://github.com/GraphmindDartmouth/DISGEN. | [
"['Zheng Huang' 'Qihui Yang' 'Dawei Zhou' 'Yujun Yan']"
] |
null | null | 2406.04606 | null | null | http://arxiv.org/pdf/2406.04606v1 | 2024-06-07T03:29:57Z | 2024-06-07T03:29:57Z | Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for
Explaining Language Model Predictions | The increasing complexity of foundational models underscores the necessity for explainability, particularly for fine-tuning, the most widely used training method for adapting models to downstream tasks. Instance attribution, one type of explanation, attributes the model prediction to each training example by an instance score. However, the robustness of instance scores, specifically towards dataset resampling, has been overlooked. To bridge this gap, we propose a notion of robustness on the sign of the instance score. We theoretically and empirically demonstrate that the popular leave-one-out-based methods lack robustness, while the Shapley value behaves significantly better, but at a higher computational cost. Accordingly, we introduce an efficient fine-tuning-free approximation of the Shapley value (FreeShap) for instance attribution based on the neural tangent kernel. We empirically demonstrate that FreeShap outperforms other methods for instance attribution and other data-centric applications such as data removal, data selection, and wrong label detection, and further generalize our scale to large language models (LLMs). Our code is available at https://github.com/JTWang2000/FreeShap. | [
"['Jingtan Wang' 'Xiaoqiang Lin' 'Rui Qiao' 'Chuan-Sheng Foo'\n 'Bryan Kian Hsiang Low']"
] |
null | null | 2406.04607 | null | null | http://arxiv.org/pdf/2406.04607v4 | 2024-06-28T03:53:21Z | 2024-06-07T03:31:58Z | MeGA: Merging Multiple Independently Trained Neural Networks Based on
Genetic Algorithm | In this paper, we introduce a novel method for merging the weights of multiple pre-trained neural networks using a genetic algorithm called MeGA. Traditional techniques, such as weight averaging and ensemble methods, often fail to fully harness the capabilities of pre-trained networks. Our approach leverages a genetic algorithm with tournament selection, crossover, and mutation to optimize weight combinations, creating a more effective fusion. This technique allows the merged model to inherit advantageous features from both parent models, resulting in enhanced accuracy and robustness. Through experiments on the CIFAR-10 dataset, we demonstrate that our genetic algorithm-based weight merging method improves test accuracy compared to individual models and conventional methods. This approach provides a scalable solution for integrating multiple pre-trained networks across various deep learning applications. Github is available at: https://github.com/YUNBLAK/MeGA-Merging-Multiple-Independently-Trained-Neural-Networks-Based-on-Genetic-Algorithm | [
"['Daniel Yun']"
] |
null | null | 2406.04609 | null | null | http://arxiv.org/pdf/2406.04609v2 | 2024-06-29T03:15:51Z | 2024-06-07T03:37:30Z | Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person
Generalization in Activity Recognition | Existing domain generalization (DG) methods for cross-person generalization tasks often face challenges in capturing intra- and inter-domain style diversity, resulting in domain gaps with the target domain. In this study, we explore a novel perspective to tackle this problem, a process conceptualized as domain padding. This proposal aims to enrich the domain diversity by synthesizing intra- and inter-domain style data while maintaining robustness to class labels. We instantiate this concept using a conditional diffusion model and introduce a style-fused sampling strategy to enhance data generation diversity. In contrast to traditional condition-guided sampling, our style-fused sampling strategy allows for the flexible use of one or more random styles to guide data synthesis. This feature presents a notable advancement: it allows for the maximum utilization of possible permutations and combinations among existing styles to generate a broad spectrum of new style instances. Empirical evaluations on a broad range of datasets demonstrate that our generated data achieves remarkable diversity within the domain space. Both intra- and inter-domain generated data have proven to be significant and valuable, contributing to varying degrees of performance enhancements. Notably, our approach outperforms state-of-the-art DG methods in all human activity recognition tasks. | [
"['Junru Zhang' 'Lang Feng' 'Zhidan Liu' 'Yuhan Wu' 'Yang He' 'Yabo Dong'\n 'Duanqing Xu']"
] |
null | null | 2406.04610 | null | null | http://arxiv.org/pdf/2406.04610v1 | 2024-06-07T03:37:36Z | 2024-06-07T03:37:36Z | Contrastive explainable clustering with differential privacy | This paper presents a novel approach in Explainable AI (XAI), integrating contrastive explanations with differential privacy in clustering methods. For several basic clustering problems, including $k$-median and $k$-means, we give efficient differential private contrastive explanations that achieve essentially the same explanations as those that non-private clustering explanations can obtain. We define contrastive explanations as the utility difference between the original clustering utility and utility from clustering with a specifically fixed centroid. In each contrastive scenario, we designate a specific data point as the fixed centroid position, enabling us to measure the impact of this constraint on clustering utility under differential privacy. Extensive experiments across various datasets show our method's effectiveness in providing meaningful explanations without significantly compromising data privacy or clustering utility. This underscores our contribution to privacy-aware machine learning, demonstrating the feasibility of achieving a balance between privacy and utility in the explanation of clustering tasks. | [
"['Dung Nguyen' 'Ariel Vetzler' 'Sarit Kraus' 'Anil Vullikanti']"
] |
null | null | 2406.04612 | null | null | http://arxiv.org/pdf/2406.04612v1 | 2024-06-07T03:40:15Z | 2024-06-07T03:40:15Z | Revisiting Attention Weights as Interpretations of Message-Passing
Neural Networks | The self-attention mechanism has been adopted in several widely-used message-passing neural networks (MPNNs) (e.g., GATs), which adaptively controls the amount of information that flows along the edges of the underlying graph. This usage of attention has made such models a baseline for studies on explainable AI (XAI) since interpretations via attention have been popularized in various domains (e.g., natural language processing and computer vision). However, existing studies often use naive calculations to derive attribution scores from attention, and do not take the precise and careful calculation of edge attribution into consideration. In our study, we aim to fill the gap between the widespread usage of attention-enabled MPNNs and their potential in largely under-explored explainability, a topic that has been actively investigated in other areas. To this end, as the first attempt, we formalize the problem of edge attribution from attention weights in GNNs. Then, we propose GATT, an edge attribution calculation method built upon the computation tree. Through comprehensive experiments, we demonstrate the effectiveness of our proposed method when evaluating attributions from GATs. Conversely, we empirically validate that simply averaging attention weights over graph attention layers is insufficient to interpret the GAT model's behavior. Code is publicly available at https://github.com/jordan7186/GAtt/tree/main. | [
"['Yong-Min Shin' 'Siqing Li' 'Xin Cao' 'Won-Yong Shin']"
] |
null | null | 2406.04619 | null | null | http://arxiv.org/pdf/2406.04619v1 | 2024-06-07T04:04:21Z | 2024-06-07T04:04:21Z | CTSyn: A Foundational Model for Cross Tabular Data Generation | Generative Foundation Models (GFMs) have produced synthetic data with remarkable quality in modalities such as images and text. However, applying GFMs to tabular data poses significant challenges due to the inherent heterogeneity of table features. Existing cross-table learning frameworks are hindered by the absence of both a generative model backbone and a decoding mechanism for heterogeneous feature values. To overcome these limitations, we introduce the Cross-Table Synthesizer (CTSyn), a diffusion-based foundational model tailored for tabular data generation. CTSyn introduces three major components: an aggregator that consolidates heterogeneous tables into a unified latent space; a conditional latent diffusion model for sampling from this space; and type-specific decoders that reconstruct values of varied data types from sampled latent vectors. Extensive testing on real-world datasets reveals that CTSyn not only significantly outperforms existing table synthesizers in utility and diversity, but also uniquely enhances performances of downstream machine learning beyond what is achievable with real data, thus establishing a new paradigm for synthetic data generation. | [
"['Xiaofeng Lin' 'Chenheng Xu' 'Matthew Yang' 'Guang Cheng']"
] |
null | null | 2406.04624 | null | null | http://arxiv.org/pdf/2406.04624v1 | 2024-06-07T04:11:45Z | 2024-06-07T04:11:45Z | Image Processing Based Forest Fire Detection | A novel approach for forest fire detection using image processing technique is proposed. A rule-based color model for fire pixel classification is used. The proposed algorithm uses RGB and YCbCr color space. The advantage of using YCbCr color space is that it can separate the luminance from the chrominance more effectively than RGB color space. The performance of the proposed algorithm is tested on two sets of images, one of which contains fire; the other contains fire-like regions. Standard methods are used for calculating the performance of the algorithm. The proposed method has both higher detection rate and lower false alarm rate. Since the algorithm is cheap in computation, it can be used for real-time forest fire detection. | [
"['Vipin V']"
] |
null | null | 2406.04626 | null | null | http://arxiv.org/pdf/2406.04626v2 | 2024-06-10T16:28:15Z | 2024-06-07T04:22:32Z | Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed
Neural Networks Framework for Interface Problems | We present an efficient physics-informed neural networks (PINNs) framework, termed Adaptive Interface-PINNs (AdaI-PINNs), to improve the modeling of interface problems with discontinuous coefficients and/or interfacial jumps. This framework is an enhanced version of its predecessor, Interface PINNs or I-PINNs (Sarma et al.; https://dx.doi.org/10.2139/ssrn.4766623), which involves domain decomposition and assignment of different predefined activation functions to the neural networks in each subdomain across a sharp interface, while keeping all other parameters of the neural networks identical. In AdaI-PINNs, the activation functions vary solely in their slopes, which are trained along with the other parameters of the neural networks. This makes the AdaI-PINNs framework fully automated without requiring preset activation functions. Comparative studies on one-dimensional, two-dimensional, and three-dimensional benchmark elliptic interface problems reveal that AdaI-PINNs outperform I-PINNs, reducing computational costs by 2-6 times while producing similar or better accuracy. | [
