AISTATS 2024
547 papers
A General Theoretical Paradigm to Understand Learning from Human Preferences
Mohammad Gheshlaghi Azar, Zhaohan Daniel Guo, Bilal Piot, Remi Munos, Mark Rowland, Michal Valko, Daniele Calandriello A Greedy Approximation for K-Determinantal Point Processes
Julia Grosse, Rahel Fischer, Roman Garnett, Philipp Hennig A Scalable Algorithm for Individually Fair K-Means Clustering
MohammadHossein Bateni, Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi A Unified Framework for Discovering Discrete Symmetries
Pavan Karjol, Rohan Kashyap, Aditya Gopalan, A. P. Prathosh A Unifying Variational Framework for Gaussian Process Motion Planning
Lucas C. Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson, Marc Deisenroth, Alexander Terenin, Yasemin Bekiroglu A/B Testing Under Interference with Partial Network Information
Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau Accuracy-Preserving Calibration via Statistical Modeling on Probability Simplex
Yasushi Esaki, Akihiro Nakamura, Keisuke Kawano, Ryoko Tokuhisa, Takuro Kutsuna Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael A. Osborne Adaptive Compression in Federated Learning via Side Information
Berivan Isik, Francesco Pase, Deniz Gunduz, Sanmi Koyejo, Tsachy Weissman, Michele Zorzi Adaptive Discretization for Event PredicTion (ADEPT)
Jimmy Hickey, Ricardo Henao, Daniel Wojdyla, Michael Pencina, Matthew Engelhard Agnostic Multi-Robust Learning Using ERM
Saba Ahmadi, Avrim Blum, Omar Montasser, Kevin M Stangl An Impossibility Theorem for Node Embedding
T. Mitchell Roddenberry, Yu Zhu, Santiago Segarra Asynchronous Randomized Trace Estimation
Vasileios Kalantzis, Shashanka Ubaru, Chai Wah Wu, Georgios Kollias, Lior Horesh Auditing Fairness Under Unobserved Confounding
Yewon Byun, Dylan Sam, Michael Oberst, Zachary Lipton, Bryan Wilder autoMALA: Locally Adaptive Metropolis-Adjusted Langevin Algorithm
Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell, Alexandre Bouchard-Cote Autoregressive Bandits
Francesco Bacchiocchi, Gianmarco Genalti, Davide Maran, Marco Mussi, Marcello Restelli, Nicola Gatti, Alberto Maria Metelli Bayesian Online Learning for Consensus Prediction
Samuel Showalter, Alex J Boyd, Padhraic Smyth, Mark Steyvers Bayesian Semi-Structured Subspace Inference
Daniel Dold, David Ruegamer, Beate Sick, Oliver Dürr Benchmarking Observational Studies with Experimental Data Under Right-Censoring
Ilker Demirel, Edward De Brouwer, Zeshan M Hussain, Michael Oberst, Anthony A Philippakis, David Sontag Best-of-Both-Worlds Algorithms for Linear Contextual Bandits
Yuko Kuroki, Alberto Rumi, Taira Tsuchiya, Fabio Vitale, Nicolò Cesa-Bianchi BLIS-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 BlockBoost: Scalable and Efficient Blocking Through Boosting
Thiago Ramos, Rodrigo Loro Schuller, Alex Akira Okuno, Lucas Nissenbaum, Roberto I Oliveira, Paulo Orenstein Boundary-Aware Uncertainty for Feature Attribution Explainers
Davin Hill, Aria Masoomi, Max Torop, Sandesh Ghimire, Jennifer Dy Can Probabilistic Feedback Drive User Impacts in Online Platforms?
Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, Chara Podimata Causal Bandits with General Causal Models and Interventions
Zirui Yan, Dennis Wei, Dmitriy A Katz, Prasanna Sattigeri, Ali Tajer Causal Discovery Under Off-Target Interventions
Davin Choo, Kirankumar Shiragur, Caroline Uhler Causal Modeling with Stationary Diffusions
Lars Lorch, Andreas Krause, Bernhard Schölkopf Certified Private Data Release for Sparse Lipschitz Functions
Konstantin Donhauser, Johan Lokna, Amartya Sanyal, March Boedihardjo, Robert Hönig, Fanny Yang Confident Feature Ranking
Bitya Neuhof, Yuval Benjamini Conformal Contextual Robust Optimization
Yash P. Patel, Sahana Rayan, Ambuj Tewari Conformalized Deep Splines for Optimal and Efficient Prediction Sets
Nathaniel Diamant, Ehsan Hajiramezanali, Tommaso Biancalani, Gabriele Scalia Consistency of Dictionary-Based Manifold Learning
Samson J. Koelle, Hanyu Zhang, Octavian-Vlad Murad, Marina Meila Consistent and Asymptotically Unbiased Estimation of Proper Calibration Errors
Teodora Popordanoska, Sebastian Gregor Gruber, Aleksei Tiulpin, Florian Buettner, Matthew B. Blaschko Consistent Optimal Transport with Empirical Conditional Measures
Piyushi Manupriya, Rachit K. Das, Sayantan Biswas, SakethaNath N Jagarlapudi Contextual Bandits with Budgeted Information Reveal
Kyra Gan, Esmaeil Keyvanshokooh, Xueqing Liu, Susan Murphy Contextual Directed Acyclic Graphs
Ryan Thompson, Edwin V. Bonilla, Robert Kohn Corruption-Robust Offline Two-Player Zero-Sum Markov Games
Andi Nika, Debmalya Mandal, Adish Singla, Goran Radanovic DAGnosis: Localized Identification of Data Inconsistencies Using Structures
Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela Schaar DE-HNN: An Effective Neural Model for Circuit Netlist Representation
Zhishang Luo, Truong Son Hy, Puoya Tabaghi, Michaël Defferrard, Elahe Rezaei, Ryan M. Carey, Rhett Davis, Rajeev Jain, Yusu Wang Deep Anytime-Valid Hypothesis Testing
Teodora Pandeva, Patrick Forré, Aaditya Ramdas, Shubhanshu Shekhar Deep Classifier Mimicry Without Data Access
Steven Braun, Martin Mundt, Kristian Kersting Deep Learning-Based Alternative Route Computation
Alex Zhai, Dee Guo, Sreenivas Gollapudi, Kostas Kollias, Daniel Delling Delegating Data Collection in Decentralized Machine Learning
Nivasini Ananthakrishnan, Stephen Bates, Michael Jordan, Nika Haghtalab Differentiable Rendering with Reparameterized Volume Sampling
Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry P Vetrov, Kirill Struminsky Directional Optimism for Safe Linear Bandits
Spencer Hutchinson, Berkay Turan, Mahnoosh Alizadeh Dissimilarity Bandits
Paolo Battellani, Alberto Maria Metelli, Francesco Trovò Distributionally Robust Model-Based Reinforcement Learning with Large State Spaces
Shyam Sundhar Ramesh, Pier Giuseppe Sessa, Yifan Hu, Andreas Krause, Ilija Bogunovic Double InfoGAN for Contrastive Analysis
Florence Carton, Robin Louiset, Pietro Gori E(3)-Equivariant Mesh Neural Networks
Thuan Anh Trang, Nhat Khang Ngo, Daniel T. Levy, Thieu Ngoc Vo, Siamak Ravanbakhsh, Truong Son Hy Efficient Conformal Prediction Under Data Heterogeneity
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horvath, Martin Takac, Eric Moulines, Maxim Panov Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
Jörn Tebbe, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann, Fabian Mies EM for Mixture of Linear Regression with Clustered Data
Amirhossein Reisizadeh, Khashayar Gatmiry, Asuman Ozdaglar Enhancing Distributional Stability Among Sub-Populations
Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui Enhancing In-Context Learning via Linear Probe Calibration
Momin Abbas, Yi Zhou, Parikshit Ram, Nathalie Baracaldo, Horst Samulowitz, Theodoros Salonidis, Tianyi Chen Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
Da Long, Wei Xing, Aditi Krishnapriyan, Robert Kirby, Shandian Zhe, Michael W. Mahoney Equivalence Testing: The Power of Bounded Adaptivity
Diptarka Chakraborty, Sourav Chakraborty, Gunjan Kumar, Kuldeep Meel Estimating 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 Exploration via Linearly Perturbed Loss Minimisation
David Janz, Shuai Liu, Alex Ayoub, Csaba Szepesvári Extragradient Type Methods for Riemannian Variational Inequality Problems
Zihao Hu, Guanghui Wang, Xi Wang, Andre Wibisono, Jacob D Abernethy, Molei Tao Fair Soft Clustering
Rune D. Kjærsgaard, Pekka Parviainen, Saket Saurabh, Madhumita Kundu, Line Clemmensen Fairness in Submodular Maximization over a Matroid Constraint
Marwa El Halabi, Jakub Tarnawski, Ashkan Norouzi-Fard, Thuy-Duong Vuong Faster Convergence with MultiWay Preferences
Aadirupa Saha, Vitaly Feldman, Yishay Mansour, Tomer Koren Federated Experiment Design Under Distributed Differential Privacy
Wei-Ning Chen, Graham Cormode, Akash Bharadwaj, Peter Romov, Ayfer Ozgur First Passage Percolation with Queried Hints
Kritkorn Karntikoon, Yiheng Shen, Sreenivas Gollapudi, Kostas Kollias, Aaron Schild, Ali K Sinop Free-Form Flows: Make Any Architecture a Normalizing Flow
Felix Draxler, Peter Sorrenson, Lea Zimmermann, Armand Rousselot, Ullrich Köthe Functional Flow Matching
Gavin Kerrigan, Giosue Migliorini, Padhraic Smyth Generative Flow Networks as Entropy-Regularized RL
Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P Vetrov Graph Machine Learning Through the Lens of Bilevel Optimization
Amber Yijia Zheng, Tong He, Yixuan Qiu, Minjie Wang, David Wipf Graph Partitioning with a Move Budget
Mina Dalirrooyfard, Elaheh Fata, Majid Behbahani, Yuriy Nevmyvaka Hodge-Compositional Edge Gaussian Processes
Maosheng Yang, Viacheslav Borovitskiy, Elvin Isufi How Good Is a Single Basin?
