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Title: SubStrat: A Subset-Based Strategy for Faster AutoML Abstract: Automated machine learning (AutoML) frameworks have become important tools in the data scientists' arsenal, as they dramatically reduce the manual work devoted to the construction of ML pipelines. Such frameworks intelligently search among millions of... |
Title: An Empirical Study of IoT Security Aspects at Sentence-Level in Developer Textual Discussions Abstract: IoT is a rapidly emerging paradigm that now encompasses almost every aspect of our modern life. As such, ensuring the security of IoT devices is crucial. IoT devices can differ from traditional computing, ther... |
Title: Beyond spectral gap: The role of the topology in decentralized learning Abstract: In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. We consider the setting in ... |
Title: Better Best of Both Worlds Bounds for Bandits with Switching Costs Abstract: We study best-of-both-worlds algorithms for bandits with switching cost, recently addressed by Rouyer, Seldin and Cesa-Bianchi, 2021. We introduce a surprisingly simple and effective algorithm that simultaneously achieves minimax optima... |
Title: Singapore Soundscape Site Selection Survey (S5): Identification of Characteristic Soundscapes of Singapore via Weighted k-means Clustering Abstract: The ecological validity of soundscape studies usually rests on a choice of soundscapes that are representative of the perceptual space under investigation. For exam... |
Title: Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse Abstract: Transformers have achieved remarkable success in several domains, ranging from natural language processing to computer vision. Nevertheless, it has been recently shown that stacking self-attention layers - the di... |
Title: Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting Abstract: Traffic forecasting is one canonical example of spatial-temporal learning task in Intelligent Traffic System. Existing approaches capture spatial dependency with a pre-determined matrix in graph convolution neura... |
Title: Towards Meta-learned Algorithm Selection using Implicit Fidelity Information Abstract: Automatically selecting the best performing algorithm for a given dataset or ranking multiple of them by their expected performance supports users in developing new machine learning applications. Most approaches for this probl... |
Title: CitySpec: An Intelligent Assistant System for Requirement Specification in Smart Cities Abstract: An increasing number of monitoring systems have been developed in smart cities to ensure that real-time operations of a city satisfy safety and performance requirements. However, many existing city requirements are ... |
Title: Intra-agent speech permits zero-shot task acquisition Abstract: Human language learners are exposed to a trickle of informative, context-sensitive language, but a flood of raw sensory data. Through both social language use and internal processes of rehearsal and practice, language learners are able to build high... |
Title: Group Meritocratic Fairness in Linear Contextual Bandits Abstract: We study the linear contextual bandit problem where an agent has to select one candidate from a pool and each candidate belongs to a sensitive group. In this setting, candidates' rewards may not be directly comparable between groups, for example ... |
Title: Shuffled Check-in: Privacy Amplification towards Practical Distributed Learning Abstract: Recent studies of distributed computation with formal privacy guarantees, such as differentially private (DP) federated learning, leverage random sampling of clients in each round (privacy amplification by subsampling) to a... |
Title: Utility of Equivariant Message Passing in Cortical Mesh Segmentation Abstract: The automated segmentation of cortical areas has been a long-standing challenge in medical image analysis. The complex geometry of the cortex is commonly represented as a polygon mesh, whose segmentation can be addressed by graph-base... |
Title: Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge Abstract: The success of deep neural networks (DNNs) is heavily dependent on computational resources. While DNNs are often employed on cloud servers, there is a growing need to operate DNNs on edge devices. Edge devices are typically lim... |
Title: Look Back When Surprised: Stabilizing Reverse Experience Replay for Neural Approximation Abstract: Experience replay methods, which are an essential part of reinforcement learning(RL) algorithms, are designed to mitigate spurious correlations and biases while learning from temporally dependent data. Roughly spea... |
Title: Fooling Explanations in Text Classifiers Abstract: State-of-the-art text classification models are becoming increasingly reliant on deep neural networks (DNNs). Due to their black-box nature, faithful and robust explanation methods need to accompany classifiers for deployment in real-life scenarios. However, it ... |