"['Sumanta Roy' 'Chandrasekhar Annavarapu' 'Pratanu Roy'\n 'Antareep Kumar Sarma']"
] |
null | null | 2406.04627 | null | null | http://arxiv.org/pdf/2406.04627v1 | 2024-06-07T04:27:32Z | 2024-06-07T04:27:32Z | Denoising-Aware Contrastive Learning for Noisy Time Series | Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. Despite the great success in recent years, there is limited discussion on the potential noise in the time series, which can severely impair the performance of existing SSL methods. To mitigate the noise, the de facto strategy is to apply conventional denoising methods before model training. However, this pre-processing approach may not fully eliminate the effect of noise in SSL for two reasons: (i) the diverse types of noise in time series make it difficult to automatically determine suitable denoising methods; (ii) noise can be amplified after mapping raw data into latent space. In this paper, we propose denoising-aware contrastive learning (DECL), which uses contrastive learning objectives to mitigate the noise in the representation and automatically selects suitable denoising methods for every sample. Extensive experiments on various datasets verify the effectiveness of our method. The code is open-sourced. | [
"['Shuang Zhou' 'Daochen Zha' 'Xiao Shen' 'Xiao Huang' 'Rui Zhang'\n 'Fu-Lai Chung']"
] |
null | null | 2406.04639 | null | null | http://arxiv.org/pdf/2406.04639v1 | 2024-06-07T04:54:00Z | 2024-06-07T04:54:00Z | Cooperative Meta-Learning with Gradient Augmentation | Model agnostic meta-learning (MAML) is one of the most widely used gradient-based meta-learning, consisting of two optimization loops: an inner loop and outer loop. MAML learns the new task from meta-initialization parameters with an inner update and finds the meta-initialization parameters in the outer loop. In general, the injection of noise into the gradient of the model for augmenting the gradient is one of the widely used regularization methods. In this work, we propose a novel cooperative meta-learning framework dubbed CML which leverages gradient-level regularization with gradient augmentation. We inject learnable noise into the gradient of the model for the model generalization. The key idea of CML is introducing the co-learner which has no inner update but the outer loop update to augment gradients for finding better meta-initialization parameters. Since the co-learner does not update in the inner loop, it can be easily deleted after meta-training. Therefore, CML infers with only meta-learner without additional cost and performance degradation. We demonstrate that CML is easily applicable to gradient-based meta-learning methods and CML leads to increased performance in few-shot regression, few-shot image classification and few-shot node classification tasks. Our codes are at https://github.com/JJongyn/CML. | [
"['Jongyun Shin' 'Seunjin Han' 'Jangho Kim']"
] |
null | null | 2406.04640 | null | null | http://arxiv.org/pdf/2406.04640v1 | 2024-06-07T04:54:36Z | 2024-06-07T04:54:36Z | LinkGPT: Teaching Large Language Models To Predict Missing Links | Large Language Models (LLMs) have shown promising results on various language and vision tasks. Recently, there has been growing interest in applying LLMs to graph-based tasks, particularly on Text-Attributed Graphs (TAGs). However, most studies have focused on node classification, while the use of LLMs for link prediction (LP) remains understudied. In this work, we propose a new task on LLMs, where the objective is to leverage LLMs to predict missing links between nodes in a graph. This task evaluates an LLM's ability to reason over structured data and infer new facts based on learned patterns. This new task poses two key challenges: (1) How to effectively integrate pairwise structural information into the LLMs, which is known to be crucial for LP performance, and (2) how to solve the computational bottleneck when teaching LLMs to perform LP. To address these challenges, we propose LinkGPT, the first end-to-end trained LLM for LP tasks. To effectively enhance the LLM's ability to understand the underlying structure, we design a two-stage instruction tuning approach where the first stage fine-tunes the pairwise encoder, projector, and node projector, and the second stage further fine-tunes the LLMs to predict links. To address the efficiency challenges at inference time, we introduce a retrieval-reranking scheme. Experiments show that LinkGPT can achieve state-of-the-art performance on real-world graphs as well as superior generalization in zero-shot and few-shot learning, surpassing existing benchmarks. At inference time, it can achieve $10times$ speedup while maintaining high LP accuracy. | [
"['Zhongmou He' 'Jing Zhu' 'Shengyi Qian' 'Joyce Chai' 'Danai Koutra']"
] |
null | null | 2406.04654 | null | null | http://arxiv.org/pdf/2406.04654v1 | 2024-06-07T05:46:39Z | 2024-06-07T05:46:39Z | GenzIQA: Generalized Image Quality Assessment using Prompt-Guided Latent
Diffusion Models | The design of no-reference (NR) image quality assessment (IQA) algorithms is extremely important to benchmark and calibrate user experiences in modern visual systems. A major drawback of state-of-the-art NR-IQA methods is their limited ability to generalize across diverse IQA settings with reasonable distribution shifts. Recent text-to-image generative models such as latent diffusion models generate meaningful visual concepts with fine details related to text concepts. In this work, we leverage the denoising process of such diffusion models for generalized IQA by understanding the degree of alignment between learnable quality-aware text prompts and images. In particular, we learn cross-attention maps from intermediate layers of the denoiser of latent diffusion models to capture quality-aware representations of images. In addition, we also introduce learnable quality-aware text prompts that enable the cross-attention features to be better quality-aware. Our extensive cross database experiments across various user-generated, synthetic, and low-light content-based benchmarking databases show that latent diffusion models can achieve superior generalization in IQA when compared to other methods in the literature. | [
"['Diptanu De' 'Shankhanil Mitra' 'Rajiv Soundararajan']"
] |
null | null | 2406.04657 | null | null | http://arxiv.org/pdf/2406.04657v1 | 2024-06-07T05:51:57Z | 2024-06-07T05:51:57Z | Crafting Heavy-Tails in Weight Matrix Spectrum without Gradient Noise | Modern training strategies of deep neural networks (NNs) tend to induce a heavy-tailed (HT) spectra of layer weights. Extensive efforts to study this phenomenon have found that NNs with HT weight spectra tend to generalize well. A prevailing notion for the occurrence of such HT spectra attributes gradient noise during training as a key contributing factor. Our work shows that gradient noise is unnecessary for generating HT weight spectra: two-layer NNs trained with full-batch Gradient Descent/Adam can exhibit HT spectra in their weights after finite training steps. To this end, we first identify the scale of the learning rate at which one step of full-batch Adam can lead to feature learning in the shallow NN, particularly when learning a single index teacher model. Next, we show that multiple optimizer steps with such (sufficiently) large learning rates can transition the bulk of the weight's spectra into an HT distribution. To understand this behavior, we present a novel perspective based on the singular vectors of the weight matrices and optimizer updates. We show that the HT weight spectrum originates from the `spike', which is generated from feature learning and interacts with the main bulk to generate an HT spectrum. Finally, we analyze the correlations between the HT weight spectra and generalization after multiple optimizer updates with varying learning rates. | [
"['Vignesh Kothapalli' 'Tianyu Pang' 'Shenyang Deng' 'Zongmin Liu'\n 'Yaoqing Yang']"
] |
null | null | 2406.04658 | null | null | http://arxiv.org/pdf/2406.04658v1 | 2024-06-07T05:56:43Z | 2024-06-07T05:56:43Z | Advanced Payment Security System:XGBoost, CatBoost and SMOTE Integrated | With the rise of various online and mobile payment systems, transaction fraud has become a significant threat to financial security. This study explores the application of advanced machine learning models, specifically XGBoost and LightGBM, for developing a more accurate and robust Payment Security Protection Model.To enhance data reliability, we meticulously processed the data sources and used SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance and improve data representation. By selecting highly correlated features, we aimed to strengthen the training process and boost model performance.We conducted thorough performance evaluations of our proposed models, comparing them against traditional methods including Random Forest, Neural Network, and Logistic Regression. Key metrics such as Precision, Recall, and F1 Score were used to rigorously assess their effectiveness.Our detailed analyses and comparisons reveal that the combination of SMOTE with XGBoost and LightGBM offers a highly efficient and powerful mechanism for payment security protection. The results show that these models not only outperform traditional approaches but also hold significant promise for advancing the field of transaction fraud prevention. | [
"['Qi Zheng' 'Chang Yu' 'Jin Cao' 'Yongshun Xu' 'Qianwen Xing' 'Yinxin Jin']"
] |
null | null | 2406.04687 | null | null | http://arxiv.org/pdf/2406.04687v1 | 2024-06-07T07:01:06Z | 2024-06-07T07:01:06Z | LogiCode: an LLM-Driven Framework for Logical Anomaly Detection | This paper presents LogiCode, a novel framework that leverages Large Language Models (LLMs) for identifying logical anomalies in industrial settings, moving beyond traditional focus on structural inconsistencies. By harnessing LLMs for logical reasoning, LogiCode autonomously generates Python codes to pinpoint anomalies such as incorrect component quantities or missing elements, marking a significant leap forward in anomaly detection technologies. A custom dataset "LOCO-Annotations" and a benchmark "LogiBench" are introduced to evaluate the LogiCode's performance across various metrics including binary classification accuracy, code generation success rate, and precision in reasoning. Findings demonstrate LogiCode's enhanced interpretability, significantly improving the accuracy of logical anomaly detection and offering detailed explanations for identified anomalies. This represents a notable shift towards more intelligent, LLM-driven approaches in industrial anomaly detection, promising substantial impacts on industry-specific applications. | [
"['Yiheng Zhang' 'Yunkang Cao' 'Xiaohao Xu' 'Weiming Shen']"
] |