Kai Lion, Lorenzo Noci, Thomas Hofmann, Gregor Bachmann Identifiability of Product of Experts Models
Manav Kant, Eric Y Ma, Andrei Staicu, Leonard J Schulman, Spencer Gordon Interpretability Guarantees with Merlin-Arthur Classifiers
Stephan Wäldchen, Kartikey Sharma, Berkant Turan, Max Zimmer, Sebastian Pokutta Intrinsic Gaussian Vector Fields on Manifolds
Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy Is This Model Reliable for Everyone? Testing for Strong Calibration
Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene A Pennello, Berkman Sahiner Large-Scale Gaussian Processes via Alternating Projection
Kaiwen Wu, Jonathan Wenger, Haydn T Jones, Geoff Pleiss, Jacob Gardner Learning 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, Tamas Sarlos, Thomas Weingarten, Adrian Weller Learning Adaptive Kernels for Statistical Independence Tests
Yixin Ren, Yewei Xia, Hao Zhang, Jihong Guan, Shuigeng Zhou Learning Fair Division from Bandit Feedback
Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki Learning Granger Causality from Instance-Wise Self-Attentive Hawkes Processes
Dongxia Wu, Tsuyoshi Ide, Georgios Kollias, Jiri Navratil, Aurelie Lozano, Naoki Abe, Yian Ma, Rose Yu Learning Safety Constraints from Demonstrations with Unknown Rewards
David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause Learning Sparse Codes with Entropy-Based ELBOs
Dmytro Velychko, Simon Damm, Asja Fischer, Jörg Lücke Learning to Defer to a Population: A Meta-Learning Approach
Dharmesh Tailor, Aditya Patra, Rajeev Verma, Putra Manggala, Eric Nalisnick Learning Under Random Distributional Shifts
Kirk C. Bansak, Elisabeth Paulson, Dominik Rothenhaeusler Length Independent PAC-Bayes Bounds for Simple RNNs
Volodimir Mitarchuk, Clara Lacroce, Rémi Eyraud, Rémi Emonet, Amaury Habrard, Guillaume Rabusseau Local Causal Discovery with Linear Non-Gaussian Cyclic Models
Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang Looping in the Human: Collaborative and Explainable Bayesian Optimization
Masaki Adachi, Brady Planden, David Howey, Michael A. Osborne, Sebastian Orbell, Natalia Ares, Krikamol Muandet, Siu Lun Chau Low-Rank MDPs with Continuous Action Spaces
Miruna Oprescu, Andrew Bennett, Nathan Kallus Maximum Entropy GFlowNets with Soft Q-Learning
Sobhan Mohammadpour, Emmanuel Bengio, Emma Frejinger, Pierre-Luc Bacon Mechanics of Next Token Prediction with Self-Attention
Yingcong Li, Yixiao Huang, Muhammed E. Ildiz, Ankit Singh Rawat, Samet Oymak Mixed Models with Multiple Instance Learning
Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian Theis, Francesco Paolo Casale Mixed Variational Flows for Discrete Variables
Gian C. Diluvi, Benjamin Bloem-Reddy, Trevor Campbell Monitoring Machine Learning-Based Risk Prediction Algorithms in the Presence of Performativity
Jean Feng, Alexej Gossmann, Gene A Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio Multi-Armed Bandits with Guaranteed Revenue per Arm
Dorian Baudry, Nadav Merlis, Mathieu Benjamin Molina, Hugo Richard, Vianney Perchet Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
Yinuo Ren, Tesi Xiao, Tanmay Gangwani, Anshuka Rangi, Holakou Rahmanian, Lexing Ying, Subhajit Sanyal Multi-Resolution Active Learning of Fourier Neural Operators
Shibo Li, Xin Yu, Wei Xing, Robert Kirby, Akil Narayan, Shandian Zhe Multi-Resolution Time-Series Transformer for Long-Term Forecasting
Yitian Zhang, Liheng Ma, Soumyasundar Pal, Yingxue Zhang, Mark Coates NoisyMix: Boosting Model Robustness to Common Corruptions
