AISTATS A

548 papers

YearTitle / Authors
2024A 4-Approximation Algorithm for Min Max Correlation Clustering.
Holger S. G. Heidrich, Jannik Irmai, Bjoern Andres
2024A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent.
Mehdi Jafarnia-Jahromi, Rahul Jain, Ashutosh Nayyar
2024A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning.
Mizhaan Prajit Maniyar, Prashanth L. A., Akash Mondal, Shalabh Bhatnagar
2024A Doubly Robust Approach to Sparse Reinforcement Learning.
Wonyoung Kim, Garud Iyengar, Assaf Zeevi
2024A General Algorithm for Solving Rank-one Matrix Sensing.
Lianke Qin, Zhao Song, Ruizhe Zhang
2024A General Theoretical Paradigm to Understand Learning from Human Preferences.
Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Rémi Munos, Mark Rowland, Michal Valko, Daniele Calandriello
2024A Greedy Approximation for k-Determinantal Point Processes.
Julia Grosse, Rahel Fischer, Roman Garnett, Philipp Hennig
2024A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization.
Mathieu Dagréou, Thomas Moreau, Samuel Vaiter, Pierre Ablin
2024A Neural Architecture Predictor based on GNN-Enhanced Transformer.
Xunzhi Xiang, Kun Jing, Jungang Xu
2024A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning.
Kihyuk Hong, Yuhang Li, Ambuj Tewari
2024A Scalable Algorithm for Individually Fair k-Means Clustering.
MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi
2024A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport.
Tianyi Lin, Marco Cuturi, Michael I. Jordan
2024A Unified Framework for Discovering Discrete Symmetries.
Pavan Karjol, Rohan Kashyap, Aditya Gopalan, A. P. Prathosh
2024A Unifying Variational Framework for Gaussian Process Motion Planning.
Lucas Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson, Marc Peter Deisenroth, Alexander Terenin, Yasemin Bekiroglu
2024A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models.
Zhongliang Guo, Weiye Li, Yifei Qian, Ognjen Arandjelovic, Lei Fang
2024A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity.
Zhihan Xiong, Romain Camilleri, Maryam Fazel, Lalit Jain, Kevin Jamieson
2024A/B testing under Interference with Partial Network Information.
Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau
2024ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data.
Maria C. Novitasari, Johannes Quaas, Miguel Rodrigues
2024Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses.
Ziye Ma, Ying Chen, Javad Lavaei, Somayeh Sojoudi
2024Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo.
Haoyang Zheng, Wei Deng, Christian Moya, Guang Lin
2024Acceleration and Implicit Regularization in Gaussian Phase Retrieval.
Tyler Maunu, Martin Molina-Fructuoso
2024Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex.
Yasushi Esaki, Akihiro Nakamura, Keisuke Kawano, Ryoko Tokuhisa, Takuro Kutsuna
2024Achieving Fairness through Separability: A Unified Framework for Fair Representation Learning.
Taeuk Jang, Hongchang Gao, Pengyi Shi, Xiaoqian Wang
2024Achieving Group Distributional Robustness and Minimax Group Fairness with Interpolating Classifiers.
Natalia Martínez, Martín Bertrán, Guillermo Sapiro
2024Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach.
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael A. Osborne
2024Adaptive Compression in Federated Learning via Side Information.
Berivan Isik, Francesco Pase, Deniz Gündüz, Sanmi Koyejo, Tsachy Weissman, Michele Zorzi
2024Adaptive Discretization for Event PredicTion (ADEPT).
Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael J. Pencina, Matthew Engelhard
2024Adaptive Experiment Design with Synthetic Controls.
Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar
2024Adaptive Federated Minimax Optimization with Lower Complexities.
Feihu Huang, Xinrui Wang, Junyi Li, Songcan Chen
2024Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification.
Marzi Heidari, Abdullah Alchihabi, Qing En, Yuhong Guo
2024Adaptive Quasi-Newton and Anderson Acceleration Framework with Explicit Global (Accelerated) Convergence Rates.
Damien Scieur
2024Adaptive and non-adaptive minimax rates for weighted Laplacian-Eigenmap based nonparametric regression.
Zhaoyang Shi, Krishna Balasubramanian, Wolfgang Polonik
2024Adaptive importance sampling for heavy-tailed distributions via α-divergence minimization.
Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux, Victor Elvira
2024Adaptivity of Diffusion Models to Manifold Structures.
Rong Tang, Yun Yang
2024Agnostic Multi-Robust Learning using ERM.
Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin M. Stangl
2024An Analytic Solution to Covariance Propagation in Neural Networks.
Oren Wright, Yorie Nakahira, José M. F. Moura
2024An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization.
Lesi Chen, Haishan Ye, Luo Luo
2024An Impossibility Theorem for Node Embedding.
T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra
2024An Improved Algorithm for Learning Drifting Discrete Distributions.
Alessio Mazzetto
2024An Online Bootstrap for Time Series.
Nicolai Palm, Thomas Nagler
2024Analysis of Kernel Mirror Prox for Measure Optimization.
Pavel E. Dvurechensky, Jia-Jie Zhu
2024Analysis of Privacy Leakage in Federated Large Language Models.
Minh N. Vu, Truc D. T. Nguyen, Tre' R. Jeter, My T. Thai
2024Analysis of Using Sigmoid Loss for Contrastive Learning.
Chungpa Lee, Joonhwan Chang, Jy-yong Sohn
2024Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions.
Zulqarnain Khan, Davin Hill, Aria Masoomi, Joshua T. Bone, Jennifer G. Dy
2024Any-dimensional equivariant neural networks.
Eitan Levin, Mateo Díaz
2024Anytime-Constrained Reinforcement Learning.
Jeremy McMahan, Xiaojin Zhu
2024Approximate Bayesian Class-Conditional Models under Continuous Representation Shift.
Thomas L. Lee, Amos J. Storkey
2024Approximate Control for Continuous-Time POMDPs.
Yannick Eich, Bastian Alt, Heinz Koeppl
2024Approximate Leave-one-out Cross Validation for Regression with ℓ
Arnab Auddy, Haolin Zou, Kamiar Rahnama Rad, Arian Maleki
2024AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.
Rustem Islamov, Mher Safaryan, Dan Alistarh
2024Asymptotic Characterisation of the Performance of Robust Linear Regression in the Presence of Outliers.
Matteo Vilucchio, Emanuele Troiani, Vittorio Erba, Florent Krzakala
2024Asynchronous Randomized Trace Estimation.
Vasileios Kalantzis, Shashanka Ubaru, Chai Wah Wu, Georgios Kollias, Lior Horesh
2024Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization.
Mathieu Even, Anastasia Koloskova, Laurent Massoulié
2024Auditing Fairness under Unobserved Confounding.
Yewon Byun, Dylan Sam, Michael Oberst, Zachary C. Lipton, Bryan Wilder
2024Autoregressive Bandits.
Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran, Marco Mussi, Marcello Restelli, Nicola Gatti, Alberto Maria Metelli
2024BLIS-Net: Classifying and Analyzing Signals on Graphs.
Charles Xu, Laney Goldman, Valentina Guo, Benjamin Hollander-Bodie, Maedee Trank-Greene, Ian Adelstein, Edward De Brouwer, Rex Ying, Smita Krishnaswamy, Michael Perlmutter
2024BOBA: Byzantine-Robust Federated Learning with Label Skewness.
Wenxuan Bao, Jun Wu, Jingrui He
2024Backward Filtering Forward Deciding in Linear Non-Gaussian State Space Models.
YunPeng Li, Hans-Andrea Loeliger
2024Bandit Pareto Set Identification: the Fixed Budget Setting.
Cyrille Kone, Emilie Kaufmann, Laura Richert
2024Bayesian Online Learning for Consensus Prediction.
Samuel Showalter, Alex J. Boyd, Padhraic Smyth, Mark Steyvers
2024Bayesian Semi-structured Subspace Inference.
Daniel Dold, David Rügamer, Beate Sick, Oliver Dürr
2024Benchmarking Observational Studies with Experimental Data under Right-Censoring.
Ilker Demirel, Edward De Brouwer, Zeshan M. Hussain, Michael Oberst, Anthony Philippakis, David A. Sontag
2024Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds.
Alexandra Maria Hotti, Lennart Alexander Van der Goten, Jens Lagergren
2024Best Arm Identification with Resource Constraints.
Zitian Li, Wang Chi Cheung
2024Best-of-Both-Worlds Algorithms for Linear Contextual Bandits.
Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi
2024Better Batch for Deep Probabilistic Time Series Forecasting.
Vincent Zhihao Zheng, Seongjin Choi, Lijun Sun
2024Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective.
Yue Xing, Xiaofeng Lin, Qifan Song, Yi Xu, Belinda Zeng, Guang Cheng
2024Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support.
Tim Reichelt, Luke Ong, Tom Rainforth
2024BlockBoost: Scalable and Efficient Blocking through Boosting.