Title: Risk Measures and Upper Probabilities: Coherence and Stratification Abstract: Machine learning typically presupposes classical probability theory which implies that aggregation is built upon expectation. There are now multiple reasons to motivate looking at richer alternatives to classical probability theory as ... |
Title: A new Hyper-heuristic based on Adaptive Simulated Annealing and Reinforcement Learning for the Capacitated Electric Vehicle Routing Problem Abstract: Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehic... |
Title: Generalized Data Distribution Iteration Abstract: To obtain higher sample efficiency and superior final performance simultaneously has been one of the major challenges for deep reinforcement learning (DRL). Previous work could handle one of these challenges but typically failed to address them concurrently. In t... |
Title: Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow Abstract: Transfer learning (TL) is becoming a powerful tool in scientific applications of neural networks (NNs), such as weather/climate prediction and turbulence modeling. TL enables out-of-distributio... |
Title: FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning Abstract: Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving way. Since in real-world applica... |
Title: From "Where" to "What": Towards Human-Understandable Explanations through Concept Relevance Propagation Abstract: The emerging field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to today's powerful but opaque deep learning models. While local XAI methods explain individual predictions ... |
Title: Deep Neural Patchworks: Coping with Large Segmentation Tasks Abstract: Convolutional neural networks are the way to solve arbitrary image segmentation tasks. However, when images are large, memory demands often exceed the available resources, in particular on a common GPU. Especially in biomedical imaging, where... |
Title: Improved Cardiac Arrhythmia Prediction Based on Heart Rate Variability Analysis Abstract: Many types of ventricular and atrial cardiac arrhythmias have been discovered in clinical practice in the past 100 years, and these arrhythmias are a major contributor to sudden cardiac death. Ventricular tachycardia, ventr... |
Title: Does Crypto Kill? Relationship between Electricity Consumption Carbon Footprints and Bitcoin Transactions Abstract: Cryptocurrencies are gaining more popularity due to their security, making counterfeits impossible. However, these digital currencies have been criticized for creating a large carbon footprint due ... |
Title: Shedding a PAC-Bayesian Light on Adaptive Sliced-Wasserstein Distances Abstract: The Sliced-Wasserstein distance (SW) is a computationally efficient and theoretically grounded alternative to the Wasserstein distance. Yet, the literature on its statistical properties with respect to the distribution of slices, be... |
Title: Inferring Unfairness and Error from Population Statistics in Binary and Multiclass Classification Abstract: We propose methods for making inferences on the fairness and accuracy of a given classifier, using only aggregate population statistics. This is necessary when it is impossible to obtain individual classif... |
Title: Analyzing the impact of feature selection on the accuracy of heart disease prediction Abstract: Heart Disease has become one of the most serious diseases that has a significant impact on human life. It has emerged as one of the leading causes of mortality among the people across the globe during the last decade.... |
Title: Rites de Passage: Elucidating Displacement to Emplacement of Refugees Abstract: Social media deliberations allow to explore refugee-related is-sues. AI-based studies have investigated refugee issues mostly around a specific event and considered unimodal approaches. Contrarily, we have employed a multimodal archi... |
Title: Demystifying the Global Convergence Puzzle of Learning Over-parameterized ReLU Nets in Very High Dimensions Abstract: This theoretical paper is devoted to developing a rigorous theory for demystifying the global convergence phenomenon in a challenging scenario: learning over-parameterized Rectified Linear Unit (... |
Title: Flexible Group Fairness Metrics for Survival Analysis Abstract: Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorithmic fairness in regression and classification however there has been... |
Title: Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection Abstract: Data augmentation has been rare in the cyber security domain due to technical difficulties in altering data in a manner that is semantically consistent with the original data. This shortfall is particularly onerous given ... |
Title: Machine Learning Sensors Abstract: Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications. Current instantiations of embedded machine learning (ML) suffer from complex integration, lack of modularity, and privacy and security concerns from data movement. This... |