null | null | 2406.04690 | null | null | http://arxiv.org/pdf/2406.04690v1 | 2024-06-07T07:02:50Z | 2024-06-07T07:02:50Z | Higher-order Structure Based Anomaly Detection on Attributed Networks | Anomaly detection (such as telecom fraud detection and medical image detection) has attracted the increasing attention of people. The complex interaction between multiple entities widely exists in the network, which can reflect specific human behavior patterns. Such patterns can be modeled by higher-order network structures, thus benefiting anomaly detection on attributed networks. However, due to the lack of an effective mechanism in most existing graph learning methods, these complex interaction patterns fail to be applied in detecting anomalies, hindering the progress of anomaly detection to some extent. In order to address the aforementioned issue, we present a higher-order structure based anomaly detection (GUIDE) method. We exploit attribute autoencoder and structure autoencoder to reconstruct node attributes and higher-order structures, respectively. Moreover, we design a graph attention layer to evaluate the significance of neighbors to nodes through their higher-order structure differences. Finally, we leverage node attribute and higher-order structure reconstruction errors to find anomalies. Extensive experiments on five real-world datasets (i.e., ACM, Citation, Cora, DBLP, and Pubmed) are implemented to verify the effectiveness of GUIDE. Experimental results in terms of ROC-AUC, PR-AUC, and Recall@K show that GUIDE significantly outperforms the state-of-art methods. | [
"['Xu Yuan' 'Na Zhou' 'Shuo Yu' 'Huafei Huang' 'Zhikui Chen' 'Feng Xia']"
] |
null | null | 2406.04693 | null | null | http://arxiv.org/pdf/2406.04693v1 | 2024-06-07T07:04:26Z | 2024-06-07T07:04:26Z | LLM-Vectorizer: LLM-based Verified Loop Vectorizer | Vectorization is a powerful optimization technique that significantly boosts the performance of high performance computing applications operating on large data arrays. Despite decades of research on auto-vectorization, compilers frequently miss opportunities to vectorize code. On the other hand, writing vectorized code manually using compiler intrinsics is still a complex, error-prone task that demands deep knowledge of specific architecture and compilers. In this paper, we evaluate the potential of large-language models (LLMs) to generate vectorized (Single Instruction Multiple Data) code from scalar programs that process individual array elements. We propose a novel finite-state machine multi-agents based approach that harnesses LLMs and test-based feedback to generate vectorized code. Our findings indicate that LLMs are capable of producing high performance vectorized code with run-time speedup ranging from 1.1x to 9.4x as compared to the state-of-the-art compilers such as Intel Compiler, GCC, and Clang. To verify the correctness of vectorized code, we use Alive2, a leading bounded translation validation tool for LLVM IR. We describe a few domain-specific techniques to improve the scalability of Alive2 on our benchmark dataset. Overall, our approach is able to verify 38.2% of vectorizations as correct on the TSVC benchmark dataset. | [
"['Jubi Taneja' 'Avery Laird' 'Cong Yan' 'Madan Musuvathi'\n 'Shuvendu K. Lahiri']"
] |
null | null | 2406.04702 | null | null | http://arxiv.org/pdf/2406.04702v1 | 2024-06-07T07:21:21Z | 2024-06-07T07:21:21Z | Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for
Federated Recommender Systems | Federated recommender systems have been crucially enhanced through data sharing and continuous model updates, attributed to the pervasive connectivity and distributed computing capabilities of Internet of Things (IoT) devices. Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount. However, existing methods fall short in tracing the flow of shared data and the evolution of model updates. Consequently, data sharing is vulnerable to exploitation by malicious entities, raising significant data privacy concerns, while excluding data sharing will result in sub-optimal recommendations. To mitigate these concerns, we present LIBERATE, a privacy-traceable federated recommender system. We design a blockchain-based traceability mechanism, ensuring data privacy during data sharing and model updates. We further enhance privacy protection by incorporating local differential privacy in user-server communication. Extensive evaluations with the real-world dataset corroborate LIBERATE's capabilities in ensuring data privacy during data sharing and model update while maintaining efficiency and performance. Results underscore blockchain-based traceability mechanism as a promising solution for privacy-preserving in federated recommender systems. | [
"['Zhen Cai' 'Tao Tang' 'Shuo Yu' 'Yunpeng Xiao' 'Feng Xia']"
] |
null | null | 2406.04706 | null | null | http://arxiv.org/pdf/2406.04706v1 | 2024-06-07T07:28:22Z | 2024-06-07T07:28:22Z | Winner-takes-all learners are geometry-aware conditional density
estimators | Winner-takes-all training is a simple learning paradigm, which handles ambiguous tasks by predicting a set of plausible hypotheses. Recently, a connection was established between Winner-takes-all training and centroidal Voronoi tessellations, showing that, once trained, hypotheses should quantize optimally the shape of the conditional distribution to predict. However, the best use of these hypotheses for uncertainty quantification is still an open question. In this work, we show how to leverage the appealing geometric properties of the Winner-takes-all learners for conditional density estimation, without modifying its original training scheme. We theoretically establish the advantages of our novel estimator both in terms of quantization and density estimation, and we demonstrate its competitiveness on synthetic and real-world datasets, including audio data. | [
"['Victor Letzelter' 'David Perera' 'Cédric Rommel' 'Mathieu Fontaine'\n 'Slim Essid' 'Gael Richard' 'Patrick Pérez']"
] |
null | null | 2406.04709 | null | null | http://arxiv.org/pdf/2406.04709v1 | 2024-06-07T07:35:14Z | 2024-06-07T07:35:14Z | ConDiff: A Challenging Dataset for Neural Solvers of Partial
Differential Equations | We present ConDiff, a novel dataset for scientific machine learning. ConDiff focuses on the diffusion equation with varying coefficients, a fundamental problem in many applications of parametric partial differential equations (PDEs). The main novelty of the proposed dataset is that we consider discontinuous coefficients with high contrast. These coefficient functions are sampled from a selected set of distributions. This class of problems is not only of great academic interest, but is also the basis for describing various environmental and industrial problems. In this way, ConDiff shortens the gap with real-world problems while remaining fully synthetic and easy to use. ConDiff consists of a diverse set of diffusion equations with coefficients covering a wide range of contrast levels and heterogeneity with a measurable complexity metric for clearer comparison between different coefficient functions. We baseline ConDiff on standard deep learning models in the field of scientific machine learning. By providing a large number of problem instances, each with its own coefficient function and right-hand side, we hope to encourage the development of novel physics-based deep learning approaches, such as neural operators and physics-informed neural networks, ultimately driving progress towards more accurate and efficient solutions of complex PDE problems. | [
"['Vladislav Trifonov' 'Alexander Rudikov' 'Oleg Iliev' 'Ivan Oseledets'\n 'Ekaterina Muravleva']"
] |
null | null | 2406.04713 | null | null | http://arxiv.org/pdf/2406.04713v1 | 2024-06-07T07:46:23Z | 2024-06-07T07:46:23Z | FlowMM: Generating Materials with Riemannian Flow Matching | Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrangements of atoms in a periodic lattice only a vanishingly small percentage are thermodynamically stable, which is a key indicator of the materials that can be experimentally realized. Two fundamental tasks in this area are to (a) predict the stable crystal structure of a known composition of elements and (b) propose novel compositions along with their stable structures. We present FlowMM, a pair of generative models that achieve state-of-the-art performance on both tasks while being more efficient and more flexible than competing methods. We generalize Riemannian Flow Matching to suit the symmetries inherent to crystals: translation, rotation, permutation, and periodic boundary conditions. Our framework enables the freedom to choose the flow base distributions, drastically simplifying the problem of learning crystal structures compared with diffusion models. In addition to standard benchmarks, we validate FlowMM's generated structures with quantum chemistry calculations, demonstrating that it is about 3x more efficient, in terms of integration steps, at finding stable materials compared to previous open methods. | [
"['Benjamin Kurt Miller' 'Ricky T. Q. Chen' 'Anuroop Sriram'\n 'Brandon M Wood']"
] |
null | null | 2406.04724 | null | null | http://arxiv.org/pdf/2406.04724v1 | 2024-06-07T08:14:24Z | 2024-06-07T08:14:24Z | Probabilistic Perspectives on Error Minimization in Adversarial
Reinforcement Learning | Deep Reinforcement Learning (DRL) policies are critically vulnerable to adversarial noise in observations, posing severe risks in safety-critical scenarios. For example, a self-driving car receiving manipulated sensory inputs about traffic signs could lead to catastrophic outcomes. Existing strategies to fortify RL algorithms against such adversarial perturbations generally fall into two categories: (a) using regularization methods that enhance robustness by incorporating adversarial loss terms into the value objectives, and (b) adopting "maximin" principles, which focus on maximizing the minimum value to ensure robustness. While regularization methods reduce the likelihood of successful attacks, their effectiveness drops significantly if an attack does succeed. On the other hand, maximin objectives, although robust, tend to be overly conservative. To address this challenge, we introduce a novel objective called Adversarial Counterfactual Error (ACoE), which naturally balances optimizing value and robustness against adversarial attacks. To optimize ACoE in a scalable manner in model-free settings, we propose a theoretically justified surrogate objective known as Cumulative-ACoE (C-ACoE). The core idea of optimizing C-ACoE is utilizing the belief about the underlying true state given the adversarially perturbed observation. Our empirical evaluations demonstrate that our method outperforms current state-of-the-art approaches for addressing adversarial RL problems across all established benchmarks (MuJoCo, Atari, and Highway) used in the literature. | [
"['Roman Belaire' 'Arunesh Sinha' 'Pradeep Varakantham']"
] |