Thiago Ramos, Rodrigo Loro Schuller, Alex Akira Okuno, Lucas Nissenbaum, Roberto I. Oliveira, Paulo Orenstein
2024Boundary-Aware Uncertainty for Feature Attribution Explainers.
Davin Hill, Aria Masoomi, Max Torop, Sandesh Ghimire, Jennifer G. Dy
2024Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty.
Yu Inatsu, Shion Takeno, Hiroyuki Hanada, Kazuki Iwata, Ichiro Takeuchi
2024Breaking isometric ties and introducing priors in Gromov-Wasserstein distances.
Pinar Demetci, Quang Huy Tran, Ievgen Redko, Ritambhara Singh
2024Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems.
Nikita Puchkin, Eduard Gorbunov, Nikolay Kutuzov, Alexander V. Gasnikov
2024Bures-Wasserstein Means of Graphs.
Isabel Haasler, Pascal Frossard
2024CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference.
Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi
2024Can Probabilistic Feedback Drive User Impacts in Online Platforms?
Jessica Dai, Bailey Flanigan, Meena Jagadeesan, Nika Haghtalab, Chara Podimata
2024Categorical Generative Model Evaluation via Synthetic Distribution Coarsening.
Florence Regol, Mark Coates
2024Causal Bandits with General Causal Models and Interventions.
Zirui Yan, Dennis Wei, Dmitriy A. Katz-Rogozhnikov, Prasanna Sattigeri, Ali Tajer
2024Causal Discovery under Off-Target Interventions.
Davin Choo, Kirankumar Shiragur, Caroline Uhler
2024Causal Modeling with Stationary Diffusions.
Lars Lorch, Andreas Krause, Bernhard Schölkopf
2024Causal Q-Aggregation for CATE Model Selection.
Hui Lan, Vasilis Syrgkanis
2024Causally Inspired Regularization Enables Domain General Representations.
Olawale Salaudeen, Sanmi Koyejo
2024Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applications.
Jie Hu, Vishwaraj Doshi, Do Young Eun
2024Certified private data release for sparse Lipschitz functions.
Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang
2024Classifier Calibration with ROC-Regularized Isotonic Regression.
Eugene Berta, Francis R. Bach, Michael I. Jordan
2024Clustering Items From Adaptively Collected Inconsistent Feedback.
Shubham Gupta, Peter W. J. Staar, Christian de Sainte Marie
2024Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates.
Ahmad Rammal, Kaja Gruntkowska, Nikita Fedin, Eduard Gorbunov, Peter Richtárik
2024Communication-Efficient Federated Learning With Data and Client Heterogeneity.
Hossein Zakerinia, Shayan Talaei, Giorgi Nadiradze, Dan Alistarh
2024Comparing Comparators in Generalization Bounds.
Fredrik Hellström, Benjamin Guedj
2024Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraints.
Ahmet Alacaoglu, Stephen J. Wright
2024Compression with Exact Error Distribution for Federated Learning.
Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li, Aymeric Dieuleveut
2024Computing epidemic metrics with edge differential privacy.
George Z. Li, Dung Nguyen, Anil Vullikanti
2024Conditional Adjustment in a Markov Equivalence Class.
Sara LaPlante, Emilija Perkovic
2024Conditions on Preference Relations that Guarantee the Existence of Optimal Policies.
Jonathan Colaço Carr, Prakash Panangaden, Doina Precup
2024Confident Feature Ranking.
Bitya Neuhof, Yuval Benjamini
2024Conformal Contextual Robust Optimization.
Yash P. Patel, Sahana Rayan, Ambuj Tewari
2024Conformalized Deep Splines for Optimal and Efficient Prediction Sets.
Nathaniel Diamant, Ehsan Hajiramezanali, Tommaso Biancalani, Gabriele Scalia
2024Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection.
Yujin Han, Mingwenchan Xu, Leying Guan
2024Consistency of Dictionary-Based Manifold Learning.
Samson J. Koelle, Hanyu Zhang, Octavian-Vlad Murad, Marina Meila
2024Consistent Hierarchical Classification with A Generalized Metric.
Yuzhou Cao, Lei Feng, Bo An
2024Consistent Optimal Transport with Empirical Conditional Measures.
Piyushi Manupriya, Rachit Keerti Das, Sayantan Biswas, Saketha Nath Jagarlapudi
2024Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors.
Teodora Popordanoska, Sebastian Gregor Gruber, Aleksei Tiulpin, Florian Büttner, Matthew B. Blaschko
2024Constant or Logarithmic Regret in Asynchronous Multiplayer Bandits with Limited Communication.
Hugo Richard, Etienne Boursier, Vianney Perchet
2024Contextual Bandits with Budgeted Information Reveal.
Kyra Gan, Esmaeil Keyvanshokooh, Xueqing Liu, Susan A. Murphy
2024Contextual Directed Acyclic Graphs.
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
2024Continual Domain Adversarial Adaptation via Double-Head Discriminators.
Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao
2024Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games.
Jing Dong, Baoxiang Wang, Yaoliang Yu
2024Coreset Markov chain Monte Carlo.
Naitong Chen, Trevor Campbell
2024Corruption-Robust Offline Two-Player Zero-Sum Markov Games.
Andi Nika, Debmalya Mandal, Adish Singla, Goran Radanovic
2024Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning.
Amey P. Pasarkar, Adji Bousso Dieng
2024Cross-model Mutual Learning for Exemplar-based Medical Image Segmentation.
Qing En, Yuhong Guo
2024Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization.
Bahador Rashidi, Kerrick Johnstonbaugh, Chao Gao
2024DAGnosis: Localized Identification of Data Inconsistencies using Structures.
Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar
2024DE-HNN: An effective neural model for Circuit Netlist representation.
Zhishang Luo, Truong Son Hy, Puoya Tabaghi, Michaël Defferrard, Elahe Rezaei, Ryan Carey, W. Rhett Davis, Rajeev Jain, Yusu Wang
2024DHMConv: Directed Hypergraph Momentum Convolution Framework.
Wenbo Zhao, Zitong Ma, Zhe Yang
2024DNNLasso: Scalable Graph Learning for Matrix-Variate Data.
Meixia Lin, Yangjing Zhang
2024Data Driven Threshold and Potential Initialization for Spiking Neural Networks.
Velibor Bojkovic, Srinivas Anumasa, Giulia De Masi, Bin Gu, Huan Xiong
2024Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations.
Mohan Wu, Martin Lysy
2024Data-Driven Confidence Intervals with Optimal Rates for the Mean of Heavy-Tailed Distributions.
Ambrus Tamás, Szabolcs Szentpéteri, Balázs Csanád Csáji
2024Data-Driven Online Model Selection With Regret Guarantees.
Christoph Dann, Claudio Gentile, Aldo Pacchiano
2024Data-Efficient Contrastive Language-Image Pretraining: Prioritizing Data Quality over Quantity.
Siddharth Joshi, Arnav Jain, Ali Payani, Baharan Mirzasoleiman
2024Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate.
Hongchang Gao
2024Deep Classifier Mimicry without Data Access.
Steven Braun, Martin Mundt, Kristian Kersting
2024Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification.
Shivvrat Arya, Yu Xiang, Vibhav Gogate
2024Deep Learning-Based Alternative Route Computation.
Alex Zhai, Dee Guo, Sreenivas Gollapudi, Kostas Kollias, Daniel Delling
2024Deep anytime-valid hypothesis testing.
Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar
2024DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data.
Taehyo Kim, Hai Shu, Qiran Jia, Mony J. de Leon
2024Delegating Data Collection in Decentralized Machine Learning.
Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab
2024Density Uncertainty Layers for Reliable Uncertainty Estimation.
Yookoon Park, David M. Blei
2024Density-Regression: Efficient and Distance-aware Deep Regressor for Uncertainty Estimation under Distribution Shifts.
Ha Manh Bui, Anqi Liu
2024Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing.
Dominik Wagner, Basim Khajwal, Luke Ong
2024DiffRed: Dimensionality reduction guided by stable rank.
Prarabdh Shukla, Gagan Raj Gupta, Kunal Dutta
2024Differentiable Rendering with Reparameterized Volume Sampling.
Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry P. Vetrov, Kirill Struminsky
2024Differentially Private Conditional Independence Testing.
Iden Kalemaj, Shiva Prasad Kasiviswanathan, Aaditya Ramdas
2024Differentially Private Reward Estimation with Preference Feedback.
Sayak Ray Chowdhury, Xingyu Zhou, Nagarajan Natarajan
2024Directed Hypergraph Representation Learning for Link Prediction.
Zitong Ma, Wenbo Zhao, Zhe Yang
2024Directional Optimism for Safe Linear Bandits.
Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh
2024Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods.
Jiaxin Zhang, Kamalika Das, Kumar Sricharan
2024Discriminator Guidance for Autoregressive Diffusion Models.