Benjamin Erichson, Soon Hoe Lim, Winnie Xu, Francisco Utrera, Ziang Cao, Michael Mahoney Non-Vacuous Generalization Bounds for Adversarial Risk in Stochastic Neural Networks
Waleed Mustafa, Philipp Liznerski, Antoine Ledent, Dennis Wagner, Puyu Wang, Marius Kloft Offline Policy Evaluation and Optimization Under Confounding
Chinmaya Kausik, Yangyi Lu, Kevin Tan, Maggie Makar, Yixin Wang, Ambuj Tewari Offline Primal-Dual Reinforcement Learning for Linear MDPs
Germano Gabbianelli, Gergely Neu, Matteo Papini, Nneka M Okolo On Cyclical MCMC Sampling
Liwei Wang, Xinru Liu, Aaron Smith, Aguemon Y Atchade On 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 On the Expected Size of Conformal Prediction Sets
Guneet S. Dhillon, George Deligiannidis, Tom Rainforth On the Nyström Approximation for Preconditioning in Kernel Machines
Amirhesam Abedsoltan, Parthe Pandit, Luis Rademacher, Mikhail Belkin Online Distribution Learning with Local Privacy Constraints
Jin Sima, Changlong Wu, Olgica Milenkovic, Wojciech Szpankowski Optimal Budgeted Rejection Sampling for Generative Models
Alexandre Verine, Muni Sreenivas Pydi, Benjamin Negrevergne, Yann Chevaleyre Optimal Estimation of Gaussian (poly)trees
Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya Optimal Sparse Survival Trees
Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin Optimal Zero-Shot Detector for Multi-Armed Attacks
Federica Granese, Marco Romanelli, Pablo Piantanida Pathwise Explanation of ReLU Neural Networks
Seongwoo Lim, Won Jo, Joohyung Lee, Jaesik Choi Pessimistic Off-Policy Multi-Objective Optimization
Shima Alizadeh, Aniruddha Bhargava, Karthick Gopalswamy, Lalit Jain, Branislav Kveton, Ge Liu Policy Learning for Localized Interventions from Observational Data
Myrl G. Marmarelis, Fred Morstatter, Aram Galstyan, Greg Ver Steeg Private Learning with Public Features
Walid Krichene, Nicolas E Mayoraz, Steffen Rendle, Shuang Song, Abhradeep Thakurta, Li Zhang Probabilistic Integral Circuits
Gennaro Gala, Cassio Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur Provable Local Learning Rule by Expert Aggregation for a Hawkes Network
Sophie Jaffard, Samuel Vaiter, Alexandre Muzy, Patricia Reynaud-Bouret Proxy Methods for Domain Adaptation
Katherine Tsai, Stephen R Pfohl, Olawale Salaudeen, Nicole Chiou, Matt Kusner, Alexander D’Amour, Sanmi Koyejo, Arthur Gretton Pure Exploration in Bandits with Linear Constraints
Emil Carlsson, Debabrota Basu, Fredrik Johansson, Devdatt Dubhashi Quantifying Intrinsic Causal Contributions via Structure Preserving Interventions
Dominik Janzing, Patrick Blöbaum, Atalanti A Mastakouri, Philipp M Faller, Lenon Minorics, Kailash Budhathoki Queuing Dynamics of Asynchronous Federated Learning
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines Random Oscillators Network for Time Series Processing
Andrea Ceni, Andrea Cossu, Maximilian W Stölzle, Jingyue Liu, Cosimo Della Santina, Davide Bacciu, Claudio Gallicchio Recovery Guarantees for Distributed-OMP
Chen Amiraz, Robert Krauthgamer, Boaz Nadler Resilient Constrained Reinforcement Learning
Dongsheng Ding, Zhengyan Huan, Alejandro Ribeiro Riemannian Laplace Approximation with the Fisher Metric
Hanlin Yu, Marcelo Hartmann, Bernardo Williams Moreno Sanchez, Mark Girolami, Arto Klami Robust Sparse Voting
Youssef Allouah, Rachid Guerraoui, Lê-Nguyên Hoang, Oscar Villemaud Safe and Interpretable Estimation of Optimal Treatment Regimes
Harsh Parikh, Quinn M Lanners, Zade Akras, Sahar Zafar, M Brandon Westover, Cynthia Rudin, Alexander Volfovsky Sample-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 Sampling-Based Safe Reinforcement Learning for Nonlinear Dynamical Systems
