Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods. - Gergely Neu; Csaba Szepesvári (UAI, 2007)

Training parsers by inverse reinforcement learning. - Gergely Neu; Csaba Szepesvári (Mach. Learn., 2009)

The Online Loop-free Stochastic Shortest-Path Problem. - Gergely Neu; András György 001; Csaba Szepesvári (COLT, 2010)

The adversarial stochastic shortest path problem with unknown transition probabilities. - Gergely Neu; András György 001; Csaba Szepesvári (AISTATS, 2012)

An Efficient Algorithm for Learning with Semi-bandit Feedback. - Gergely Neu; Gábor Bartók (ALT, 2013)

Prediction by random-walk perturbation. - Luc Devroye; Gábor Lugosi; Gergely Neu (COLT, 2013)

Online learning in episodic Markovian decision processes by relative entropy policy search. - Alexander Zimin; Gergely Neu (NIPS, 2013)

Online tanulás nemstacionárius Markov döntési folyamatokban - Gergely Neu (2013)

Efficient learning by implicit exploration in bandit problems with side observations. - Tomás Kocák; Gergely Neu; Michal Valko; Rémi Munos (NIPS, 2014)

Online combinatorial optimization with stochastic decision sets and adversarial losses. - Gergely Neu; Michal Valko (NIPS, 2014)

Exploiting easy data in online optimization. - Amir Sani; Gergely Neu; Alessandro Lazaric (NIPS, 2014)

Online learning in MDPs with side information. - Yasin Abbasi-Yadkori; Gergely Neu (CoRR, 2014)

Online Markov Decision Processes Under Bandit Feedback. - Gergely Neu; András György 001; Csaba Szepesvári; András Antos (IEEE Trans. Autom. Control., 2014)

Near-Optimal Rates for Limited-Delay Universal Lossy Source Coding. - András György 001; Gergely Neu (IEEE Trans. Inf. Theory, 2014)

First-order regret bounds for combinatorial semi-bandits. - Gergely Neu (COLT, 2015)

Explore no more: Improved high-probability regret bounds for non-stochastic bandits. - Gergely Neu (NIPS, 2015)

Random-Walk Perturbations for Online Combinatorial Optimization. - Luc Devroye; Gábor Lugosi; Gergely Neu (IEEE Trans. Inf. Theory, 2015)

Online Learning with Noisy Side Observations. - Tomás Kocák; Gergely Neu; Michal Valko (AISTATS, 2016)

Online learning with Erdos-Renyi side-observation graphs. - Tomás Kocák; Gergely Neu; Michal Valko (UAI, 2016)

Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits. - Gergely Neu; Gábor Bartók (J. Mach. Learn. Res., 2016)

Fast rates for online learning in Linearly Solvable Markov Decision Processes. - Gergely Neu; Vicenç Gómez (COLT, 2017)

Algorithmic Stability and Hypothesis Complexity. - Tongliang Liu; Gábor Lugosi; Gergely Neu; Dacheng Tao (ICML, 2017)

Boltzmann Exploration Done Right. - Nicolò Cesa-Bianchi; Claudio Gentile; Gergely Neu; Gábor Lugosi (NIPS, 2017)

A unified view of entropy-regularized Markov decision processes. - Gergely Neu; Anders Jonsson 001; Vicenç Gómez (CoRR, 2017)

On the Hardness of Inventory Management with Censored Demand Data. - Gábor Lugosi; Mihalis G. Markakis; Gergely Neu (CoRR, 2017)

Iterate Averaging as Regularization for Stochastic Gradient Descent. - Gergely Neu; Lorenzo Rosasco (COLT, 2018)

Wireless Optimisation via Convex Bandits: Unlicensed LTE/WiFi Coexistence. - Cristina Cano; Gergely Neu (NetAI@SIGCOMM, 2018)

Online Influence Maximization with Local Observations. - Gábor Lugosi; Gergely Neu; Julia Olkhovskaya (ALT, 2019)

Bandit Principal Component Analysis. - Wojciech Kotlowski; Gergely Neu (COLT, 2019)

Beating SGD Saturation with Tail-Averaging and Minibatching. - Nicole Mücke; Gergely Neu; Lorenzo Rosasco (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; André Barreto 001; Gergely Neu (NeurIPS, 2019)

Collaborative Spatial Reuse in wireless networks via selfish Multi-Armed Bandits. - Francesc Wilhelmi; Cristina Cano; Gergely Neu; Boris Bellalta; Anders Jonsson 001; Sergio Barrachina-Muñoz (Ad Hoc Networks, 2019)

Potential and pitfalls of Multi-Armed Bandits for decentralized Spatial Reuse in WLANs. - Francesc Wilhelmi; Sergio Barrachina-Muñoz; Boris Bellalta; Cristina Cano; Anders Jonsson 001; Gergely Neu (J. Netw. Comput. Appl., 2019)

Algorithmic Learning Theory 2020: Preface. - Aryeh Kontorovich; Gergely Neu (ALT, 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 (COLT, 2020)

Faster saddle-point optimization for solving large-scale Markov decision processes. - Joan Bas-Serrano; Gergely Neu (L4DC, 2020)