Filip Ekström Kelvinius, Fredrik Lindsten
2024Dissimilarity Bandits.
Paolo Battellani, Alberto Maria Metelli, Francesco Trovò
2024Distributionally Robust Model-based Reinforcement Learning with Large State Spaces.
Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic
2024Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation.
Zhishuai Liu, Pan Xu
2024Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets.
Liyan Xie, Yuchen Liang, Venugopal V. Veeravalli
2024Don't Be Pessimistic Too Early: Look K Steps Ahead!
Chaoqi Wang, Ziyu Ye, Kevin Murphy, Yuxin Chen
2024Double InfoGAN for Contrastive Analysis.
Florence Carton, Robin Louiset, Pietro Gori
2024Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation.
Vinod Kumar Chauhan, Jiandong Zhou, Ghadeer O. Ghosheh, Soheila Molaei, David A. Clifton
2024E(3)-Equivariant Mesh Neural Networks.
Thuan Anh Trang, Nhat Khang Ngo, Daniel Levy, Ngoc Thieu Vo, Siamak Ravanbakhsh, Truong Son Hy
2024EM for Mixture of Linear Regression with Clustered Data.
Amirhossein Reisizadeh, Khashayar Gatmiry, Asuman E. Ozdaglar
2024Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability.
Rajdeep Haldar, Yue Xing, Qifan Song
2024Efficient Active Learning Halfspaces with Tsybakov Noise: A Non-convex Optimization Approach.
Yinan Li, Chicheng Zhang
2024Efficient Conformal Prediction under Data Heterogeneity.
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
2024Efficient Data Shapley for Weighted Nearest Neighbor Algorithms.
Jiachen T. Wang, Prateek Mittal, Ruoxi Jia
2024Efficient Graph Laplacian Estimation by Proximal Newton.
Yakov Medvedovsky, Eran Treister, Tirza S. Routtenberg
2024Efficient Low-Dimensional Compression of Overparameterized Models.
Soo Min Kwon, Zekai Zhang, Dogyoon Song, Laura Balzano, Qing Qu
2024Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent.
Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard, Nadia Oudjane
2024Efficient Neural Architecture Design via Capturing Architecture-Performance Joint Distribution.
Yue Liu, Ziyi Yu, Zitu Liu, Wenjie Tian
2024Efficient Quantum Agnostic Improper Learning of Decision Trees.
Sagnik Chatterjee, Tharrmashastha SAPV, Debajyoti Bera
2024Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems.
Neharika Jali, Guannan Qu, Weina Wang, Gauri Joshi
2024Efficient Variational Sequential Information Control.
Jianwei Shen, Jason Pacheco
2024Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning.
Jörn Tebbe, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann, Fabian Mies
2024Electronic Medical Records Assisted Digital Clinical Trial Design.
Xinrui Ruan, Jingshen Wang, Yingfei Wang, Waverly Wei
2024Emergent specialization from participation dynamics and multi-learner retraining.
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff, Jamie Morgenstern, Maryam Fazel
2024End-to-end Feature Selection Approach for Learning Skinny Trees.
Shibal Ibrahim, Kayhan Behdin, Rahul Mazumder
2024Enhancing Distributional Stability among Sub-populations.
Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui
2024Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization.
Zhenzhang Ye, Gabriel Peyré, Daniel Cremers, Pierre Ablin
2024Enhancing In-context Learning via Linear Probe Calibration.
Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen
2024Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels.
Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney
2024Equivalence Testing: The Power of Bounded Adaptivity.
Diptarka Chakraborty, Sourav Chakraborty, Gunjan Kumar, Kuldeep S. Meel
2024Equivariant bootstrapping for uncertainty quantification in imaging inverse problems.
Marcelo Pereyra, Julián Tachella
2024Error bounds for any regression model using Gaussian processes with gradient information.
Rafael Savvides, Hoang Phuc Hau Luu, Kai Puolamäki
2024Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression.
Sijin Chen, Zhize Li, Yuejie Chi
2024Estimating treatment effects from single-arm trials via latent-variable modeling.
Manuel Haussmann, Tran Minh Son Le, Viivi Halla-aho, Samu Kurki, Jussi Leinonen, Miika Koskinen, Samuel Kaski, Harri Lähdesmäki
2024Estimation of partially known Gaussian graphical models with score-based structural priors.
Martin Sevilla, Antonio G. Marques, Santiago Segarra
2024Ethics in Action: Training Reinforcement Learning Agents for Moral Decision-making In Text-based Adventure Games.
Weichen Li, Rati Devidze, Waleed Mustafa, Sophie Fellenz
2024Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers.
Pim de Haan, Taco Cohen, Johann Brehmer
2024Explanation-based Training with Differentiable Insertion/Deletion Metric-aware Regularizers.
Yuya Yoshikawa, Tomoharu Iwata
2024Exploration via linearly perturbed loss minimisation.
David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári
2024Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems.
Chendi Qian, Didier Chételat, Christopher Morris
2024Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks.
Marcus A. K. September, Francesco Sanna Passino, Leonie Tabea Goldmann, Anton Hinel
2024Extragradient Type Methods for Riemannian Variational Inequality Problems.
Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D. Abernethy, Molei Tao
2024FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning.
Xiang Meng, Wenyu Chen, Riade Benbaki, Rahul Mazumder
2024Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent.
Pratik Patil, Yuchen Wu, Ryan J. Tibshirani
2024Fair Machine Unlearning: Data Removal while Mitigating Disparities.
Alex Oesterling, Jiaqi Ma, Flávio P. Calmon, Himabindu Lakkaraju
2024Fair Soft Clustering.
Rune D. Kjærsgaard, Pekka Parviainen, Saket Saurabh, Madhumita Kundu, Line H. Clemmensen
2024Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes.
Jinwon Sohn, Qifan Song, Guang Lin
2024Fair k-center Clustering with Outliers.
Daichi Amagata
2024FairRR: Pre-Processing for Group Fairness through Randomized Response.
Joshua John Ward, Xianli Zeng, Guang Cheng
2024Fairness in Submodular Maximization over a Matroid Constraint.
Marwa El Halabi, Jakub Tarnawski, Ashkan Norouzi-Fard, Thuy-Duong Vuong
2024Faithful graphical representations of local independence.
Søren Wengel Mogensen
2024Fast 1-Wasserstein distance approximations using greedy strategies.
Guillaume Houry, Han Bao, Han Zhao, Makoto Yamada
2024Fast Dynamic Sampling for Determinantal Point Processes.
Zhao Song, Junze Yin, Lichen Zhang, Ruizhe Zhang
2024Fast Fourier Bayesian Quadrature.
Houston Warren, Fabio Ramos
2024Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging.
Chung-En Tsai, Hao-Chung Cheng, Yen-Huan Li
2024Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent.
Jialun Zhang, Richard Y. Zhang, Hong-Ming Chiu
2024Fast and Adversarial Robust Kernelized SDU Learning.
Yajing Fan, Wanli Shi, Yi Chang, Bin Gu
2024Faster Convergence with MultiWay Preferences.
Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren
2024Faster Recalibration of an Online Predictor via Approachability.
Princewill Okoroafor, Robert D. Kleinberg, Wen Sun
2024Feasible Q-Learning for Average Reward Reinforcement Learning.
Ying Jin, Ramki Gummadi, Zhengyuan Zhou, Jose H. Blanchet
2024FedFisher: Leveraging Fisher Information for One-Shot Federated Learning.
Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi
2024Federated Experiment Design under Distributed Differential Privacy.
Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, Ayfer Özgür
2024Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks.
Soheila Molaei, Anshul Thakur, Ghazaleh Niknam, Andrew A. S. Soltan, Hadi Zare, David A. Clifton
2024Federated Linear Contextual Bandits with Heterogeneous Clients.
Ethan Blaser, Chuanhao Li, Hongning Wang
2024Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models.
Soheun Yi, Sanghack Lee
2024First Passage Percolation with Queried Hints.
Kritkorn Karntikoon, Yiheng Shen, Sreenivas Gollapudi, Kostas Kollias, Aaron Schild, Ali Kemal Sinop
2024Fitting ARMA Time Series Models without Identification: A Proximal Approach.
Yin Liu, Sam Davanloo Tajbakhsh
2024Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit.
Shintaro Nakamura, Masashi Sugiyama
2024Fixed-kinetic Neural Hamiltonian Flows for enhanced interpretability and reduced complexity.
Vincent Souveton, Arnaud Guillin, Jens Jasche, Guilhem Lavaux, Manon Michel
2024Formal Verification of Unknown Stochastic Systems via Non-parametric Estimation.
Zhi Zhang, Chenyu Ma, Saleh Soudijani, Sadegh Soudjani
2024Free-form Flows: Make Any Architecture a Normalizing Flow.
Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, Ullrich Köthe
2024From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach.