null | null | 2406.04727 | null | null | http://arxiv.org/pdf/2406.04727v1 | 2024-06-07T08:19:59Z | 2024-06-07T08:19:59Z | Predicting Polymer Properties Based on Multimodal Multitask Pretraining | In the past few decades, polymers, high-molecular-weight compounds formed by bonding numerous identical or similar monomers covalently, have played an essential role in various scientific fields. In this context, accurate prediction of their properties is becoming increasingly crucial. Typically, the properties of a polymer, such as plasticity, conductivity, bio-compatibility, and so on, are highly correlated with its 3D structure. However, current methods for predicting polymer properties heavily rely on information from polymer SMILES sequences (P-SMILES strings) while ignoring crucial 3D structural information, leading to sub-optimal performance. In this work, we propose MMPolymer, a novel multimodal multitask pretraining framework incorporating both polymer 1D sequential information and 3D structural information to enhance downstream polymer property prediction tasks. Besides, to overcome the limited availability of polymer 3D data, we further propose the "Star Substitution" strategy to extract 3D structural information effectively. During pretraining, MMPolymer not only predicts masked tokens and recovers 3D coordinates but also achieves the cross-modal alignment of latent representation. Subsequently, we further fine-tune the pretrained MMPolymer for downstream polymer property prediction tasks in the supervised learning paradigm. Experimental results demonstrate that MMPolymer achieves state-of-the-art performance in various polymer property prediction tasks. Moreover, leveraging the pretrained MMPolymer and using only one modality (either P-SMILES string or 3D conformation) during fine-tuning can also surpass existing polymer property prediction methods, highlighting the exceptional capability of MMPolymer in polymer feature extraction and utilization. Our online platform for polymer property prediction is available at https://app.bohrium.dp.tech/mmpolymer. | [
"['Fanmeng Wang' 'Wentao Guo' 'Minjie Cheng' 'Shen Yuan' 'Hongteng Xu'\n 'Zhifeng Gao']"
] |
null | null | 2406.04731 | null | null | http://arxiv.org/pdf/2406.04731v1 | 2024-06-07T08:26:31Z | 2024-06-07T08:26:31Z | Efficient Continual Finite-Sum Minimization | Given a sequence of functions $f_1,ldots,f_n$ with $f_i:mathcal{D}mapsto mathbb{R}$, finite-sum minimization seeks a point ${x}^star in mathcal{D}$ minimizing $sum_{j=1}^n f_j(x)/n$. In this work, we propose a key twist into the finite-sum minimization, dubbed as continual finite-sum minimization, that asks for a sequence of points ${x}_1^star,ldots,{x}_n^star in mathcal{D}$ such that each ${x}^star_i in mathcal{D}$ minimizes the prefix-sum $sum_{j=1}^if_j(x)/i$. Assuming that each prefix-sum is strongly convex, we develop a first-order continual stochastic variance reduction gradient method ($mathrm{CSVRG}$) producing an $epsilon$-optimal sequence with $mathcal{tilde{O}}(n/epsilon^{1/3} + 1/sqrt{epsilon})$ overall first-order oracles (FO). An FO corresponds to the computation of a single gradient $nabla f_j(x)$ at a given $x in mathcal{D}$ for some $j in [n]$. Our approach significantly improves upon the $mathcal{O}(n/epsilon)$ FOs that $mathrm{StochasticGradientDescent}$ requires and the $mathcal{O}(n^2 log (1/epsilon))$ FOs that state-of-the-art variance reduction methods such as $mathrm{Katyusha}$ require. We also prove that there is no natural first-order method with $mathcal{O}left(n/epsilon^alpharight)$ gradient complexity for $alpha < 1/4$, establishing that the first-order complexity of our method is nearly tight. | [
"['Ioannis Mavrothalassitis' 'Stratis Skoulakis' 'Leello Tadesse Dadi'\n 'Volkan Cevher']"
] |
null | null | 2406.04739 | null | null | http://arxiv.org/pdf/2406.04739v1 | 2024-06-07T08:39:40Z | 2024-06-07T08:39:40Z | A survey and benchmark of high-dimensional Bayesian optimization of
discrete sequences | Optimizing discrete black-box functions is key in several domains, e.g. protein engineering and drug design. Due to the lack of gradient information and the need for sample efficiency, Bayesian optimization is an ideal candidate for these tasks. Several methods for high-dimensional continuous and categorical Bayesian optimization have been proposed recently. However, our survey of the field reveals highly heterogeneous experimental set-ups across methods and technical barriers for the replicability and application of published algorithms to real-world tasks. To address these issues, we develop a unified framework to test a vast array of high-dimensional Bayesian optimization methods and a collection of standardized black-box functions representing real-world application domains in chemistry and biology. These two components of the benchmark are each supported by flexible, scalable, and easily extendable software libraries (poli and poli-baselines), allowing practitioners to readily incorporate new optimization objectives or discrete optimizers. Project website: https://machinelearninglifescience.github.io/hdbo_benchmark | [
"['Miguel González-Duque' 'Richard Michael' 'Simon Bartels'\n 'Yevgen Zainchkovskyy' 'Søren Hauberg' 'Wouter Boomsma']"
] |
null | null | 2406.04743 | null | null | http://arxiv.org/pdf/2406.04743v1 | 2024-06-07T08:42:26Z | 2024-06-07T08:42:26Z | When Swarm Learning meets energy series data: A decentralized
collaborative learning design based on blockchain | Machine learning models offer the capability to forecast future energy production or consumption and infer essential unknown variables from existing data. However, legal and policy constraints within specific energy sectors render the data sensitive, presenting technical hurdles in utilizing data from diverse sources. Therefore, we propose adopting a Swarm Learning (SL) scheme, which replaces the centralized server with a blockchain-based distributed network to address the security and privacy issues inherent in Federated Learning (FL)'s centralized architecture. Within this distributed Collaborative Learning framework, each participating organization governs nodes for inter-organizational communication. Devices from various organizations utilize smart contracts for parameter uploading and retrieval. Consensus mechanism ensures distributed consistency throughout the learning process, guarantees the transparent trustworthiness and immutability of parameters on-chain. The efficacy of the proposed framework is substantiated across three real-world energy series modeling scenarios with superior performance compared to Local Learning approaches, simultaneously emphasizing enhanced data security and privacy over Centralized Learning and FL method. Notably, as the number of data volume and the count of local epochs increases within a threshold, there is an improvement in model performance accompanied by a reduction in the variance of performance errors. Consequently, this leads to an increased stability and reliability in the outcomes produced by the model. | [
"['Lei Xu' 'Yulong Chen' 'Yuntian Chen' 'Longfeng Nie' 'Xuetao Wei'\n 'Liang Xue' 'Dongxiao Zhang']"
] |
null | null | 2406.04745 | null | null | http://arxiv.org/pdf/2406.04745v1 | 2024-06-07T08:43:53Z | 2024-06-07T08:43:53Z | Confidence-aware Contrastive Learning for Selective Classification | Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks and focus on modifying the architecture of classification layers to enable the model to estimate the confidence of its prediction. This work provides a generalization bound for selective classification, disclosing that optimizing feature layers helps improve the performance of selective classification. Inspired by this theory, we propose to explicitly improve the selective classification model at the feature level for the first time, leading to a novel Confidence-aware Contrastive Learning method for Selective Classification, CCL-SC, which similarizes the features of homogeneous instances and differentiates the features of heterogeneous instances, with the strength controlled by the model's confidence. The experimental results on typical datasets, i.e., CIFAR-10, CIFAR-100, CelebA, and ImageNet, show that CCL-SC achieves significantly lower selective risk than state-of-the-art methods, across almost all coverage degrees. Moreover, it can be combined with existing methods to bring further improvement. | [
"['Yu-Chang Wu' 'Shen-Huan Lyu' 'Haopu Shang' 'Xiangyu Wang' 'Chao Qian']"
] |
null | null | 2406.04746 | null | null | http://arxiv.org/pdf/2406.04746v1 | 2024-06-07T08:46:19Z | 2024-06-07T08:46:19Z | PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance
Prediction | Text-to-image generation has recently emerged as a viable alternative to text-to-image retrieval, due to the visually impressive results of generative diffusion models. Although query performance prediction is an active research topic in information retrieval, to the best of our knowledge, there is no prior study that analyzes the difficulty of queries (prompts) in text-to-image generation, based on human judgments. To this end, we introduce the first dataset of prompts which are manually annotated in terms of image generation performance. In order to determine the difficulty of the same prompts in image retrieval, we also collect manual annotations that represent retrieval performance. We thus propose the first benchmark for joint text-to-image prompt and query performance prediction, comprising 10K queries. Our benchmark enables: (i) the comparative assessment of the difficulty of prompts/queries in image generation and image retrieval, and (ii) the evaluation of prompt/query performance predictors addressing both generation and retrieval. We present results with several pre-generation/retrieval and post-generation/retrieval performance predictors, thus providing competitive baselines for future research. Our benchmark and code is publicly available under the CC BY 4.0 license at https://github.com/Eduard6421/PQPP. | [
"['Eduard Poesina' 'Adriana Valentina Costache' 'Adrian-Gabriel Chifu'\n 'Josiane Mothe' 'Radu Tudor Ionescu']"
] |
null | null | 2406.04755 | null | null | http://arxiv.org/pdf/2406.04755v1 | 2024-06-07T08:54:55Z | 2024-06-07T08:54:55Z | Sales Whisperer: A Human-Inconspicuous Attack on LLM Brand
Recommendations | Large language model (LLM) users might rely on others (e.g., prompting services), to write prompts. However, the risks of trusting prompts written by others remain unstudied. In this paper, we assess the risk of using such prompts on brand recommendation tasks when shopping. First, we found that paraphrasing prompts can result in LLMs mentioning given brands with drastically different probabilities, including a pair of prompts where the probability changes by 100%. Next, we developed an approach that can be used to perturb an original base prompt to increase the likelihood that an LLM mentions a given brand. We designed a human-inconspicuous algorithm that perturbs prompts, which empirically forces LLMs to mention strings related to a brand more often, by absolute improvements up to 78.3%. Our results suggest that our perturbed prompts, 1) are inconspicuous to humans, 2) force LLMs to recommend a target brand more often, and 3) increase the perceived chances of picking targeted brands. | [
"['Weiran Lin' 'Anna Gerchanovsky' 'Omer Akgul' 'Lujo Bauer'\n 'Matt Fredrikson' 'Zifan Wang']"
] |
null | null | 2406.04759 | null | null | http://arxiv.org/pdf/2406.04759v1 | 2024-06-07T09:01:25Z | 2024-06-07T09:01:25Z | Probabilistic Weather Forecasting with Hierarchical Graph Neural