Title: On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning Abstract: Intelligent agents should have the ability to leverage knowledge from previously learned tasks in order to learn new ones quickly and efficiently. Meta-learning approaches have emerged as a popular solution to achieve this. However,... |
Title: Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning Abstract: While annotating decent amounts of data to satisfy sophisticated learning models can be cost-prohibitive for many real-world applications. Active learning (AL) and semi-supervised learni... |
Title: Future Artificial Intelligence tools and perspectives in medicine Abstract: Purpose of review: Artificial intelligence (AI) has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e., image, molecular ... |
Title: GAAF: Searching Activation Functions for Binary Neural Networks through Genetic Algorithm Abstract: Binary neural networks (BNNs) show promising utilization in cost and power-restricted domains such as edge devices and mobile systems. This is due to its significantly less computation and storage demand, but at t... |
Title: Joint Manifold Learning and Density Estimation Using Normalizing Flows Abstract: Based on the manifold hypothesis, real-world data often lie on a low-dimensional manifold, while normalizing flows as a likelihood-based generative model are incapable of finding this manifold due to their structural constraints. So... |
Title: Generalization Error Bounds for Deep Neural Networks Trained by SGD Abstract: Generalization error bounds for deep neural networks trained by stochastic gradient descent (SGD) are derived by combining a dynamical control of an appropriate parameter norm and the Rademacher complexity estimate based on parameter n... |
Title: Recent Advances in Bayesian Optimization Abstract: Bayesian optimization has emerged at the forefront of expensive black-box optimization due to its data efficiency. Recent years have witnessed a proliferation of studies on the development of new Bayesian optimization algorithms and their applications. Hence, th... |
Title: On the balance between the training time and interpretability of neural ODE for time series modelling Abstract: Most machine learning methods are used as a black box for modelling. We may try to extract some knowledge from physics-based training methods, such as neural ODE (ordinary differential equation). Neura... |
Title: Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking Abstract: Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. The model needs to capture the system behavior in multiple flight regimes and opera... |
Title: PyTSK: A Python Toolbox for TSK Fuzzy Systems Abstract: This paper presents PyTSK, a Python toolbox for developing Takagi-Sugeno-Kang (TSK) fuzzy systems. Based on scikit-learn and PyTorch, PyTSK allows users to optimize TSK fuzzy systems using fuzzy clustering or mini-batch gradient descent (MBGD) based algorit... |
Title: Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning Abstract: In this work we propose Neuro-Nav, an open-source library for neurally plausible reinforcement learning (RL). RL is among the most common modeling frameworks for studying decision making, learning, and navigation in biological organisms... |
Title: Integrating Random Effects in Deep Neural Networks Abstract: Modern approaches to supervised learning like deep neural networks (DNNs) typically implicitly assume that observed responses are statistically independent. In contrast, correlated data are prevalent in real-life large-scale applications, with typical ... |
Title: Neural Network Decoders for Permutation Codes Correcting Different Errors Abstract: Permutation codes were extensively studied in order to correct different types of errors for the applications on power line communication and rank modulation for flash memory. In this paper, we introduce the neural network decode... |
Title: Subject Membership Inference Attacks in Federated Learning Abstract: Privacy in Federated Learning (FL) is studied at two different granularities: item-level, which protects individual data points, and user-level, which protects each user (participant) in the federation. Nearly all of the private FL literature i... |
Title: Early Abnormal Detection of Sewage Pipe Network: Bagging of Various Abnormal Detection Algorithms Abstract: Abnormalities of the sewage pipe network will affect the normal operation of the whole city. Therefore, it is important to detect the abnormalities early. This paper propose an early abnormal-detection met... |
Title: Deep Learning-based FEA surrogate for sub-sea pressure vessel Abstract: During the design process of an autonomous underwater vehicle (AUV), the pressure vessel has a critical role. The pressure vessel contains dry electronics, power sources, and other sensors that can not be flooded. A traditional design approa... |