Tuan Nguyen, Hirotada Honda, Takashi Sano, Vinh Nguyen, Shugo Nakamura, Tan Minh Nguyen
2024From Data Imputation to Data Cleaning - Automated Cleaning of Tabular Data Improves Downstream Predictive Performance.
Sebastian Jäger, Felix Biessmann
2024Functional Flow Matching.
Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth
2024Functional Graphical Models: Structure Enables Offline Data-Driven Optimization.
Kuba Grudzien Kuba, Masatoshi Uehara, Sergey Levine, Pieter Abbeel
2024Fusing Individualized Treatment Rules Using Secondary Outcomes.
Daiqi Gao, Yuanjia Wang, Donglin Zeng
2024GRAWA: Gradient-based Weighted Averaging for Distributed Training of Deep Learning Models.
Tolga Dimlioglu, Anna Choromanska
2024Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels.
Raphaël Carpintero Perez, Sébastien Da Veiga, Josselin Garnier, Brian Staber
2024General Identifiability and Achievability for Causal Representation Learning.
Burak Varici, Emre Acartürk, Karthikeyan Shanmugam, Ali Tajer
2024General Tail Bounds for Non-Smooth Stochastic Mirror Descent.
Khaled Eldowa, Andrea Paudice
2024Generalization Bounds for Label Noise Stochastic Gradient Descent.
Jung Eun Huh, Patrick Rebeschini
2024Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimization.
Siqi Zhang, Yifan Hu, Liang Zhang, Niao He
2024Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees.
Alexia Jolicoeur-Martineau, Kilian Fatras, Tal Kachman
2024Generative Flow Networks as Entropy-Regularized RL.
Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov
2024Gibbs-Based Information Criteria and the Over-Parameterized Regime.
Haobo Chen, Gregory W. Wornell, Yuheng Bu
2024GmGM: a fast multi-axis Gaussian graphical model.
Ethan B. Andrew, David R. Westhead, Luisa Cutillo
2024Graph Machine Learning through the Lens of Bilevel Optimization.
Amber Yijia Zheng, Tong He, Yixuan Qiu, Minjie Wang, David Wipf
2024Graph Partitioning with a Move Budget.
Mina Dalirrooyfard, Elaheh Fata, Majid Behbahani, Yuriy Nevmyvaka
2024Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets.
Panagiotis Lymperopoulos, Liping Liu
2024Graph fission and cross-validation.
James Leiner, Aaditya Ramdas
2024Hidden yet quantifiable: A lower bound for confounding strength using randomized trials.
Piersilvio De Bartolomeis, Javier Abad Martinez, Konstantin Donhauser, Fanny Yang
2024HintMiner: Automatic Question Hints Mining From Q&A Web Posts with Language Model via Self-Supervised Learning.
Zhenyu Zhang, Jiudong Yang
2024Hodge-Compositional Edge Gaussian Processes.
Maosheng Yang, Viacheslav Borovitskiy, Elvin Isufi
2024Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection.
Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt
2024Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation.
Jiayi Huang, Han Zhong, Liwei Wang, Lin Yang
2024How Good is a Single Basin?
Kai Lion, Lorenzo Noci, Thomas Hofmann, Gregor Bachmann
2024How does GPT-2 Predict Acronyms? Extracting and Understanding a Circuit via Mechanistic Interpretability.
Jorge García-Carrasco, Alejandro Maté, Juan C. Trujillo
2024Identifiability of Product of Experts Models.
Manav Kant, Eric Y. Ma, Andrei Staicu, Leonard J. Schulman, Spencer Gordon
2024Identifiable Feature Learning for Spatial Data with Nonlinear ICA.
Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen
2024Identification and Estimation of "Causes of Effects" using Covariate-Mediator Information.
Ryusei Shingaki, Manabu Kuroki
2024Identifying Confounding from Causal Mechanism Shifts.
Sarah Mameche, Jilles Vreeken, David Kaltenpoth
2024Identifying Copeland Winners in Dueling Bandits with Indifferences.
Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
2024Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias.
Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman
2024Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training.
Tom Sander, Maxime Sylvestre, Alain Durmus
2024Implicit Regularization in Deep Tucker Factorization: Low-Rankness via Structured Sparsity.
Kais Hariz, Hachem Kadri, Stéphane Ayache, Maher Moakher, Thierry Artières
2024Importance Matching Lemma for Lossy Compression with Side Information.
Buu Phan, Ashish Khisti, Christos Louizos
2024Imposing Fairness Constraints in Synthetic Data Generation.
Mahed Abroshan, Andrew Elliott, Mohammad Mahdi Khalili
2024Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition.
Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
2024Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion.
Junghyun Lee, Se-Young Yun, Kwang-Sung Jun
2024Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes.
Washim Uddin Mondal, Vaneet Aggarwal
2024Improving Robustness via Tilted Exponential Layer: A Communication-Theoretic Perspective.
Bhagyashree Puranik, Ahmad Beirami, Yao Qin, Upamanyu Madhow
2024Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models.
Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
2024Independent Learning in Constrained Markov Potential Games.
Philip Jordan, Anas Barakat, Niao He
2024Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs.
Mishfad Shaikh Veedu, Deepjyoti Deka, Murti V. Salapaka
2024Information-theoretic Analysis of Bayesian Test Data Sensitivity.
Futoshi Futami, Tomoharu Iwata
2024Informative Path Planning with Limited Adaptivity.
Rayen Tan, Rohan Ghuge, Viswanath Nagarajan
2024Integrating Uncertainty Awareness into Conformalized Quantile Regression.
Raphael Rossellini, Rina Foygel Barber, Rebecca Willett
2024International Conference on Artificial Intelligence and Statistics, 2-4 May 2024, Palau de Congressos, Valencia, Spain.
Sanjoy Dasgupta, Stephan Mandt, Yingzhen Li
2024Interpretability Guarantees with Merlin-Arthur Classifiers.
Stephan Wäldchen, Kartikey Sharma, Berkant Turan, Max Zimmer, Sebastian Pokutta
2024Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data.
Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
2024Intrinsic Gaussian Vector Fields on Manifolds.
Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy
2024Invariant Aggregator for Defending against Federated Backdoor Attacks.
Xiaoyang Wang, Dimitrios Dimitriadis, Sanmi Koyejo, Shruti Tople
2024Is this model reliable for everyone? Testing for strong calibration.
Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner
2024Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data.
Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
2024Joint control variate for faster black-box variational inference.
Xi Wang, Tomas Geffner, Justin Domke
2024Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate.
Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher
2024LEDetection: A Simple Framework for Semi-Supervised Few-Shot Object Detection.
Phi Vu Tran
2024LP-based Construction of DC Decompositions for Efficient Inference of Markov Random Fields.
Chaitanya Murti, Dhruva Kashyap, Chiranjib Bhattacharyya
2024Large-Scale Gaussian Processes via Alternating Projection.
Kaiwen Wu, Jonathan Wenger, Haydn Thomas Jones, Geoff Pleiss, Jacob R. Gardner
2024Learning Adaptive Kernels for Statistical Independence Tests.
Yixin Ren, Yewei Xia, Hao Zhang, Jihong Guan, Shuigeng Zhou
2024Learning Cartesian Product Graphs with Laplacian Constraints.
Changhao Shi, Gal Mishne
2024Learning Dynamics in Linear VAE: Posterior Collapse Threshold, Superfluous Latent Space Pitfalls, and Speedup with KL Annealing.
Yuma Ichikawa, Koji Hukushima
2024Learning Extensive-Form Perfect Equilibria in Two-Player Zero-Sum Sequential Games.
Martino Bernasconi, Alberto Marchesi, Francesco Trovò
2024Learning Fair Division from Bandit Feedback.
Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki
2024Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes.
Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jirí Navrátil, Aurélie C. Lozano, Naoki Abe, Yi-An Ma, Rose Yu
2024Learning Latent Partial Matchings with Gumbel-IPF Networks.
Hedda Cohen Indelman, Tamir Hazan
2024Learning Populations of Preferences via Pairwise Comparison Queries.
Gokcan Tatli, Yi Chen, Ramya Korlakai Vinayak
2024Learning Safety Constraints from Demonstrations with Unknown Rewards.
David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause
2024Learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order Optimization.
Zhou Zhai, Wanli Shi, Heng Huang, Yi Chang, Bin Gu
2024Learning Sparse Codes with Entropy-Based ELBOs.
Dmytro Velychko, Simon Damm, Asja Fischer, Jörg Lücke
2024Learning Under Random Distributional Shifts.
Kirk C. Bansak, Elisabeth Paulson, Dominik Rothenhäusler
2024Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data.
Yuqin Yang, Saber Salehkaleybar, Negar Kiyavash
2024Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers.
Krzysztof Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamás Sarlós, Thomas Weingarten, Adrian Weller
2024Learning multivariate temporal point processes via the time-change theorem.
Guilherme Augusto Zagatti, See-Kiong Ng, Stéphane Bressan
2024Learning the Pareto Set Under Incomplete Preferences: Pure Exploration in Vector Bandits.