Networks | In recent years, machine learning has established itself as a powerful tool for high-resolution weather forecasting. While most current machine learning models focus on deterministic forecasts, accurately capturing the uncertainty in the chaotic weather system calls for probabilistic modeling. We propose a probabilistic weather forecasting model called Graph-EFM, combining a flexible latent-variable formulation with the successful graph-based forecasting framework. The use of a hierarchical graph construction allows for efficient sampling of spatially coherent forecasts. Requiring only a single forward pass per time step, Graph-EFM allows for fast generation of arbitrarily large ensembles. We experiment with the model on both global and limited area forecasting. Ensemble forecasts from Graph-EFM achieve equivalent or lower errors than comparable deterministic models, with the added benefit of accurately capturing forecast uncertainty. | [
"['Joel Oskarsson' 'Tomas Landelius' 'Marc Peter Deisenroth'\n 'Fredrik Lindsten']"
] |
null | null | 2406.04766 | null | null | http://arxiv.org/pdf/2406.04766v1 | 2024-06-07T09:09:14Z | 2024-06-07T09:09:14Z | Reinforcement Learning and Regret Bounds for Admission Control | The expected regret of any reinforcement learning algorithm is lower bounded by $Omegaleft(sqrt{DXAT}right)$ for undiscounted returns, where $D$ is the diameter of the Markov decision process, $X$ the size of the state space, $A$ the size of the action space and $T$ the number of time steps. However, this lower bound is general. A smaller regret can be obtained by taking into account some specific knowledge of the problem structure. In this article, we consider an admission control problem to an $M/M/c/S$ queue with $m$ job classes and class-dependent rewards and holding costs. Queuing systems often have a diameter that is exponential in the buffer size $S$, making the previous lower bound prohibitive for any practical use. We propose an algorithm inspired by UCRL2, and use the structure of the problem to upper bound the expected total regret by $O(Slog T + sqrt{mT log T})$ in the finite server case. In the infinite server case, we prove that the dependence of the regret on $S$ disappears. | [
"['Lucas Weber' 'Ana Bušić' 'Jiamin Zhu']"
] |
null | null | 2406.04769 | null | null | http://arxiv.org/pdf/2406.04769v1 | 2024-06-07T09:15:29Z | 2024-06-07T09:15:29Z | Diffusion-based Generative Image Outpainting for Recovery of
FOV-Truncated CT Images | Field-of-view (FOV) recovery of truncated chest CT scans is crucial for accurate body composition analysis, which involves quantifying skeletal muscle and subcutaneous adipose tissue (SAT) on CT slices. This, in turn, enables disease prognostication. Here, we present a method for recovering truncated CT slices using generative image outpainting. We train a diffusion model and apply it to truncated CT slices generated by simulating a small FOV. Our model reliably recovers the truncated anatomy and outperforms the previous state-of-the-art despite being trained on 87% less data. | [
"['Michelle Espranita Liman' 'Daniel Rueckert' 'Florian J. Fintelmann'\n 'Philip Müller']"
] |
null | null | 2406.04772 | null | null | http://arxiv.org/pdf/2406.04772v1 | 2024-06-07T09:17:33Z | 2024-06-07T09:17:33Z | REP: Resource-Efficient Prompting for On-device Continual Learning | On-device continual learning (CL) requires the co-optimization of model accuracy and resource efficiency to be practical. This is extremely challenging because it must preserve accuracy while learning new tasks with continuously drifting data and maintain both high energy and memory efficiency to be deployable on real-world devices. Typically, a CL method leverages one of two types of backbone networks: CNN or ViT. It is commonly believed that CNN-based CL excels in resource efficiency, whereas ViT-based CL is superior in model performance, making each option attractive only for a single aspect. In this paper, we revisit this comparison while embracing powerful pre-trained ViT models of various sizes, including ViT-Ti (5.8M parameters). Our detailed analysis reveals that many practical options exist today for making ViT-based methods more suitable for on-device CL, even when accuracy, energy, and memory are all considered. To further expand this impact, we introduce REP, which improves resource efficiency specifically targeting prompt-based rehearsal-free methods. Our key focus is on avoiding catastrophic trade-offs with accuracy while trimming computational and memory costs throughout the training process. We achieve this by exploiting swift prompt selection that enhances input data using a carefully provisioned model, and by developing two novel algorithms-adaptive token merging (AToM) and adaptive layer dropping (ALD)-that optimize the prompt updating stage. In particular, AToM and ALD perform selective skipping across the data and model-layer dimensions without compromising task-specific features in vision transformer models. Extensive experiments on three image classification datasets validate REP's superior resource efficiency over current state-of-the-art methods. | [
"['Sungho Jeon' 'Xinyue Ma' 'Kwang In Kim' 'Myeongjae Jeon']"
] |
null | null | 2406.04777 | null | null | http://arxiv.org/pdf/2406.04777v1 | 2024-06-07T09:21:06Z | 2024-06-07T09:21:06Z | TDT Loss Takes It All: Integrating Temporal Dependencies among Targets
into Non-Autoregressive Time Series Forecasting | Learning temporal dependencies among targets (TDT) benefits better time series forecasting, where targets refer to the predicted sequence. Although autoregressive methods model TDT recursively, they suffer from inefficient inference and error accumulation. We argue that integrating TDT learning into non-autoregressive methods is essential for pursuing effective and efficient time series forecasting. In this study, we introduce the differencing approach to represent TDT and propose a parameter-free and plug-and-play solution through an optimization objective, namely TDT Loss. It leverages the proportion of inconsistent signs between predicted and ground truth TDT as an adaptive weight, dynamically balancing target prediction and fine-grained TDT fitting. Importantly, TDT Loss incurs negligible additional cost, with only $mathcal{O}(n)$ increased computation and $mathcal{O}(1)$ memory requirements, while significantly enhancing the predictive performance of non-autoregressive models. To assess the effectiveness of TDT loss, we conduct extensive experiments on 7 widely used datasets. The experimental results of plugging TDT loss into 6 state-of-the-art methods show that out of the 168 experiments, 75.00% and 94.05% exhibit improvements in terms of MSE and MAE with the maximum 24.56% and 16.31%, respectively. | [
"['Qi Xiong' 'Kai Tang' 'Minbo Ma' 'Jie Xu' 'Tianrui Li']"
] |
null | null | 2406.04779 | null | null | http://arxiv.org/pdf/2406.04779v1 | 2024-06-07T09:28:18Z | 2024-06-07T09:28:18Z | Mobile Network Configuration Recommendation using Deep Generative Graph
Neural Network | There are vast number of configurable parameters in a Radio Access Telecom Network. A significant amount of these parameters is configured by Radio Node or cell based on their deployment setting. Traditional methods rely on domain knowledge for individual parameter configuration, often leading to sub-optimal results. To improve this, a framework using a Deep Generative Graph Neural Network (GNN) is proposed. It encodes the network into a graph, extracts subgraphs for each RAN node, and employs a Siamese GNN (S-GNN) to learn embeddings. The framework recommends configuration parameters for a multitude of parameters and detects misconfigurations, handling both network expansion and existing cell reconfiguration. Tested on real-world data, the model surpasses baselines, demonstrating accuracy, generalizability, and robustness against concept drift. | [
"['Shirwan Piroti' 'Ashima Chawla' 'Tahar Zanouda']"
] |
null | null | 2406.04793 | null | null | http://arxiv.org/pdf/2406.04793v1 | 2024-06-07T09:40:09Z | 2024-06-07T09:40:09Z | Learning-Augmented Priority Queues | Priority queues are one of the most fundamental and widely used data structures in computer science. Their primary objective is to efficiently support the insertion of new elements with assigned priorities and the extraction of the highest priority element. In this study, we investigate the design of priority queues within the learning-augmented framework, where algorithms use potentially inaccurate predictions to enhance their worst-case performance. We examine three prediction models spanning different use cases, and show how the predictions can be leveraged to enhance the performance of priority queue operations. Moreover, we demonstrate the optimality of our solution and discuss some possible applications. | [
"['Ziyad Benomar' 'Christian Coester']"
] |
null | null | 2406.04802 | null | null | http://arxiv.org/pdf/2406.04802v2 | 2024-07-13T06:58:05Z | 2024-06-07T10:06:13Z | Predictive Dynamic Fusion | Multimodal fusion is crucial in joint decision-making systems for rendering holistic judgments. Since multimodal data changes in open environments, dynamic fusion has emerged and achieved remarkable progress in numerous applications. However, most existing dynamic multimodal fusion methods lack theoretical guarantees and easily fall into suboptimal problems, yielding unreliability and instability. To address this issue, we propose a Predictive Dynamic Fusion (PDF) framework for multimodal learning. We proceed to reveal the multimodal fusion from a generalization perspective and theoretically derive the predictable Collaborative Belief (Co-Belief) with Mono- and Holo-Confidence, which provably reduces the upper bound of generalization error. Accordingly, we further propose a relative calibration strategy to calibrate the predicted Co-Belief for potential uncertainty. Extensive experiments on multiple benchmarks confirm our superiority. Our code is available at https://github.com/Yinan-Xia/PDF. | [
"['Bing Cao' 'Yinan Xia' 'Yi Ding' 'Changqing Zhang' 'Qinghua Hu']"
] |