Title: Efficient decentralized multi-agent learning in asymmetric queuing systems Abstract: We study decentralized multi-agent learning in bipartite queuing systems, a standard model for service systems. In particular, $N$ agents request service from $K$ servers in a fully decentralized way, i.e, by running the same al... |
Title: Searching Similarity Measure for Binarized Neural Networks Abstract: Being a promising model to be deployed in resource-limited devices, Binarized Neural Networks (BNNs) have drawn extensive attention from both academic and industry. However, comparing to the full-precision deep neural networks (DNNs), BNNs suff... |
Title: Efficient Machine Learning, Compilers, and Optimizations for Embedded Systems Abstract: Deep Neural Networks (DNNs) have achieved great success in a massive number of artificial intelligence (AI) applications by delivering high-quality computer vision, natural language processing, and virtual reality application... |
Title: Concentration bounds for SSP Q-learning for average cost MDPs Abstract: We derive a concentration bound for a Q-learning algorithm for average cost Markov decision processes based on an equivalent shortest path problem, and compare it numerically with the alternative scheme based on relative value iteration. |
Title: Improving the Diagnosis of Psychiatric Disorders with Self-Supervised Graph State Space Models Abstract: Single subject prediction of brain disorders from neuroimaging data has gained increasing attention in recent years. Yet, for some heterogeneous disorders such as major depression disorder (MDD) and autism sp... |
Title: Assessing Project-Level Fine-Tuning of ML4SE Models Abstract: Machine Learning for Software Engineering (ML4SE) is an actively growing research area that focuses on methods that help programmers in their work. In order to apply the developed methods in practice, they need to achieve reasonable quality in order t... |
Title: Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images Abstract: Parotid gland tumors account for approximately 2% to 10% of head and neck tumors. Preoperative tumor localization, differential diagnosis, and subsequent selection of appropriate treatment for parotid gland tumors is critical. Howe... |
Title: Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification Abstract: We consider using gradient descent to minimize the nonconvex function $f(X)=\phi(XX^{T})$ over an $n\times r$ factor matrix $X$, in which $\phi$ is an underlying smooth con... |
Title: Specification-Guided Learning of Nash Equilibria with High Social Welfare Abstract: Reinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent systems, we propose a novel reinforcement learning fr... |
Title: AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems Abstract: Recent work has illuminated the vulnerability of speaker recognition systems (SRSs) against adversarial attacks, raising significant security concerns in deploying SRSs. However, they considered only a few settings (e.g.... |
Title: Adaptive Regularization for Adversarial Training Abstract: Adversarial training, which is to enhance robustness against adversarial attacks, has received much attention because it is easy to generate human-imperceptible perturbations of data to deceive a given deep neural network. In this paper, we propose a new... |
Title: An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training Abstract: Large medical imaging data sets are becoming increasingly available. A common challenge in these data sets is to ensure that each sample meets minimum quality requirements de... |
Title: Building Robust Ensembles via Margin Boosting Abstract: In the context of adversarial robustness, a single model does not usually have enough power to defend against all possible adversarial attacks, and as a result, has sub-optimal robustness. Consequently, an emerging line of work has focused on learning an en... |
Title: KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction Abstract: Designing accurate deep learning models for molecular property prediction plays an increasingly essential role in drug and material discovery. Recently, due to the scarcity of labeled molecules, self-supervised l... |
Title: DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings Abstract: The existence of multiple load-solution mappings of non-convex AC-OPF problems poses a fundamental challenge to deep neural network (DNN) schemes. As the training dataset may contain a mixture of data points... |
Title: Computational Doob's $h$-transforms for Online Filtering of Discretely Observed Diffusions Abstract: This paper is concerned with online filtering of discretely observed nonlinear diffusion processes. Our approach is based on the fully adapted auxiliary particle filter, which involves Doob's $h$-transforms that ... |