Efe Mert Karagözlü, Yasar Cahit Yildirim, Çagin Ararat, Cem Tekin
2024Learning to Defer to a Population: A Meta-Learning Approach.
Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric T. Nalisnick
2024Learning to Rank for Optimal Treatment Allocation Under Resource Constraints.
Fahad Kamran, Maggie Makar, Jenna Wiens
2024Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models.
Shivvrat Arya, Tahrima Rahman, Vibhav Gogate
2024Learning-Based Algorithms for Graph Searching Problems.
Adela Frances DePavia, Erasmo Tani, Ali Vakilian
2024Length independent PAC-Bayes bounds for Simple RNNs.
Volodimir Mitarchuk, Clara Lacroce, Rémi Eyraud, Rémi Emonet, Amaury Habrard, Guillaume Rabusseau
2024Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks.
Hristo Papazov, Scott Pesme, Nicolas Flammarion
2024Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias.
Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko
2024Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures.
Paul Viallard, Rémi Emonet, Amaury Habrard, Emilie Morvant, Valentina Zantedeschi
2024Lexicographic Optimization: Algorithms and Stability.
Jacob D. Abernethy, Robert E. Schapire, Umar Syed
2024Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
Kyurae Kim, Yi-An Ma, Jacob R. Gardner
2024Local Causal Discovery with Linear non-Gaussian Cyclic Models.
Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang
2024Looping in the Human: Collaborative and Explainable Bayesian Optimization.
Masaki Adachi, Brady Planden, David A. Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau
2024Low-rank MDPs with Continuous Action Spaces.
Miruna Oprescu, Andrew Bennett, Nathan Kallus
2024Lower-level Duality Based Reformulation and Majorization Minimization Algorithm for Hyperparameter Optimization.
He Chen, Haochen Xu, Rujun Jiang, Anthony Man-Cho So
2024MIM-Reasoner: Learning with Theoretical Guarantees for Multiplex Influence Maximization.
Nguyen Hoang Khoi Do, Tanmoy Chowdhury, Chen Ling, Liang Zhao, My T. Thai
2024MINTY: Rule-based models that minimize the need for imputing features with missing values.
Lena Stempfle, Fredrik D. Johansson
2024MMD-based Variable Importance for Distributional Random Forest.
Clément Bénard, Jeffrey Näf, Julie Josse
2024Making Better Use of Unlabelled Data in Bayesian Active Learning.
Freddie Bickford Smith, Adam Foster, Tom Rainforth
2024Manifold-Aligned Counterfactual Explanations for Neural Networks.
Asterios Tsiourvas, Wei Sun, Georgia Perakis
2024Maximum entropy GFlowNets with soft Q-learning.
Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon
2024Mechanics of Next Token Prediction with Self-Attention.
Yingcong Li, Yixiao Huang, Muhammed Emrullah Ildiz, Ankit Singh Rawat, Samet Oymak
2024Membership Testing in Markov Equivalence Classes via Independence Queries.
Jiaqi Zhang, Kirankumar Shiragur, Caroline Uhler
2024Meta Learning in Bandits within shared affine Subspaces.
Steven Bilaj, Sofien Dhouib, Setareh Maghsudi
2024Mind the GAP: Improving Robustness to Subpopulation Shifts with Group-Aware Priors.
Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe
2024Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles.
Kevin Scaman, Mathieu Even, Batiste Le Bars, Laurent Massoulié
2024Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable.
Hyeok Kyu Kwon, Minwoo Chae
2024Minimizing Convex Functionals over Space of Probability Measures via KL Divergence Gradient Flow.
Rentian Yao, Linjun Huang, Yun Yang
2024Mitigating Underfitting in Learning to Defer with Consistent Losses.
Shuqi Liu, Yuzhou Cao, Qiaozhen Zhang, Lei Feng, Bo An
2024Mixed Models with Multiple Instance Learning.
Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian J. Theis, Francesco Paolo Casale
2024Mixed variational flows for discrete variables.
Gian Carlo Diluvi, Benjamin Bloem-Reddy, Trevor Campbell
2024Mixture-of-Linear-Experts for Long-term Time Series Forecasting.
Ronghao Ni, Zinan Lin, Shuaiqi Wang, Giulia Fanti
2024Model-Based Best Arm Identification for Decreasing Bandits.
Sho Takemori, Yuhei Umeda, Aditya Gopalan
2024Model-based Policy Optimization under Approximate Bayesian Inference.
Chaoqi Wang, Yuxin Chen, Kevin Murphy
2024Monitoring machine learning-based risk prediction algorithms in the presence of performativity.
Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio
2024Monotone Operator Theory-Inspired Message Passing for Learning Long-Range Interaction on Graphs.
Justin M. Baker, Qingsong Wang, Martin Berzins, Thomas Strohmer, Bao Wang
2024Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels.
Osama A. Hanna, Merve Karakas, Lin Yang, Christina Fragouli
2024Multi-Agent Learning in Contextual Games under Unknown Constraints.
Anna M. Maddux, Maryam Kamgarpour
2024Multi-Dimensional Hyena for Spatial Inductive Bias.
Itamar Zimerman, Lior Wolf
2024Multi-Domain Causal Representation Learning via Weak Distributional Invariances.
Kartik Ahuja, Amin Mansouri, Yixin Wang
2024Multi-Level Symbolic Regression: Function Structure Learning for Multi-Level Data.
Kei Sen Fong, Mehul Motani
2024Multi-Resolution Active Learning of Fourier Neural Operators.
Shibo Li, Xin Yu, Wei W. Xing, Robert M. Kirby, Akil Narayan, Shandian Zhe
2024Multi-armed bandits with guaranteed revenue per arm.
Dorian Baudry, Nadav Merlis, Mathieu Benjamin Molina, Hugo Richard, Vianney Perchet
2024Multi-objective Optimization via Wasserstein-Fisher-Rao Gradient Flow.
Yinuo Ren, Tesi Xiao, Tanmay Gangwani, Anshuka Rangi, Holakou Rahmanian, Lexing Ying, Subhajit Sanyal
2024Multi-resolution Time-Series Transformer for Long-term Forecasting.
Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates
2024Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures.
Mingyuan Zhang, Shivani Agarwal
2024Multitask Online Learning: Listen to the Neighborhood Buzz.
Juliette Achddou, Nicolò Cesa-Bianchi, Pierre Laforgue
2024Multivariate Time Series Forecasting By Graph Attention Networks With Theoretical Guarantees.
Zhi Zhang, Weijian Li, Han Liu
2024Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses.
Shiliang Zuo
2024Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization.
Yutong Wang, Rishi Sonthalia, Wei Hu
2024Near-Optimal Convex Simple Bilevel Optimization with a Bisection Method.
Jiulin Wang, Xu Shi, Rujun Jiang
2024Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games.
Yang Cai, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng
2024Near-Optimal Pure Exploration in Matrix Games: A Generalization of Stochastic Bandits & Dueling Bandits.
Arnab Maiti, Ross Boczar, Kevin Jamieson, Lillian J. Ratliff
2024Near-optimal Per-Action Regret Bounds for Sleeping Bandits.
Quan M. Nguyen, Nishant A. Mehta
2024Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean.
Anton Frederik Thielmann, René-Marcel Kruse, Thomas Kneib, Benjamin Säfken
2024Neural McKean-Vlasov Processes: Distributional Dependence in Diffusion Processes.
Haoming Yang, Ali Hasan, Yuting Ng, Vahid Tarokh
2024No-Regret Algorithms for Safe Bayesian Optimization with Monotonicity Constraints.
Arpan Losalka, Jonathan Scarlett
2024NoisyMix: Boosting Model Robustness to Common Corruptions.
N. Benjamin Erichson, Soon Hoe Lim, Winnie Xu, Francisco Utrera, Ziang Cao, Michael W. Mahoney
2024Non-Convex Joint Community Detection and Group Synchronization via Generalized Power Method.
Sijin Chen, Xiwei Cheng, Anthony Man-Cho So
2024Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning.
Zhishuai Li, Yunhao Nie, Ziyue Li, Lei Bai, Yisheng Lv, Rui Zhao
2024Non-vacuous Generalization Bounds for Adversarial Risk in Stochastic Neural Networks.
Waleed Mustafa, Philipp Liznerski, Antoine Ledent, Dennis Wagner, Puyu Wang, Marius Kloft
2024Nonparametric Automatic Differentiation Variational Inference with Spline Approximation.
Yuda Shao, Shan Yu, Tianshu Feng
2024Offline Policy Evaluation and Optimization Under Confounding.
Chinmaya Kausik, Yangyi Lu, Kevin Tan, Maggie Makar, Yixin Wang, Ambuj Tewari
2024Offline Primal-Dual Reinforcement Learning for Linear MDPs.
Germano Gabbianelli, Gergely Neu, Matteo Papini, Nneka Okolo
2024On Convergence in Wasserstein Distance and f-divergence Minimization Problems.