null | null | 2406.04805 | null | null | http://arxiv.org/pdf/2406.04805v1 | 2024-06-07T10:12:01Z | 2024-06-07T10:12:01Z | GENIE: Watermarking Graph Neural Networks for Link Prediction | Graph Neural Networks (GNNs) have advanced the field of machine learning by utilizing graph-structured data, which is ubiquitous in the real world. GNNs have applications in various fields, ranging from social network analysis to drug discovery. GNN training is strenuous, requiring significant computational resources and human expertise. It makes a trained GNN an indispensable Intellectual Property (IP) for its owner. Recent studies have shown GNNs to be vulnerable to model-stealing attacks, which raises concerns over IP rights protection. Watermarking has been shown to be effective at protecting the IP of a GNN model. Existing efforts to develop a watermarking scheme for GNNs have only focused on the node classification and the graph classification tasks. To the best of our knowledge, we introduce the first-ever watermarking scheme for GNNs tailored to the Link Prediction (LP) task. We call our proposed watermarking scheme GENIE (watermarking Graph nEural Networks for lInk prEdiction). We design GENIE using a novel backdoor attack to create a trigger set for two key methods of LP: (1) node representation-based and (2) subgraph-based. In GENIE, the watermark is embedded into the GNN model by training it on both the trigger set and a modified training set, resulting in a watermarked GNN model. To assess a suspect model, we verify the watermark against the trigger set. We extensively evaluate GENIE across 3 model architectures (i.e., SEAL, GCN, and GraphSAGE) and 7 real-world datasets. Furthermore, we validate the robustness of GENIE against 11 state-of-the-art watermark removal techniques and 3 model extraction attacks. We also demonstrate that GENIE is robust against ownership piracy attack. Our ownership demonstration scheme statistically guarantees both False Positive Rate (FPR) and False Negative Rate (FNR) to be less than $10^{-6}$. | [
"['Venkata Sai Pranav Bachina' 'Ankit Gangwal' 'Aaryan Ajay Sharma'\n 'Charu Sharma']"
] |
null | null | 2406.04808 | null | null | http://arxiv.org/pdf/2406.04808v1 | 2024-06-07T10:23:03Z | 2024-06-07T10:23:03Z | VERA: Generating Visual Explanations of Two-Dimensional Embeddings via
Region Annotation | Two-dimensional embeddings obtained from dimensionality reduction techniques, such as MDS, t-SNE, and UMAP, are widely used across various disciplines to visualize high-dimensional data. These visualizations provide a valuable tool for exploratory data analysis, allowing researchers to visually identify clusters, outliers, and other interesting patterns in the data. However, interpreting the resulting visualizations can be challenging, as it often requires additional manual inspection to understand the differences between data points in different regions of the embedding space. To address this issue, we propose Visual Explanations via Region Annotation (VERA), an automatic embedding-annotation approach that generates visual explanations for any two-dimensional embedding. VERA produces informative explanations that characterize distinct regions in the embedding space, allowing users to gain an overview of the embedding landscape at a glance. Unlike most existing approaches, which typically require some degree of manual user intervention, VERA produces static explanations, automatically identifying and selecting the most informative visual explanations to show to the user. We illustrate the usage of VERA on a real-world data set and validate the utility of our approach with a comparative user study. Our results demonstrate that the explanations generated by VERA are as useful as fully-fledged interactive tools on typical exploratory data analysis tasks but require significantly less time and effort from the user. | [
"['Pavlin G. Poličar' 'Blaž Zupan']"
] |
null | null | 2406.04812 | null | null | http://arxiv.org/pdf/2406.04812v1 | 2024-06-07T10:27:07Z | 2024-06-07T10:27:07Z | Generating Piano Practice Policy with a Gaussian Process | A typical process of learning to play a piece on a piano consists of a progression through a series of practice units that focus on individual dimensions of the skill, the so-called practice modes. Practice modes in learning to play music comprise a particularly large set of possibilities, such as hand coordination, posture, articulation, ability to read a music score, correct timing or pitch, etc. Self-guided practice is known to be suboptimal, and a model that schedules optimal practice to maximize a learner's progress still does not exist. Because we each learn differently and there are many choices for possible piano practice tasks and methods, the set of practice modes should be dynamically adapted to the human learner, a process typically guided by a teacher. However, having a human teacher guide individual practice is not always feasible since it is time-consuming, expensive, and often unavailable. In this work, we present a modeling framework to guide the human learner through the learning process by choosing the practice modes generated by a policy model. To this end, we present a computational architecture building on a Gaussian process that incorporates 1) the learner state, 2) a policy that selects a suitable practice mode, 3) performance evaluation, and 4) expert knowledge. The proposed policy model is trained to approximate the expert-learner interaction during a practice session. In our future work, we will test different Bayesian optimization techniques, e.g., different acquisition functions, and evaluate their effect on the learning progress. | [
"['Alexandra Moringen' 'Elad Vromen' 'Helge Ritter' 'Jason Friedman']"
] |
null | null | 2406.04814 | null | null | http://arxiv.org/pdf/2406.04814v1 | 2024-06-07T10:32:23Z | 2024-06-07T10:32:23Z | Online Continual Learning of Video Diffusion Models From a Single Video
Stream | Diffusion models have shown exceptional capabilities in generating realistic videos. Yet, their training has been predominantly confined to offline environments where models can repeatedly train on i.i.d. data to convergence. This work explores the feasibility of training diffusion models from a semantically continuous video stream, where correlated video frames sequentially arrive one at a time. To investigate this, we introduce two novel continual video generative modeling benchmarks, Lifelong Bouncing Balls and Windows 95 Maze Screensaver, each containing over a million video frames generated from navigating stationary environments. Surprisingly, our experiments show that diffusion models can be effectively trained online using experience replay, achieving performance comparable to models trained with i.i.d. samples given the same number of gradient steps. | [
"['Jason Yoo' 'Dylan Green' 'Geoff Pleiss' 'Frank Wood']"
] |
null | null | 2406.04815 | null | null | http://arxiv.org/pdf/2406.04815v1 | 2024-06-07T10:35:29Z | 2024-06-07T10:35:29Z | Skill-aware Mutual Information Optimisation for Generalisation in
Reinforcement Learning | Meta-Reinforcement Learning (Meta-RL) agents can struggle to operate across tasks with varying environmental features that require different optimal skills (i.e., different modes of behaviours). Using context encoders based on contrastive learning to enhance the generalisability of Meta-RL agents is now widely studied but faces challenges such as the requirement for a large sample size, also referred to as the $log$-$K$ curse. To improve RL generalisation to different tasks, we first introduce Skill-aware Mutual Information (SaMI), an optimisation objective that aids in distinguishing context embeddings according to skills, thereby equipping RL agents with the ability to identify and execute different skills across tasks. We then propose Skill-aware Noise Contrastive Estimation (SaNCE), a $K$-sample estimator used to optimise the SaMI objective. We provide a framework for equipping an RL agent with SaNCE in practice and conduct experimental validation on modified MuJoCo and Panda-gym benchmarks. We empirically find that RL agents that learn by maximising SaMI achieve substantially improved zero-shot generalisation to unseen tasks. Additionally, the context encoder equipped with SaNCE demonstrates greater robustness to reductions in the number of available samples, thus possessing the potential to overcome the $log$-$K$ curse. | [
"['Xuehui Yu' 'Mhairi Dunion' 'Xin Li' 'Stefano V. Albrecht']"
] |
null | null | 2406.04822 | null | null | http://arxiv.org/pdf/2406.04822v1 | 2024-06-07T10:47:40Z | 2024-06-07T10:47:40Z | M2NO: Multiresolution Operator Learning with Multiwavelet-based
Algebraic Multigrid Method | Solving partial differential equations (PDEs) effectively necessitates a multi-scale approach, particularly critical in high-dimensional scenarios characterized by increasing grid points or resolution. Traditional methods often fail to capture the detailed features necessary for accurate modeling, presenting a significant challenge in scientific computing. In response, we introduce the Multiwavelet-based Algebraic Multigrid Neural Operator (M2NO), a novel deep learning framework that synergistically combines multiwavelet transformations and algebraic multigrid (AMG) techniques. By exploiting the inherent similarities between these two approaches, M2NO overcomes their individual limitations and enhances precision and flexibility across various PDE benchmarks. Employing Multiresolution Analysis (MRA) with high-pass and low-pass filters, the model executes hierarchical decomposition to accurately delineate both global trends and localized details within PDE solutions, supporting adaptive data representation at multiple scales. M2NO also automates node selection and adeptly manages complex boundary conditions through its multiwavelet-based operators. Extensive evaluations on a diverse array of PDE datasets with different boundary conditions confirm M2NO's superior performance. Furthermore, M2NO excels in handling high-resolution and super-resolution tasks, consistently outperforming competing models and demonstrating robust adaptability in complex computational scenarios. | [
"['Zhihao Li' 'Zhilu Lai' 'Xiaobo Wang' 'Wei Wang']"
] |
null | null | 2406.04824 | null | null | http://arxiv.org/pdf/2406.04824v2 | 2024-07-01T04:48:24Z | 2024-06-07T10:49:59Z | FunBO: Discovering Acquisition Functions for Bayesian Optimization with
FunSearch | The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations. The best-performing AF can vary significantly across optimization problems, often requiring ad-hoc and problem-specific choices. This work tackles the challenge of designing novel AFs that perform well across a variety of experimental settings. Based on FunSearch, a recent work using Large Language Models (LLMs) for discovery in mathematical sciences, we propose FunBO, an LLM-based method that can be used to learn new AFs written in computer code by leveraging access to a limited number of evaluations for a set of objective functions. We provide the analytic expression of all discovered AFs and evaluate them on various global optimization benchmarks and hyperparameter optimization tasks. We show how FunBO identifies AFs that generalize well in and out of the training distribution of functions, thus outperforming established general-purpose AFs and achieving competitive performance against AFs that are customized to specific function types and are learned via transfer-learning algorithms. | [
"['Virginia Aglietti' 'Ira Ktena' 'Jessica Schrouff' 'Eleni Sgouritsa'\n 'Francisco J. R. Ruiz' 'Alan Malek' 'Alexis Bellot' 'Silvia Chiappa']"
] |