A Unifying View of Optimism in Episodic Reinforcement Learning. - Gergely Neu; Ciara Pike-Burke (NeurIPS, 2020)

Logistic Q-Learning. - Joan Bas-Serrano; Sebastian Curi; Andreas Krause 001; Gergely Neu (AISTATS, 2021)

Convex Q-Learning. - Fan Lu; Prashant G. Mehta; Sean P. Meyn; Gergely Neu (ACC, 2021)

Information-Theoretic Generalization Bounds for Stochastic Gradient Descent. - Gergely Neu (COLT, 2021)

Online learning in MDPs with linear function approximation and bandit feedback. - Gergely Neu; Julia Olkhovskaya (NeurIPS, 2021)

Learning to maximize global influence from local observations. - Gábor Lugosi; Gergely Neu; Julia Olkhovskaya (CoRR, 2021)

Robustness and risk management via distributional dynamic programming. - Mastane Achab; Gergely Neu (CoRR, 2021)

Convex Analytic Theory for Convex Q-Learning. - Fan Lu; Prashant G. Mehta; Sean P. Meyn; Gergely Neu (CDC, 2022)

Generalization Bounds via Convex Analysis. - Gábor Lugosi; Gergely Neu (COLT, 2022)

Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. - Gergely Neu; Julia Olkhovskaya; Matteo Papini; Ludovic Schwartz (NeurIPS, 2022)

Proximal Point Imitation Learning. - Luca Viano; Angeliki Kamoutsi; Gergely Neu; Igor Krawczuk; Volkan Cevher (NeurIPS, 2022)

Sufficient Exploration for Convex Q-learning. - Fan Lu; Prashant G. Mehta; Sean P. Meyn; Gergely Neu (CoRR, 2022)

Nonstochastic Contextual Combinatorial Bandits. - Lukas Zierahn; Dirk van der Hoeven; Nicolò Cesa-Bianchi; Gergely Neu (AISTATS, 2023)

Online Learning with Off-Policy Feedback. - Germano Gabbianelli; Gergely Neu; Matteo Papini (ALT, 2023)

Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization. - Gergely Neu; Nneka Okolo (ALT, 2023)

Optimistic Planning by Regularized Dynamic Programming. - Antoine Moulin; Gergely Neu (ICML, 2023)

First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. - Julia Olkhovskaya; Jack J. Mayo; Tim van Erven; Gergely Neu; Chen-Yu Wei (NeurIPS, 2023)

Online-to-PAC Conversions: Generalization Bounds via Regret Analysis. - Gábor Lugosi; Gergely Neu (CoRR, 2023)

Offline Primal-Dual Reinforcement Learning for Linear MDPs. - Germano Gabbianelli; Gergely Neu; Matteo Papini; Nneka Okolo (AISTATS, 2024)

Importance-Weighted Offline Learning Done Right. - Germano Gabbianelli; Gergely Neu; Matteo Papini (ALT, 2024)

Adversarial Contextual Bandits Go Kernelized. - Gergely Neu; Julia Olkhovskaya; Sattar Vakili (ALT, 2024)

Optimistic Information Directed Sampling. - Gergely Neu; Matteo Papini; Ludovic Schwartz (COLT, 2024)

Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization. - Gergely Neu; Nneka Okolo (ICML, 2024)

Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently. - Sergio Calo; Anders Jonsson 001; Gergely Neu; Ludovic Schwartz; Javier Segovia-Aguas (NeurIPS, 2024)

On the Hardness of Learning from Censored and Nonstationary Demand. - Gábor Lugosi; Mihalis G. Markakis; Gergely Neu (INFORMS J. Optim., 2024)

Online-to-PAC generalization bounds under graph-mixing dependencies. - Baptiste Abélès; Gergely Neu; Eugenio Clerico (AISTATS, 2025)

Offline RL via Feature-Occupancy Gradient Ascent. - Gergely Neu; Nneka Okolo (AISTATS, 2025)

Generalization bounds for mixing processes via delayed online-to-PAC conversions. - Baptiste Abélès; Eugenio Clerico; Gergely Neu (ALT, 2025)

Optimistically Optimistic Exploration for Provably Efficient Infinite-Horizon Reinforcement and Imitation Learning. - Antoine Moulin; Gergely Neu; Luca Viano (COLT, 2025)

Confidence Sequences for Generalized Linear Models via Regret Analysis. - Eugenio Clerico; Hamish Flynn; Wojciech Kotlowski; Gergely Neu (CoRR, 2025)

Distances for Markov chains from sample streams. - Sergio Calo; Anders Jonsson 001; Gergely Neu; Ludovic Schwartz; Javier Segovia-Aguas (CoRR, 2025)

Inverse Q-Learning Done Right: Offline Imitation Learning in Qπ-Realizable MDPs. - Antoine Moulin; Gergely Neu; Luca Viano (CoRR, 2025)

Linear Bandits with Non-i.i.d. Noise. - Baptiste Abélès; Eugenio Clerico; Hamish Flynn; Gergely Neu (CoRR, 2025)