Cheuk Ting Li, Jingwei Zhang, Farzan Farnia
2024On Counterfactual Metrics for Social Welfare: Incentives, Ranking, and Information Asymmetry.
Serena Wang, Stephen Bates, P. M. Aronow, Michael I. Jordan
2024On Feynman-Kac training of partial Bayesian neural networks.
Zheng Zhao, Sebastian Mair, Thomas B. Schön, Jens Sjölund
2024On Parameter Estimation in Deviated Gaussian Mixture of Experts.
Huy Nguyen, Khai Nguyen, Nhat Ho
2024On Ranking-based Tests of Independence.
Myrto Limnios, Stéphan Clémençon
2024On The Temporal Domain of Differential Equation Inspired Graph Neural Networks.
Moshe Eliasof, Eldad Haber, Eran Treister, Carola-Bibiane Schönlieb
2024On cyclical MCMC sampling.
Liwei Wang, Xinru Liu, Aaron Smith, Aguemon Y. Atchadé
2024On learning history-based policies for controlling Markov decision processes.
Gandharv Patil, Aditya Mahajan, Doina Precup
2024On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem.
Georg Pichler, Marco Romanelli, Divya Prakash Manivannan, Prashanth Krishnamurthy, Farshad Khorrami, Siddharth Garg
2024On the Effect of Key Factors in Spurious Correlation: A theoretical Perspective.
Yipei Wang, Xiaoqian Wang
2024On the Expected Size of Conformal Prediction Sets.
Guneet S. Dhillon, George Deligiannidis, Tom Rainforth
2024On the Generalization Ability of Unsupervised Pretraining.
Yuyang Deng, Junyuan Hong, Jiayu Zhou, Mehrdad Mahdavi
2024On the Impact of Overparameterization on the Training of a Shallow Neural Network in High Dimensions.
Simon Martin, Francis R. Bach, Giulio Biroli
2024On the Misspecification of Linear Assumptions in Synthetic Controls.
Achille O. R. Nazaret, Claudia Shi, David M. Blei
2024On the Model-Misspecification in Reinforcement Learning.
Yunfan Li, Lin Yang
2024On the Nyström Approximation for Preconditioning in Kernel Machines.
Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin
2024On the Privacy of Selection Mechanisms with Gaussian Noise.
Jonathan Lebensold, Doina Precup, Borja Balle
2024On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation.
Jiawei Huang, Batuhan Yardim, Niao He
2024On the Theoretical Expressive Power and the Design Space of Higher-Order Graph Transformers.
Cai Zhou, Rose Yu, Yusu Wang
2024On the Vulnerability of Fairness Constrained Learning to Malicious Noise.
Avrim Blum, Princewill Okoroafor, Aadirupa Saha, Kevin M. Stangl
2024On the connection between Noise-Contrastive Estimation and Contrastive Divergence.
Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten
2024On the estimation of persistence intensity functions and linear representations of persistence diagrams.
Weichen Wu, Jisu Kim, Alessandro Rinaldo
2024On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: a regret lower bound for WSU-UX.
Ali Mortazavi, Junhao Lin, Nishant A. Mehta
2024On-Demand Federated Learning for Arbitrary Target Class Distributions.
Isu Jeong, Seulki Lee
2024Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods.
Davoud Ataee Tarzanagh, Parvin Nazari, Bojian Hou, Li Shen, Laura Balzano
2024Online Calibrated and Conformal Prediction Improves Bayesian Optimization.
Shachi Deshpande, Charles Marx, Volodymyr Kuleshov
2024Online Distribution Learning with Local Privacy Constraints.
Jin Sima, Changlong Wu, Olgica Milenkovic, Wojciech Szpankowski
2024Online Learning in Contextual Second-Price Pay-Per-Click Auctions.
Mengxiao Zhang, Haipeng Luo
2024Online Learning of Decision Trees with Thompson Sampling.
Ayman Chaouki, Jesse Read, Albert Bifet
2024Online learning in bandits with predicted context.
Yongyi Guo, Ziping Xu, Susan A. Murphy
2024Online multiple testing with e-values.
Ziyu Xu, Aaditya Ramdas
2024Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization.
Alejandro D. de la Concha Duarte, Nicolas Vayatis, Argyris Kalogeratos
2024Optimal Budgeted Rejection Sampling for Generative Models.
Alexandre Verine, Muni Sreenivas Pydi, Benjamin Négrevergne, Yann Chevaleyre
2024Optimal Exploration is no harder than Thompson Sampling.
Zhaoqi Li, Kevin Jamieson, Lalit Jain
2024Optimal Sparse Survival Trees.
Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin
2024Optimal Transport for Measures with Noisy Tree Metric.
Tam Le, Truyen Nguyen, Kenji Fukumizu
2024Optimal Zero-Shot Detector for Multi-Armed Attacks.
Federica Granese, Marco Romanelli, Pablo Piantanida
2024Optimal estimation of Gaussian (poly)trees.
Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
2024Optimising Distributions with Natural Gradient Surrogates.
Jonathan So, Richard E. Turner
2024Oracle-Efficient Pessimism: Offline Policy Optimization In Contextual Bandits.
Lequn Wang, Akshay Krishnamurthy, Alex Slivkins
2024Ordinal Potential-based Player Rating.
Nelson Vadori, Rahul Savani
2024Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles.
Fan Yang, Pierre Le Bodic, Michael Kamp, Mario Boley
2024P-tensors: a General Framework for Higher Order Message Passing in Subgraph Neural Networks.
Andrew R. Hands, Tianyi Sun, Risi Kondor
2024Parameter-Agnostic Optimization under Relaxed Smoothness.
Florian Hübler, Junchi Yang, Xiang Li, Niao He
2024Pathwise Explanation of ReLU Neural Networks.
Seongwoo Lim, Won Jo, Joohyung Lee, Jaesik Choi
2024Personalized Federated X-armed Bandit.
Wenjie Li, Qifan Song, Jean Honorio
2024Pessimistic Off-Policy Multi-Objective Optimization.
Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu
2024Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations.
Hanjiang Hu, Zuxin Liu, Linyi Li, Jiacheng Zhu, Ding Zhao
2024Policy Evaluation for Reinforcement Learning from Human Feedback: A Sample Complexity Analysis.
Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang
2024Policy Learning for Localized Interventions from Observational Data.
Myrl G. Marmarelis, Fred Morstatter, Aram Galstyan, Greg Ver Steeg
2024Positivity-free Policy Learning with Observational Data.
Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang
2024Posterior Uncertainty Quantification in Neural Networks using Data Augmentation.
Luhuan Wu, Sinead A. Williamson
2024PrIsing: Privacy-Preserving Peer Effect Estimation via Ising Model.
Abhinav Chakraborty, Anirban Chatterjee, Abhinandan Dalal
2024Preventing Arbitrarily High Confidence on Far-Away Data in Point-Estimated Discriminative Neural Networks.
Ahmad Rashid, Serena Hacker, Guojun Zhang, Agustinus Kristiadi, Pascal Poupart
2024Prior-dependent analysis of posterior sampling reinforcement learning with function approximation.
Yingru Li, Zhi-Quan Luo
2024Privacy-Constrained Policies via Mutual Information Regularized Policy Gradients.
Chris Cundy, Rishi Desai, Stefano Ermon
2024Privacy-Preserving Decentralized Actor-Critic for Cooperative Multi-Agent Reinforcement Learning.
Maheed H. Ahmed, Mahsa Ghasemi
2024Private Learning with Public Features.
Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang
2024Probabilistic Calibration by Design for Neural Network Regression.
Victor Dheur, Souhaib Ben Taieb
2024Probabilistic Integral Circuits.
Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur
2024Probabilistic Modeling for Sequences of Sets in Continuous-Time.
Yuxin Chang, Alex J. Boyd, Padhraic Smyth
2024Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains.
Nikita Tsoy, Anna Mihalkova, Teodora N. Todorova, Nikola Konstantinov
2024Provable Policy Gradient Methods for Average-Reward Markov Potential Games.
Min Cheng, Ruida Zhou, P. R. Kumar, Chao Tian
2024Provable local learning rule by expert aggregation for a Hawkes network.
Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret
2024Proving Linear Mode Connectivity of Neural Networks via Optimal Transport.
Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut
2024Proximal Causal Inference for Synthetic Control with Surrogates.
Jizhou Liu, Eric Tchetgen Tchetgen, Carlos Varjão
2024Proxy Methods for Domain Adaptation.
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt J. Kusner, Alexander D'Amour, Sanmi Koyejo, Arthur Gretton
2024Pure Exploration in Bandits with Linear Constraints.
Emil Carlsson, Debabrota Basu, Fredrik D. Johansson, Devdatt P. Dubhashi
2024Quantifying Uncertainty in Natural Language Explanations of Large Language Models.
Sree Harsha Tanneru, Chirag Agarwal, Himabindu Lakkaraju
2024Quantifying intrinsic causal contributions via structure preserving interventions.