null | null | 2406.04825 | null | null | http://arxiv.org/pdf/2406.04825v2 | 2024-06-11T13:33:16Z | 2024-06-07T10:50:03Z | Graph Mining under Data scarcity | Multitude of deep learning models have been proposed for node classification in graphs. However, they tend to perform poorly under labeled-data scarcity. Although Few-shot learning for graphs has been introduced to overcome this problem, the existing models are not easily adaptable for generic graph learning frameworks like Graph Neural Networks (GNNs). Our work proposes an Uncertainty Estimator framework that can be applied on top of any generic GNN backbone network (which are typically designed for supervised/semi-supervised node classification) to improve the node classification performance. A neural network is used to model the Uncertainty Estimator as a probability distribution rather than probabilistic discrete scalar values. We train these models under the classic episodic learning paradigm in the $n$-way, $k$-shot fashion, in an end-to-end setting. Our work demonstrates that implementation of the uncertainty estimator on a GNN backbone network improves the classification accuracy under Few-shot setting without any meta-learning specific architecture. We conduct experiments on multiple datasets under different Few-shot settings and different GNN-based backbone networks. Our method outperforms the baselines, which demonstrates the efficacy of the Uncertainty Estimator for Few-shot node classification on graphs with a GNN. | [
"['Appan Rakaraddi' 'Lam Siew-Kei' 'Mahardhika Pratama'\n 'Marcus de Carvalho']"
] |
null | null | 2406.04827 | null | null | http://arxiv.org/pdf/2406.04827v2 | 2024-07-05T21:38:38Z | 2024-06-07T10:52:15Z | Black Box Differential Privacy Auditing Using Total Variation Distance | We present a practical method to audit the differential privacy (DP) guarantees of a machine learning model using a small hold-out dataset that is not exposed to the model during the training. Having a score function such as the loss function employed during the training, our method estimates the total variation (TV) distance between scores obtained with a subset of the training data and the hold-out dataset. With some meta information about the underlying DP training algorithm, these TV distance values can be converted to $(varepsilon,delta)$-guarantees for any $delta$. We show that these score distributions asymptotically give lower bounds for the DP guarantees of the underlying training algorithm, however, we perform a one-shot estimation for practicality reasons. We specify conditions that lead to lower bounds for the DP guarantees with high probability. To estimate the TV distance between the score distributions, we use a simple density estimation method based on histograms. We show that the TV distance gives a very close to optimally robust estimator and has an error rate $mathcal{O}(k^{-1/3})$, where $k$ is the total number of samples. Numerical experiments on benchmark datasets illustrate the effectiveness of our approach and show improvements over baseline methods for black-box auditing. | [
"['Antti Koskela' 'Jafar Mohammadi']"
] |
null | null | 2406.04838 | null | null | http://arxiv.org/pdf/2406.04838v1 | 2024-06-07T11:10:07Z | 2024-06-07T11:10:07Z | Algorithms for learning value-aligned policies considering admissibility
relaxation | The emerging field of emph{value awareness engineering} claims that software agents and systems should be value-aware, i.e. they must make decisions in accordance with human values. In this context, such agents must be capable of explicitly reasoning as to how far different courses of action are aligned with these values. For this purpose, values are often modelled as preferences over states or actions, which are then aggregated to determine the sequences of actions that are maximally aligned with a certain value. Recently, additional value admissibility constraints at this level have been considered as well. However, often relaxed versions of these constraints are needed, and this increases considerably the complexity of computing value-aligned policies. To obtain efficient algorithms that make value-aligned decisions considering admissibility relaxation, we propose the use of learning techniques, in particular, we have used constrained reinforcement learning algorithms. In this paper, we present two algorithms, $epsilontext{-}ADQL$ for strategies based on local alignment and its extension $epsilontext{-}CADQL$ for a sequence of decisions. We have validated their efficiency in a water distribution problem in a drought scenario. | [
"['Andrés Holgado-Sánchez' 'Joaquín Arias' 'Holger Billhardt'\n 'Sascha Ossowski']"
] |
null | null | 2406.04841 | null | null | http://arxiv.org/pdf/2406.04841v1 | 2024-06-07T11:13:38Z | 2024-06-07T11:13:38Z | Primitive Agentic First-Order Optimization | Efficient numerical optimization methods can improve performance and reduce the environmental impact of computing in many applications. This work presents a proof-of-concept study combining primitive state representations and agent-environment interactions as first-order optimizers in the setting of budget-limited optimization. Through reinforcement learning (RL) over a set of training instances of an optimization problem class, optimal policies for sequential update selection of algorithmic iteration steps are approximated in generally formulated low-dimensional partial state representations that consider aspects of progress and resource use. For the investigated case studies, deployment of the trained agents to unseen instances of the quadratic optimization problem classes outperformed conventional optimal algorithms with optimized hyperparameters. The results show that elementary RL methods combined with succinct partial state representations can be used as heuristics to manage complexity in RL-based optimization, paving the way for agentic optimization approaches. | [
"['R. Sala']"
] |
null | null | 2406.04843 | null | null | http://arxiv.org/pdf/2406.04843v1 | 2024-06-07T11:16:17Z | 2024-06-07T11:16:17Z | Variational Flow Matching for Graph Generation | We present a formulation of flow matching as variational inference, which we refer to as variational flow matching (VFM). Based on this formulation we develop CatFlow, a flow matching method for categorical data. CatFlow is easy to implement, computationally efficient, and achieves strong results on graph generation tasks. In VFM, the objective is to approximate the posterior probability path, which is a distribution over possible end points of a trajectory. We show that VFM admits both the CatFlow objective and the original flow matching objective as special cases. We also relate VFM to score-based models, in which the dynamics are stochastic rather than deterministic, and derive a bound on the model likelihood based on a reweighted VFM objective. We evaluate CatFlow on one abstract graph generation task and two molecular generation tasks. In all cases, CatFlow exceeds or matches performance of the current state-of-the-art models. | [
"['Floor Eijkelboom' 'Grigory Bartosh' 'Christian Andersson Naesseth'\n 'Max Welling' 'Jan-Willem van de Meent']"
] |
null | null | 2406.04845 | null | null | http://arxiv.org/pdf/2406.04845v1 | 2024-06-07T11:19:30Z | 2024-06-07T11:19:30Z | FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large
Language Models | Federated learning has enabled multiple parties to collaboratively train large language models without directly sharing their data (FedLLM). Following this training paradigm, the community has put massive efforts from diverse aspects including framework, performance, and privacy. However, an unpleasant fact is that there are currently no realistic datasets and benchmarks for FedLLM and previous works all rely on artificially constructed datasets, failing to capture properties in real-world scenarios. Addressing this, we propose FedLLM-Bench, which involves 8 training methods, 4 training datasets, and 6 evaluation metrics, to offer a comprehensive testbed for the FedLLM community. FedLLM-Bench encompasses three datasets (e.g., user-annotated multilingual dataset) for federated instruction tuning and one dataset (e.g., user-annotated preference dataset) for federated preference alignment, whose scale of client number ranges from 38 to 747. Our datasets incorporate several representative diversities: language, quality, quantity, instruction, length, embedding, and preference, capturing properties in real-world scenarios. Based on FedLLM-Bench, we conduct experiments on all datasets to benchmark existing FL methods and provide empirical insights (e.g., multilingual collaboration). We believe that our FedLLM-Bench can benefit the FedLLM community by reducing required efforts, providing a practical testbed, and promoting fair comparisons. Code and datasets are available at https://github.com/rui-ye/FedLLM-Bench. | [
"['Rui Ye' 'Rui Ge' 'Xinyu Zhu' 'Jingyi Chai' 'Yaxin Du' 'Yang Liu'\n 'Yanfeng Wang' 'Siheng Chen']"
] |
null | null | 2406.04848 | null | null | http://arxiv.org/pdf/2406.04848v1 | 2024-06-07T11:27:18Z | 2024-06-07T11:27:18Z | CTBENCH: A Library and Benchmark for Certified Training | Training certifiably robust neural networks is an important but challenging task. While many algorithms for (deterministic) certified training have been proposed, they are often evaluated on different training schedules, certification methods, and systematically under-tuned hyperparameters, making it difficult to compare their performance. To address this challenge, we introduce CTBENCH, a unified library and a high-quality benchmark for certified training that evaluates all algorithms under fair settings and systematically tuned hyperparameters. We show that (1) almost all algorithms in CTBENCH surpass the corresponding reported performance in literature in the magnitude of algorithmic improvements, thus establishing new state-of-the-art, and (2) the claimed advantage of recent algorithms drops significantly when we enhance the outdated baselines with a fair training schedule, a fair certification method and well-tuned hyperparameters. Based on CTBENCH, we provide new insights into the current state of certified training and suggest future research directions. We are confident that CTBENCH will serve as a benchmark and testbed for future research in certified training. | [
"['Yuhao Mao' 'Stefan Balauca' 'Martin Vechev']"
] |
null | null | 2406.04853 | null | null | http://arxiv.org/pdf/2406.04853v1 | 2024-06-07T11:35:15Z | 2024-06-07T11:35:15Z | Time-Series JEPA for Predictive Remote Control under Capacity-Limited
Networks | In remote control systems, transmitting large data volumes (e.g. video feeds) from wireless sensors to faraway controllers is challenging when the uplink channel capacity is limited (e.g. RedCap devices or massive wireless sensor networks). Furthermore, the controllers often only need the information-rich components of the original data. To address this, we propose a Time-Series Joint Embedding Predictive Architecture (TS-JEPA) and a semantic actor trained through self-supervised learning. This approach harnesses TS-JEPA's semantic representation power and predictive capabilities by capturing spatio-temporal correlations in the source data. We leverage this to optimize uplink channel utilization, while the semantic actor calculates control commands directly from the encoded representations, rather than from the original data. We test our model through multiple parallel instances of the well-known inverted cart-pole scenario, where the approach is validated through the maximization of stability under constrained uplink channel capacity. | [