Title: On Outer Bi-Lipschitz Extensions of Linear Johnson-Lindenstrauss Embeddings of Low-Dimensional Submanifolds of $\mathbb{R}^N$ Abstract: Let $\mathcal{M}$ be a compact $d$-dimensional submanifold of $\mathbb{R}^N$ with reach $\tau$ and volume $V_{\mathcal M}$. Fix $\epsilon \in (0,1)$. In this paper we prove that... |
Title: Imitating Past Successes can be Very Suboptimal Abstract: Prior work has proposed a simple strategy for reinforcement learning (RL): label experience with the outcomes achieved in that experience, and then imitate the relabeled experience. These outcome-conditioned imitation learning methods are appealing becaus... |
Title: On the Role of Discount Factor in Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) enables effective learning from previously collected data without exploration, which shows great promise in real-world applications when exploration is expensive or even infeasible. The discount factor,... |
Title: Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics Abstract: The statistical regularities in language corpora encode well-known social biases into word embeddings. Here, we focus on gender to provide a comprehensive analysis of group-based biases in widely-used static En... |
Title: Data Stealing Attack on Medical Images: Is it Safe to Export Networks from Data Lakes? Abstract: In privacy-preserving machine learning, it is common that the owner of the learned model does not have any physical access to the data. Instead, only a secured remote access to a data lake is granted to the model own... |
Title: Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition Abstract: Speaker recognition systems (SRSs) have recently been shown to be vulnerable to adversarial attacks, raising significant security concerns. In this work, we systematically investigate transformation and adversarial ... |
Title: Group privacy for personalized federated learning Abstract: Federated learning is a type of collaborative machine learning, where participating clients process their data locally, sharing only updates to the collaborative model. This enables to build privacy-aware distributed machine learning models, among other... |
Title: Towards a General Purpose CNN for Long Range Dependencies in $\mathrm{N}$D Abstract: The use of Convolutional Neural Networks (CNNs) is widespread in Deep Learning due to a range of desirable model properties which result in an efficient and effective machine learning framework. However, performant CNN architect... |
Title: FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning Abstract: Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medical institutions often use electrodes... |
Title: Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage Abstract: Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discriminatory bias in predictions. Altho... |
Title: Generating Long Videos of Dynamic Scenes Abstract: We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time. Existing video generation methods often fail to produce new content as a function of time while maintaining consiste... |
Title: A Benchmark for Federated Hetero-Task Learning Abstract: To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of bot... |
Title: Robust Sparse Mean Estimation via Sum of Squares Abstract: We study the problem of high-dimensional sparse mean estimation in the presence of an $\epsilon$-fraction of adversarial outliers. Prior work obtained sample and computationally efficient algorithms for this task for identity-covariance subgaussian distr... |
Title: Learning in Observable POMDPs, without Computationally Intractable Oracles Abstract: Much of reinforcement learning theory is built on top of oracles that are computationally hard to implement. Specifically for learning near-optimal policies in Partially Observable Markov Decision Processes (POMDPs), existing al... |
Title: Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach Abstract: Upcoming technologies like digital twins, autonomous, and artificial intelligent systems involving safety-critical applications require models which are accurate, inter... |
Title: Adversarial Reprogramming Revisited Abstract: Adversarial reprogramming, introduced by Elsayed, Goodfellow, and Sohl-Dickstein, seeks to repurpose a neural network to perform a different task, by manipulating its input without modifying its weights. We prove that two-layer ReLU neural networks with random weight... |
Title: Discrete State-Action Abstraction via the Successor Representation Abstract: When reinforcement learning is applied with sparse rewards, agents must spend a prohibitively long time exploring the unknown environment without any learning signal. Abstraction is one approach that provides the agent with an intrinsic... |
Title: FDGNN: Fully Dynamic Graph Neural Network Abstract: Dynamic Graph Neural Networks recently became more and more important as graphs from many scientific fields, ranging from mathematics, biology, social sciences, and physics to computer science, are dynamic by nature. While temporal changes (dynamics) play an es... |