Dominik Janzing, Patrick Blöbaum, Atalanti-Anastasia Mastakouri, Philipp Michael Faller, Lenon Minorics, Kailash Budhathoki
2024Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models.
Frederiek Wesel, Kim Batselier
2024Queuing dynamics of asynchronous Federated Learning.
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines
2024RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model.
Junyi Fan, Yuxuan Han, Jialin Zeng, Jian-Feng Cai, Yang Wang, Yang Xiang, Jiheng Zhang
2024Random Oscillators Network for Time Series Processing.
Andrea Ceni, Andrea Cossu, Maximilian W. Stölzle, Jingyue Liu, Cosimo Della Santina, Davide Bacciu, Claudio Gallicchio
2024Recovery Guarantees for Distributed-OMP.
Chen Amiraz, Robert Krauthgamer, Boaz Nadler
2024Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures.
Hao Liang, Zhiquan Luo
2024Reparameterized Variational Rejection Sampling.
Martin Jankowiak, Du Phan
2024Resilient Constrained Reinforcement Learning.
Dongsheng Ding, Zhengyan Huan, Alejandro Ribeiro
2024Restricted Isometry Property of Rank-One Measurements with Random Unit-Modulus Vectors.
Wei Zhang, Zhenni Wang
2024Revisiting the Noise Model of Stochastic Gradient Descent.
Barak Battash, Lior Wolf, Ofir Lindenbaum
2024Reward-Relevance-Filtered Linear Offline Reinforcement Learning.
Angela Zhou
2024Riemannian Laplace Approximation with the Fisher Metric.
Hanlin Yu, Marcelo Hartmann, Bernardo Williams Moreno Sanchez, Mark Girolami, Arto Klami
2024Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum.
Shogo Iwazaki, Tomohiko Tanabe, Mitsuru Irie, Shion Takeno, Yu Inatsu
2024Robust Approximate Sampling via Stochastic Gradient Barker Dynamics.
Lorenzo Mauri, Giacomo Zanella
2024Robust Data Clustering with Outliers via Transformed Tensor Low-Rank Representation.
Tong Wu
2024Robust Non-linear Normalization of Heterogeneous Feature Distributions with Adaptive Tanh-Estimators.
Felip Guimerà Cuevas, Helmut Schmid
2024Robust Offline Reinforcement Learning with Heavy-Tailed Rewards.
Jin Zhu, Runzhe Wan, Zhengling Qi, Shikai Luo, Chengchun Shi
2024Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis.
Sangil Han, Sungkyu Jung, Kyoowon Kim
2024Robust Sparse Voting.
Youssef Allouah, Rachid Guerraoui, Lê-Nguyên Hoang, Oscar Villemaud
2024Robust variance-regularized risk minimization with concomitant scaling.
Matthew J. Holland
2024SADI: Similarity-Aware Diffusion Model-Based Imputation for Incomplete Temporal EHR Data.
Zongyu Dai, Emily J. Getzen, Qi Long
2024SDEs for Minimax Optimization.
Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Proske, Aurélien Lucchi
2024SDMTR: A Brain-inspired Transformer for Relation Inference.
Xiangyu Zeng, Jie Lin, Piao Hu, Zhihao Li, Tianxi Huang
2024SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization.
Yann Fraboni, Martin Van Waerebeke, Kevin Scaman, Richard Vidal, Laetitia Kameni, Marco Lorenzi
2024SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits.
Subhojyoti Mukherjee, Qiaomin Xie, Josiah P. Hanna, Robert D. Nowak
2024SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification.
Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
2024Safe and Interpretable Estimation of Optimal Treatment Regimes.
Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky
2024Sample Complexity Characterization for Linear Contextual MDPs.
Junze Deng, Yuan Cheng, Shaofeng Zou, Yingbin Liang
2024Sample Efficient Learning of Factored Embeddings of Tensor Fields.
Taemin Heo, Chandrajit Bajaj
2024Sample-Efficient Personalization: Modeling User Parameters as Low Rank Plus Sparse Components.
Soumyabrata Pal, Prateek Varshney, Gagan Madan, Prateek Jain, Abhradeep Thakurta, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava
2024Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs.
Sanmitra Ghosh, Paul Birrell, Daniela De Angelis
2024Sampling-based Safe Reinforcement Learning for Nonlinear Dynamical Systems.
Wesley Suttle, Vipul Kumar Sharma, Krishna Chaitanya Kosaraju, Seetharaman Sivaranjani, Ji Liu, Vijay Gupta, Brian M. Sadler
2024Scalable Algorithms for Individual Preference Stable Clustering.
Ron Mosenzon, Ali Vakilian
2024Scalable Higher-Order Tensor Product Spline Models.
David Rügamer
2024Scalable Learning of Item Response Theory Models.
Susanne Frick, Amer Krivosija, Alexander Munteanu
2024Scalable Meta-Learning with Gaussian Processes.
Petru Tighineanu, Lukas Grossberger, Paul Baireuther, Kathrin Skubch, Stefan Falkner, Julia Vinogradska, Felix Berkenkamp
2024Score Operator Newton transport.
Nisha Chandramoorthy, Florian T. Schäfer, Youssef M. Marzouk
2024Self-Compatibility: Evaluating Causal Discovery without Ground Truth.
Philipp Michael Faller, Leena C. Vankadara, Atalanti-Anastasia Mastakouri, Francesco Locatello, Dominik Janzing
2024Self-Supervised Quantization-Aware Knowledge Distillation.
Kaiqi Zhao, Ming Zhao
2024Sequence Length Independent Norm-Based Generalization Bounds for Transformers.
Jacob Trauger, Ambuj Tewari
2024Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference.
Declan McNamara, Jackson Loper, Jeffrey Regier
2024Sequential learning of the Pareto front for multi-objective bandits.
Élise Crepon, Aurélien Garivier, Wouter M. Koolen
2024Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations.
Krzysztof Kacprzyk, Mihaela van der Schaar
2024Sharp error bounds for imbalanced classification: how many examples in the minority class?
Anass Aghbalou, Anne Sabourin, François Portier
2024Sharpened Lazy Incremental Quasi-Newton Method.
Aakash Sunil Lahoti, Spandan Senapati, Ketan Rajawat, Alec Koppel
2024Simple and scalable algorithms for cluster-aware precision medicine.
Amanda M. Buch, Conor Liston, Logan Grosenick
2024Simulating weighted automata over sequences and trees with transformers.
Michael Rizvi-Martel, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau
2024Simulation-Based Stacking.
Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke
2024Simulation-Free Schrödinger Bridges via Score and Flow Matching.
Alexander Tong, Nikolay Malkin, Kilian Fatras, Lazar Atanackovic, Yanlei Zhang, Guillaume Huguet, Guy Wolf, Yoshua Bengio
2024Sinkhorn Flow as Mirror Flow: A Continuous-Time Framework for Generalizing the Sinkhorn Algorithm.
Mohammad Reza Karimi, Ya-Ping Hsieh, Andreas Krause
2024Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels.
Tamim El Ahmad, Luc Brogat-Motte, Pierre Laforgue, Florence d'Alché-Buc
2024Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning.
Zeqi Ye, Hansheng Jiang
2024Soft-constrained Schrödinger Bridge: a Stochastic Control Approach.
Jhanvi Garg, Xianyang Zhang, Quan Zhou
2024Solving Attention Kernel Regression Problem via Pre-conditioner.
Zhao Song, Junze Yin, Lichen Zhang
2024Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint.
Haoyue Tang, Tian Xie, Aosong Feng, Hanyu Wang, Chenyang Zhang, Yang Bai
2024Sparse and Faithful Explanations Without Sparse Models.
Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin
2024Spectrum Extraction and Clipping for Implicitly Linear Layers.
Ali Ebrahimpour Boroojeny, Matus Telgarsky, Hari Sundaram
2024Stochastic Approximation with Biased MCMC for Expectation Maximization.
Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner
2024Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling.
Arman Adibi, Nicolò Dal Fabbro, Luca Schenato, Sanjeev R. Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra
2024Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities.
Konstantinos Emmanouilidis, René Vidal, Nicolas Loizou
2024Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases.
Ruslan Nazykov, Aleksandr Shestakov, Vladimir Solodkin, Aleksandr Beznosikov, Gauthier Gidel, Alexander V. Gasnikov
2024Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements.
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Angeliki Giannou, Yudong Chen, Qiaomin Xie
2024Stochastic Multi-Armed Bandits with Strongly Reward-Dependent Delays.
Yifu Tang, Yingfei Wang, Zeyu Zheng
2024Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization.
Wei Shen, Minhui Huang, Jiawei Zhang, Cong Shen
2024Strategic Usage in a Multi-Learner Setting.
Eliot Shekhtman, Sarah Dean
2024Structural perspective on constraint-based learning of Markov networks.
