ML Anthology

Neu, Gergely

57 publications

NeurIPS 2025 Distances for Markov Chains from Sample Streams Sergio Calo, Anders Jonsson, Gergely Neu, Ludovic Schwartz, Javier Segovia-Aguas
ALT 2025 Generalization Bounds for Mixing Processes via Delayed Online-to-PAC Conversions Baptiste Abélès, Eugenio Clerico, Gergely Neu
NeurIPS 2025 Inverse Q-Learning Done Right: Offline Imitation Learning in $Q^\pi$-Realizable MDPs Antoine Moulin, Gergely Neu, Luca Viano
AISTATS 2025 Offline RL via Feature-Occupancy Gradient Ascent Gergely Neu, Nneka Okolo
AISTATS 2025 Online-to-PAC Generalization Bounds Under Graph-Mixing Dependencies Baptiste Abélès, Gergely Neu, Eugenio Clerico
COLT 2025 Optimistically Optimistic Exploration for Provably Efficient Infinite-Horizon Reinforcement and Imitation Learning Antoine Moulin, Gergely Neu, Luca Viano
NeurIPS 2025 Sparse Optimistic Information Directed Sampling Ludovic Schwartz, Hamish Flynn, Gergely Neu
ALT 2024 Adversarial Contextual Bandits Go Kernelized Gergely Neu, Julia Olkhovskaya, Sattar Vakili
NeurIPS 2024 Bisimulation Metrics Are Optimal Transport Distances, and Can Be Computed Efficiently Sergio Calo, Anders Jonsson, Gergely Neu, Ludovic Schwartz, Javier Segovia-Aguas
ICMLW 2024 Bisimulation Metrics Are Optimal Transport Distances, and Can Be Computed Efficiently Sergio Calo, Anders Jonsson, Gergely Neu, Ludovic Schwartz, Javier Segovia-Aguas
ICML 2024 Dealing with Unbounded Gradients in Stochastic Saddle-Point Optimization Gergely Neu, Nneka Okolo
ALT 2024 Importance-Weighted Offline Learning Done Right Germano Gabbianelli, Gergely Neu, Matteo Papini
AISTATS 2024 Offline Primal-Dual Reinforcement Learning for Linear MDPs Germano Gabbianelli, Gergely Neu, Matteo Papini, Nneka M Okolo
ICMLW 2024 Offline RL via Feature-Occupancy Gradient Ascent Gergely Neu, Nneka Okolo
COLT 2024 Optimistic Information Directed Sampling Gergely Neu, Matteo Papini, Ludovic Schwartz
ICMLW 2024 Optimistic Information Directed Sampling Gergely Neu, Matteo Papini, Ludovic Schwartz
COLT 2023 Conference on Learning Theory 2023: Preface Gergely Neu, Lorenzo Rosasco
ALT 2023 Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization Gergely Neu, Nneka Okolo
NeurIPS 2023 First- and Second-Order Bounds for Adversarial Linear Contextual Bandits Julia Olkhovskaya, Jack Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei
AISTATS 2023 Nonstochastic Contextual Combinatorial Bandits Lukas Zierahn, Dirk Hoeven, Nicolò Cesa-Bianchi, Gergely Neu
ALT 2023 Online Learning with Off-Policy Feedback Germano Gabbianelli, Gergely Neu, Matteo Papini
ICML 2023 Optimistic Planning by Regularized Dynamic Programming Antoine Moulin, Gergely Neu
COLT 2022 Generalization Bounds via Convex Analysis Gabor Lugosi, Gergely Neu
NeurIPS 2022 Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits Gergely Neu, Iuliia Olkhovskaia, Matteo Papini, Ludovic Schwartz
NeurIPS 2022 Proximal Point Imitation Learning Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher
AISTATS 2021 Logistic Q-Learning Joan Bas-Serrano, Sebastian Curi, Andreas Krause, Gergely Neu
COLT 2021 Information-Theoretic Generalization Bounds for Stochastic Gradient Descent Gergely Neu, Gintare Karolina Dziugaite, Mahdi Haghifam, Daniel M. Roy