Wesley Suttle, Vipul Kumar Sharma, Krishna Chaitanya Kosaraju, Sivaranjani Seetharaman, Ji Liu, Vijay Gupta, Brian M Sadler Scalable Learning of Item Response Theory Models
Susanne Frick, Amer Krivosija, Alexander Munteanu Scalable Meta-Learning with Gaussian Processes
Petru Tighineanu, Lukas Grossberger, Paul Baireuther, Kathrin Skubch, Stefan Falkner, Julia Vinogradska, Felix Berkenkamp Score Operator Newton Transport
Nisha Chandramoorthy, Florian T Schaefer, Youssef M Marzouk SDEs for Minimax Optimization
Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Proske, Aurelien Lucchi Self-Compatibility: Evaluating Causal Discovery Without Ground Truth
Philipp M. Faller, Leena C. Vankadara, Atalanti A. Mastakouri, Francesco Locatello, Dominik Janzing Sharpened Lazy Incremental Quasi-Newton Method
Aakash Sunil Lahoti, Spandan Senapati, Ketan Rajawat, Alec Koppel Simulating Weighted Automata over Sequences and Trees with Transformers
Michael Rizvi-Martel, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau Simulation-Based Stacking
Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke Simulation-Free Schrödinger Bridges via Score and Flow Matching
Alexander Y. Tong, Nikolay Malkin, Kilian Fatras, Lazar Atanackovic, Yanlei Zhang, Guillaume Huguet, Guy Wolf, Yoshua Bengio Sparse and Faithful Explanations Without Sparse Models
Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin Stochastic Approximation with Delayed Updates: Finite-Time Rates Under Markovian Sampling
Arman Adibi, Nicolò Fabbro, Luca Schenato, Sanjeev Kulkarni, H. Vincent Poor, George J. Pappas, Hamed Hassani, Aritra Mitra Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases
Ruslan Nazykov, Aleksandr Shestakov, Vladimir Solodkin, Aleksandr Beznosikov, Gauthier Gidel, Alexander Gasnikov Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements
Emmanouil Vasileios Vlatakis-Gkaragkounis, Angeliki Giannou, Yudong Chen, Qiaomin Xie Submodular Minimax Optimization: Finding Effective Sets
Loay Raed Mualem, Ethan R Elenberg, Moran Feldman, Amin Karbasi Sum-Max Submodular Bandits
Stephen U. Pasteris, Alberto Rumi, Fabio Vitale, Nicolò Cesa-Bianchi SVARM-IQ: Efficient Approximation of Any-Order Shapley Interactions Through Stratification
Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier Symmetric Equilibrium Learning of VAEs
Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov Tackling the XAI Disagreement Problem with Regional Explanations
Gabriel Laberge, Yann Batiste Pequignot, Mario Marchand, Foutse Khomh The Solution Path of SLOPE
Xavier Dupuis, Patrick Tardivel Thompson Sampling Itself Is Differentially Private
Tingting Ou, Rachel Cummings, Marco Avella Medina Timing as an Action: Learning When to Observe and Act
Helen Zhou, Audrey Huang, Kamyar Azizzadenesheli, David Childers, Zachary Lipton Towards 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 Training Implicit Generative Models via an Invariant Statistical Loss
José Manuel Frutos, Pablo Olmos, Manuel Alberto Vazquez Lopez, Joaquín Míguez Variational Resampling
Oskar Kviman, Nicola Branchini, Víctor Elvira, Jens Lagergren Vector Quantile Regression on Manifolds
Marco Pegoraro, Sanketh Vedula, Aviv A Rosenberg, Irene Tallini, Emanuele Rodola, Alex Bronstein Warped Diffusion for Latent Differentiation Inference
Masahiro Nakano, Hiroki Sakuma, Ryo Nishikimi, Ryohei Shibue, Takashi Sato, Tomoharu Iwata, Kunio Kashino Weight-Sharing Regularization
Mehran Shakerinava, Motahareh MS Sohrabi, Siamak Ravanbakhsh, Simon Lacoste-Julien