Tuukka Korhonen, Fedor V. Fomin, Pekka Parviainen
2024Structured Transforms Across Spaces with Cost-Regularized Optimal Transport.
Othmane Sebbouh, Marco Cuturi, Gabriel Peyré
2024Submodular Minimax Optimization: Finding Effective Sets.
Loay Raed Mualem, Ethan R. Elenberg, Moran Feldman, Amin Karbasi
2024Subsampling Error in Stochastic Gradient Langevin Diffusions.
Kexin Jin, Chenguang Liu, Jonas Latz
2024Sum-max Submodular Bandits.
Stephen U. Pasteris, Alberto Rumi, Fabio Vitale, Nicolò Cesa-Bianchi
2024Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks.
Kaiting Liu, Zahra Atashgahi, Ghada Sokar, Mykola Pechenizkiy, Decebal Constantin Mocanu
2024Surrogate Active Subspaces for Jump-Discontinuous Functions.
Nathan Wycoff
2024Surrogate Bayesian Networks for Approximating Evolutionary Games.
Vincent Hsiao, Dana S. Nau, Bobak Pezeshki, Rina Dechter
2024Symmetric Equilibrium Learning of VAEs.
Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov
2024Tackling the XAI Disagreement Problem with Regional Explanations.
Gabriel Laberge, Yann Batiste Pequignot, Mario Marchand, Foutse Khomh
2024Taming False Positives in Out-of-Distribution Detection with Human Feedback.
Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak
2024Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence.
Ilyas Fatkhullin, Niao He
2024TenGAN: Pure Transformer Encoders Make an Efficient Discrete GAN for De Novo Molecular Generation.
Chen Li, Yoshihiro Yamanishi
2024Tensor-view Topological Graph Neural Network.
Tao Wen, Elynn Y. Chen, Yuzhou Chen
2024Testing Generated Distributions in GANs to Penalize Mode Collapse.
Yanxiang Gong, Zhiwei Xie, Mei Xie, Xin Ma
2024Testing exchangeability by pairwise betting.
Aytijhya Saha, Aaditya Ramdas
2024The ALℓ
John Hood, Aaron J. Schein
2024The Effective Number of Shared Dimensions Between Paired Datasets.
Hamza Giaffar, Camille E. Rullán Buxó, Mikio Aoi
2024The Galerkin method beats Graph-Based Approaches for Spectral Algorithms.
Vivien A Cabannnes, Francis Bach
2024The Relative Gaussian Mechanism and its Application to Private Gradient Descent.
Hadrien Hendrikx, Paul Mangold, Aurélien Bellet
2024The Risks of Recourse in Binary Classification.
Hidde Fokkema, Damien Garreau, Tim van Erven
2024The Solution Path of SLOPE.
Xavier Dupuis, Patrick Tardivel
2024The effect of Leaky ReLUs on the training and generalization of overparameterized networks.
Yinglong Guo, Shaohan Li, Gilad Lerman
2024The sample complexity of ERMs in stochastic convex optimization.
Daniel Carmon, Amir Yehudayoff, Roi Livni
2024Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention.
Anqi Mao, Mehryar Mohri, Yutao Zhong
2024Theory-guided Message Passing Neural Network for Probabilistic Inference.
Zijun Cui, Hanjing Wang, Tian Gao, Kartik Talamadupula, Qiang Ji
2024Think Before You Duel: Understanding Complexities of Preference Learning under Constrained Resources.
Rohan Deb, Aadirupa Saha, Arindam Banerjee
2024Think Global, Adapt Local: Learning Locally Adaptive K-Nearest Neighbor Kernel Density Estimators.
Kenny Falkær Olsen, Rasmus M. Hoeegh Lindrup, Morten Mørup
2024Thompson Sampling Itself is Differentially Private.
Tingting Ou, Rachel Cummings, Marco Avella Medina
2024Tight Verification of Probabilistic Robustness in Bayesian Neural Networks.
Ben Batten, Mehran Hosseini, Alessio Lomuscio
2024Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model.
Nikolaos Nakis, Abdulkadir Çelikkanat, Louis Boucherie, Sune Lehmann, Morten Mørup
2024Timing as an Action: Learning When to Observe and Act.
Helen Zhou, Audrey Huang, Kamyar Azizzadenesheli, David Childers, Zachary C. Lipton
2024To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models.
Cyrus Cousins, I. Elizabeth Kumar, Suresh Venkatasubramanian
2024Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL.
Arnob Ghosh, Xingyu Zhou, Ness B. Shroff
2024Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts.
Huy Nguyen, TrungTin Nguyen, Khai Nguyen, Nhat Ho
2024Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective.
Sanath Kumar Krishnamurthy, Adrienne Margaret Propp, Susan Athey
2024Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach.
Juanwu Lu, Wei Zhan, Masayoshi Tomizuka, Yeping Hu
2024Towards Practical Non-Adversarial Distribution Matching.
Ziyu Gong, Ben Usman, Han Zhao, David I. Inouye
2024Towards a Complete Benchmark on Video Moment Localization.
Jinyeong Chae, Donghwa Kim, Kwanseok Kim, Doyeon Lee, Sangho Lee, Seongsu Ha, Jonghwan Mun, Wooyoung Kang, Byungseok Roh, Joonseok Lee
2024Training Implicit Generative Models via an Invariant Statistical Loss.
José Manuel de Frutos, Pablo M. Olmos, Manuel Alberto Vazquez Lopez, Joaquín Míguez
2024Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach.
Ivan Peshekhonov, Aleksey Arzhantsev, Maxim V. Rakhuba
2024TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression.
Zelin He, Ying Sun, Runze Li
2024Transductive conformal inference with adaptive scores.
Ulysse Gazin, Gilles Blanchard, Étienne Roquain
2024Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression.
Kevin Li, Max Balakirsky, Simon Mak
2024Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting.
Louis Sharrock, Daniel Dodd, Christopher Nemeth
2024Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process.
Lingkai Kong, Haotian Sun, Yuchen Zhuang, Haorui Wang, Wenhao Mu, Chao Zhang
2024Uncertainty Matters: Stable Conclusions under Unstable Assessment of Fairness Results.
Ainhize Barrainkua, Paula Gordaliza, José Antonio Lozano, Novi Quadrianto
2024Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting.
Siyuan Xu, Yucheng Wang, Mingzhou Fan, Byung-Jun Yoon, Xiaoning Qian
2024Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters.
Zhenyu Sun, Xiaochun Niu, Ermin Wei
2024Understanding Inverse Scaling and Emergence in Multitask Representation Learning.
Muhammed Emrullah Ildiz, Zhe Zhao, Samet Oymak
2024Understanding Progressive Training Through the Framework of Randomized Coordinate Descent.
Rafal Szlendak, Elnur Gasanov, Peter Richtárik
2024Understanding the Generalization Benefits of Late Learning Rate Decay.
Yinuo Ren, Chao Ma, Lexing Ying
2024Unified Transfer Learning in High-Dimensional Linear Regression.
Shuo Shuo Liu
2024Unsupervised Change Point Detection in Multivariate Time Series.
Daoping Wu, Suhas Gundimeda, Shaoshuai Mou, Christopher J. Quinn
2024Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio.
Amirhossein Ahmadian, Yifan Ding, Gabriel Eilertsen, Fredrik Lindsten
2024Unveiling Latent Causal Rules: A Temporal Point Process Approach for Abnormal Event Explanation.
Yiling Kuang, Chao Yang, Yang Yang, Shuang Li
2024User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates.
Daogao Liu, Hilal Asi
2024VEC-SBM: Optimal Community Detection with Vectorial Edges Covariates.
Guillaume Braun, Masashi Sugiyama
2024Variational Gaussian Process Diffusion Processes.
Prakhar Verma, Vincent Adam, Arno Solin
2024Variational Resampling.
Oskar Kviman, Nicola Branchini, Víctor Elvira, Jens Lagergren
2024Vector Quantile Regression on Manifolds.
Marco Pegoraro, Sanketh Vedula, Aviv Rosenberg, Irene Tallini, Emanuele Rodolà, Alex M. Bronstein
2024Warped Diffusion for Latent Differentiation Inference.
Masahiro Nakano, Hiroki Sakuma, Ryo Nishikimi, Ryohei Shibue, Takashi Sato, Tomoharu Iwata, Kunio Kashino
2024Weight-Sharing Regularization.
Mehran Shakerinava, Motahareh Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien
2024When No-Rejection Learning is Consistent for Regression with Rejection.
Xiaocheng Li, Shang Liu, Chunlin Sun, Hanzhao Wang
2024Why is parameter averaging beneficial in SGD? An objective smoothing perspective.
Atsushi Nitanda, Ryuhei Kikuchi, Shugo Maeda, Denny Wu
2024XB-MAML: Learning Expandable Basis Parameters for Effective Meta-Learning with Wide Task Coverage.
Jae-Jun Lee, Sung Whan Yoon
2024autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm.
Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Côté