NeurIPS 2021 Online Learning in MDPs with Linear Function Approximation and Bandit Feedback. Gergely Neu, Julia Olkhovskaya
NeurIPS 2020 A Unifying View of Optimism in Episodic Reinforcement Learning Gergely Neu, Ciara Pike-Burke
ALT 2020 Algorithmic Learning Theory 2020: Preface Aryeh Kontorovich, Gergely Neu
COLT 2020 Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits Gergely Neu, Julia Olkhovskaya
COLT 2020 Fast Rates for Online Prediction with Abstention Gergely Neu, Nikita Zhivotovskiy
L4DC 2020 Faster Saddle-Point Optimization for Solving Large-Scale Markov Decision Processes Joan Bas Serrano, Gergely Neu
NeurIPS 2019 Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A Mann, Andre Barreto, Gergely Neu
COLT 2019 Bandit Principal Component Analysis Wojciech Kotłowski, Gergely Neu
NeurIPS 2019 Beating SGD Saturation with Tail-Averaging and Minibatching Nicole Muecke, Gergely Neu, Lorenzo Rosasco
ALT 2019 Online Influence Maximization with Local Observations Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
COLT 2018 Iterate Averaging as Regularization for Stochastic Gradient Descent Gergely Neu, Lorenzo Rosasco
ICML 2017 Algorithmic Stability and Hypothesis Complexity Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao
NeurIPS 2017 Boltzmann Exploration Done Right Nicolò Cesa-Bianchi, Claudio Gentile, Gabor Lugosi, Gergely Neu
COLT 2017 Fast Rates for Online Learning in Linearly Solvable Markov Decision Processes Gergely Neu, Vicenç Gómez
JMLR 2016 Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits Gergely Neu, Gábor Bartók
UAI 2016 Online Learning with Erdos-Renyi Side-Observation Graphs Tomás Kocák, Gergely Neu, Michal Valko
AISTATS 2016 Online Learning with Noisy Side Observations Tomás Kocák, Gergely Neu, Michal Valko
NeurIPS 2015 Explore No More: Improved High-Probability Regret Bounds for Non-Stochastic Bandits Gergely Neu
COLT 2015 First-Order Regret Bounds for Combinatorial Semi-Bandits Gergely Neu
NeurIPS 2014 Efficient Learning by Implicit Exploration in Bandit Problems with Side Observations Tomáš Kocák, Gergely Neu, Michal Valko, Remi Munos
NeurIPS 2014 Exploiting Easy Data in Online Optimization Amir Sani, Gergely Neu, Alessandro Lazaric
NeurIPS 2014 Online Combinatorial Optimization with Stochastic Decision Sets and Adversarial Losses Gergely Neu, Michal Valko
ALT 2013 An Efficient Algorithm for Learning with Semi-Bandit Feedback Gergely Neu, Gábor Bartók
NeurIPS 2013 Online Learning in Episodic Markovian Decision Processes by Relative Entropy Policy Search Alexander Zimin, Gergely Neu
COLT 2013 Prediction by Random-Walk Perturbation Luc Devroye, Gábor Lugosi, Gergely Neu
AISTATS 2012 The Adversarial Stochastic Shortest Path Problem with Unknown Transition Probabilities Gergely Neu, Andras Gyorgy, Csaba Szepesvari
NeurIPS 2010 Online Markov Decision Processes Under Bandit Feedback Gergely Neu, Andras Antos, András György, Csaba Szepesvári
COLT 2010 The Online Loop-Free Stochastic Shortest-Path Problem Gergely Neu, András György, Csaba Szepesvári
MLJ 2009 Training Parsers by Inverse Reinforcement Learning Gergely Neu, Csaba Szepesvári
UAI 2007 Apprenticeship Learning Using Inverse Reinforcement Learning and Gradient Methods Gergely Neu, Csaba Szepesvári