"['Abanoub M. Girgis' 'Alvaro Valcarce' 'Mehdi Bennis']"
] |
null | null | 2406.04857 | null | null | http://arxiv.org/pdf/2406.04857v1 | 2024-06-07T11:40:54Z | 2024-06-07T11:40:54Z | A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph
Clustering | We consider the semi-random graph model of [Makarychev, Makarychev and Vijayaraghavan, STOC'12], where, given a random bipartite graph with $alpha$ edges and an unknown bipartition $(A, B)$ of the vertex set, an adversary can add arbitrary edges inside each community and remove arbitrary edges from the cut $(A, B)$ (i.e. all adversarial changes are textit{monotone} with respect to the bipartition). For this model, a polynomial time algorithm is known to approximate the Balanced Cut problem up to value $O(alpha)$ [MMV'12] as long as the cut $(A, B)$ has size $Omega(alpha)$. However, it consists of slow subroutines requiring optimal solutions for logarithmically many semidefinite programs. We study the fine-grained complexity of the problem and present the first near-linear time algorithm that achieves similar performances to that of [MMV'12]. Our algorithm runs in time $O(|V(G)|^{1+o(1)} + |E(G)|^{1+o(1)})$ and finds a balanced cut of value $O(alpha)$. Our approach appears easily extendible to related problem, such as Sparsest Cut, and also yields an near-linear time $O(1)$-approximation to Dagupta's objective function for hierarchical clustering [Dasgupta, STOC'16] for the semi-random hierarchical stochastic block model inputs of [Cohen-Addad, Kanade, Mallmann-Trenn, Mathieu, JACM'19]. | [
"['Vincent Cohen-Addad' \"Tommaso d'Orsi\" 'Aida Mousavifar']"
] |
null | null | 2406.04859 | null | null | http://arxiv.org/pdf/2406.04859v1 | 2024-06-07T11:44:50Z | 2024-06-07T11:44:50Z | Stochastic full waveform inversion with deep generative prior for
uncertainty quantification | To obtain high-resolution images of subsurface structures from seismic data, seismic imaging techniques such as Full Waveform Inversion (FWI) serve as crucial tools. However, FWI involves solving a nonlinear and often non-unique inverse problem, presenting challenges such as local minima trapping and inadequate handling of inherent uncertainties. In addressing these challenges, we propose leveraging deep generative models as the prior distribution of geophysical parameters for stochastic Bayesian inversion. This approach integrates the adjoint state gradient for efficient back-propagation from the numerical solution of partial differential equations. Additionally, we introduce explicit and implicit variational Bayesian inference methods. The explicit method computes variational distribution density using a normalizing flow-based neural network, enabling computation of the Bayesian posterior of parameters. Conversely, the implicit method employs an inference network attached to a pretrained generative model to estimate density, incorporating an entropy estimator. Furthermore, we also experimented with the Stein Variational Gradient Descent (SVGD) method as another variational inference technique, using particles. We compare these variational Bayesian inference methods with conventional Markov chain Monte Carlo (McMC) sampling. Each method is able to quantify uncertainties and to generate seismic data-conditioned realizations of subsurface geophysical parameters. This framework provides insights into subsurface structures while accounting for inherent uncertainties. | [
"['Yuke Xie' 'Hervé Chauris' 'Nicolas Desassis']"
] |
null | null | 2406.04860 | null | null | http://arxiv.org/pdf/2406.04860v1 | 2024-06-07T11:45:31Z | 2024-06-07T11:45:31Z | Multi-View Stochastic Block Models | Graph clustering is a central topic in unsupervised learning with a multitude of practical applications. In recent years, multi-view graph clustering has gained a lot of attention for its applicability to real-world instances where one has access to multiple data sources. In this paper we formalize a new family of models, called textit{multi-view stochastic block models} that captures this setting. For this model, we first study efficient algorithms that naively work on the union of multiple graphs. Then, we introduce a new efficient algorithm that provably outperforms previous approaches by analyzing the structure of each graph separately. Furthermore, we complement our results with an information-theoretic lower bound studying the limits of what can be done in this model. Finally, we corroborate our results with experimental evaluations. | [
"['Vincent Cohen-Addad' \"Tommaso d'Orsi\" 'Silvio Lattanzi' 'Rajai Nasser']"
] |
null | null | 2406.04867 | null | null | http://arxiv.org/pdf/2406.04867v2 | 2024-06-14T01:11:09Z | 2024-06-07T12:07:09Z | Deep learning for precipitation nowcasting: A survey from the
perspective of time series forecasting | Deep learning-based time series forecasting has dominated the short-term precipitation forecasting field with the help of its ability to estimate motion flow in high-resolution datasets. The growing interest in precipitation nowcasting offers substantial opportunities for the advancement of current forecasting technologies. Nevertheless, there has been a scarcity of in-depth surveys of time series precipitation forecasting using deep learning. Thus, this paper systemically reviews recent progress in time series precipitation forecasting models. Specifically, we investigate the following key points within background components, covering: i) preprocessing, ii) objective functions, and iii) evaluation metrics. We then categorize forecasting models into textit{recursive} and textit{multiple} strategies based on their approaches to predict future frames, investigate the impacts of models using the strategies, and performance assessments. Finally, we evaluate current deep learning-based models for precipitation forecasting on a public benchmark, discuss their limitations and challenges, and present some promising research directions. Our contribution lies in providing insights for a better understanding of time series precipitation forecasting and in aiding the development of robust AI solutions for the future. | [
"['Sojung An' 'Tae-Jin Oh' 'Eunha Sohn' 'Donghyun Kim']"
] |
null | null | 2406.04868 | null | null | http://arxiv.org/pdf/2406.04868v1 | 2024-06-07T12:07:16Z | 2024-06-07T12:07:16Z | Perturb-and-Project: Differentially Private Similarities and Marginals | We revisit the input perturbations framework for differential privacy where noise is added to the input $Ain mathcal{S}$ and the result is then projected back to the space of admissible datasets $mathcal{S}$. Through this framework, we first design novel efficient algorithms to privately release pair-wise cosine similarities. Second, we derive a novel algorithm to compute $k$-way marginal queries over $n$ features. Prior work could achieve comparable guarantees only for $k$ even. Furthermore, we extend our results to $t$-sparse datasets, where our efficient algorithms yields novel, stronger guarantees whenever $tle n^{5/6}/log n,.$ Finally, we provide a theoretical perspective on why textit{fast} input perturbation algorithms works well in practice. The key technical ingredients behind our results are tight sum-of-squares certificates upper bounding the Gaussian complexity of sets of solutions. | [
"['Vincent Cohen-Addad' \"Tommaso d'Orsi\" 'Alessandro Epasto'\n 'Vahab Mirrokni' 'Peilin Zhong']"
] |
null | null | 2406.04872 | null | null | http://arxiv.org/pdf/2406.04872v1 | 2024-06-07T12:12:20Z | 2024-06-07T12:12:20Z | Diversified Batch Selection for Training Acceleration | The remarkable success of modern machine learning models on large datasets often demands extensive training time and resource consumption. To save cost, a prevalent research line, known as online batch selection, explores selecting informative subsets during the training process. Although recent efforts achieve advancements by measuring the impact of each sample on generalization, their reliance on additional reference models inherently limits their practical applications, when there are no such ideal models available. On the other hand, the vanilla reference-model-free methods involve independently scoring and selecting data in a sample-wise manner, which sacrifices the diversity and induces the redundancy. To tackle this dilemma, we propose Diversified Batch Selection (DivBS), which is reference-model-free and can efficiently select diverse and representative samples. Specifically, we define a novel selection objective that measures the group-wise orthogonalized representativeness to combat the redundancy issue of previous sample-wise criteria, and provide a principled selection-efficient realization. Extensive experiments across various tasks demonstrate the significant superiority of DivBS in the performance-speedup trade-off. The code is publicly available. | [
"['Feng Hong' 'Yueming Lyu' 'Jiangchao Yao' 'Ya Zhang' 'Ivor W. Tsang'\n 'Yanfeng Wang']"
] |
null | null | 2406.04890 | null | null | http://arxiv.org/pdf/2406.04890v1 | 2024-06-07T12:36:31Z | 2024-06-07T12:36:31Z | Enhancing Indoor Temperature Forecasting through Synthetic Data in
Low-Data Environments | Forecasting indoor temperatures is important to achieve efficient control of HVAC systems. In this task, the limited data availability presents a challenge as most of the available data is acquired during standard operation where extreme scenarios and transitory regimes such as major temperature increases or decreases are de-facto excluded. Acquisition of such data requires significant energy consumption and a dedicated facility, hindering the quantity and diversity of available data. Cost related constraints however do not allow for continuous year-around acquisition. To address this, we investigate the efficacy of data augmentation techniques leveraging SoTA AI-based methods for synthetic data generation. Inspired by practical and experimental motivations, we explore fusion strategies of real and synthetic data to improve forecasting models. This approach alleviates the need for continuously acquiring extensive time series data, especially in contexts involving repetitive heating and cooling cycles in buildings. In our evaluation 1) we assess the performance of synthetic data generators independently, particularly focusing on SoTA AI-based methods; 2) we measure the utility of incorporating synthetically augmented data in a subsequent forecasting tasks where we employ a simple model in two distinct scenarios: 1) we first examine an augmentation technique that combines real and synthetically generated data to expand the training dataset, 2) we delve into utilizing synthetic data to tackle dataset imbalances. Our results highlight the potential of synthetic data augmentation in enhancing forecasting accuracy while mitigating training variance. Through empirical experiments, we show significant improvements achievable by integrating synthetic data, thereby paving the way for more robust forecasting models in low-data regime. | [
"['Zachari Thiry' 'Massimiliano Ruocco' 'Alessandro Nocente'\n 'Michail Spitieris']"
] |
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