Title: Short Blocklength Wiretap Channel Codes via Deep Learning: Design and Performance Evaluation Abstract: We design short blocklength codes for the Gaussian wiretap channel under information-theoretic security guarantees. Our approach consists in decoupling the reliability and secrecy constraints in our code design... |
Title: SHRED: 3D Shape Region Decomposition with Learned Local Operations Abstract: We present SHRED, a method for 3D SHape REgion Decomposition. SHRED takes a 3D point cloud as input and uses learned local operations to produce a segmentation that approximates fine-grained part instances. We endow SHRED with three dec... |
Title: Parametric Chordal Sparsity for SDP-based Neural Network Verification Abstract: Many future technologies rely on neural networks, but verifying the correctness of their behavior remains a major challenge. It is known that neural networks can be fragile in the presence of even small input perturbations, yielding ... |
Title: Few-Shot Learning by Dimensionality Reduction in Gradient Space Abstract: We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace. In experimental and theoretical analyses, we show that... |
Title: Towards Practical Differential Privacy in Data Analysis: Understanding the Effect of Epsilon on Utility in Private ERM Abstract: In this paper, we focus our attention on private Empirical Risk Minimization (ERM), which is one of the most commonly used data analysis method. We take the first step towards solving ... |
Title: EiX-GNN : Concept-level eigencentrality explainer for graph neural networks Abstract: Explaining is a human knowledge transfer process regarding a phenomenon between an explainer and an explainee. Each word used to explain this phenomenon must be carefully selected by the explainer in accordance with the current... |
Title: A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via Differential Privacy Abstract: Currently, the federated graph neural network (GNN) has attracted a lot of attention due to its wide applications in reality without violating the privacy regulations. Among all the privacy-preserving technologi... |
Title: DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning Abstract: Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines. However, users tend to mistrust the optimizat... |
Title: How does overparametrization affect performance on minority groups? Abstract: The benefits of overparameterization for the overall performance of modern machine learning (ML) models are well known. However, the effect of overparameterization at a more granular level of data subgroups is less understood. Recent e... |
Title: Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits Abstract: We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers many common reward distribution... |
Title: A Penny for Your (visual) Thoughts: Self-Supervised Reconstruction of Natural Movies from Brain Activity Abstract: Reconstructing natural videos from fMRI brain recordings is very challenging, for two main reasons: (i) As fMRI data acquisition is difficult, we only have a limited amount of supervised samples, wh... |
Title: NOMAD: Nonlinear Manifold Decoders for Operator Learning Abstract: Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By directly learning maps (operato... |
Title: A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction Abstract: Recent emergence of high-throughput drug screening assays sparkled an intensive development of machine learning methods, including models for prediction of sensitivity of cancer cell lines to anti-cance... |
Title: Two Ways of Understanding Social Dynamics: Analyzing the Predictability of Emergence of Objects in Reddit r/place Dependent on Locality in Space and Time Abstract: Lately, studying social dynamics in interacting agents has been boosted by the power of computer models, which bring the richness of qualitative work... |
Title: Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure Abstract: The practicality of reinforcement learning algorithms has been limited due to poor scaling with respect to the problem size, as the sample complexity of learning an $\epsilon$-optimal policy i... |
Title: Certifying Data-Bias Robustness in Linear Regression Abstract: Datasets typically contain inaccuracies due to human error and societal biases, and these inaccuracies can affect the outcomes of models trained on such datasets. We present a technique for certifying whether linear regression models are pointwise-ro... |
Title: Contributor-Aware Defenses Against Adversarial Backdoor Attacks Abstract: Deep neural networks for image classification are well-known to be vulnerable to adversarial attacks. One such attack that has garnered recent attention is the adversarial backdoor attack, which has demonstrated the capability to perform t... |
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