Gergely Neu

ORCID

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 82

Top Venues

Publications

Baptiste Abeles, Eugenio Clerico, Gergely Neu
Generalization bounds for mixing processes via delayed online-to-PAC conversions. CoRR (2024)
Germano Gabbianelli, Gergely Neu, Matteo Papini
Importance-Weighted Offline Learning Done Right. ALT (2024)
Gergely Neu, Nneka Okolo
Dealing with unbounded gradients in stochastic saddle-point optimization. CoRR (2024)
Sergio Calo, Anders Jonsson, Gergely Neu, Ludovic Schwartz, Javier Segovia-Aguas
Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently. CoRR (2024)
Gergely Neu, Matteo Papini, Ludovic Schwartz
Optimistic Information Directed Sampling. COLT (2024)
Gergely Neu, Nneka Okolo
Offline RL via Feature-Occupancy Gradient Ascent. CoRR (2024)
Germano Gabbianelli, Gergely Neu, Matteo Papini, Nneka Okolo
Offline Primal-Dual Reinforcement Learning for Linear MDPs. AISTATS (2024)
Gábor Lugosi, Mihalis G. Markakis, Gergely Neu
On the Hardness of Learning from Censored and Nonstationary Demand. INFORMS J. Optim. 6 (2) (2024)
Gergely Neu, Matteo Papini, Ludovic Schwartz
Optimistic Information Directed Sampling. CoRR (2024)
Gergely Neu, Julia Olkhovskaya, Sattar Vakili
Adversarial Contextual Bandits Go Kernelized. ALT (2024)
Antoine Moulin, Gergely Neu
Optimistic Planning by Regularized Dynamic Programming. ICML (2023)
Gergely Neu, Nneka Okolo
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization. ALT (2023)
Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. NeurIPS (2023)
Gergely Neu, Julia Olkhovskaya, Sattar Vakili
Adversarial Contextual Bandits Go Kernelized. CoRR (2023)
Germano Gabbianelli, Gergely Neu, Nneka Okolo, Matteo Papini
Offline Primal-Dual Reinforcement Learning for Linear MDPs. CoRR (2023)
Antoine Moulin, Gergely Neu
Optimistic Planning by Regularized Dynamic Programming. CoRR (2023)
Julia Olkhovskaya, Jack J. Mayo, Tim van Erven, Gergely Neu, Chen-Yu Wei
First- and Second-Order Bounds for Adversarial Linear Contextual Bandits. CoRR (2023)
Lukas Zierahn, Dirk van der Hoeven, Nicolò Cesa-Bianchi, Gergely Neu
Nonstochastic Contextual Combinatorial Bandits. AISTATS (2023)
Gábor Lugosi, Gergely Neu
Online-to-PAC Conversions: Generalization Bounds via Regret Analysis. CoRR (2023)
Germano Gabbianelli, Gergely Neu, Matteo Papini
Importance-Weighted Offline Learning Done Right. CoRR (2023)
Germano Gabbianelli, Gergely Neu, Matteo Papini
Online Learning with Off-Policy Feedback. ALT (2023)
Fan Lu, Prashant G. Mehta, Sean P. Meyn, Gergely Neu
Convex Analytic Theory for Convex Q-Learning. CDC (2022)
Gergely Neu, Gábor Lugosi
Generalization Bounds via Convex Analysis. CoRR (2022)
Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. CoRR (2022)
Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher
Proximal Point Imitation Learning. CoRR (2022)
Gergely Neu, Iuliia Olkhovskaia, Matteo Papini, Ludovic Schwartz
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits. NeurIPS (2022)
Gábor Lugosi, Gergely Neu
Generalization Bounds via Convex Analysis. COLT (2022)
Gergely Neu, Nneka Okolo
Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization. CoRR (2022)
Luca Viano, Angeliki Kamoutsi, Gergely Neu, Igor Krawczuk, Volkan Cevher
Proximal Point Imitation Learning. NeurIPS (2022)
Fan Lu, Prashant G. Mehta, Sean Meyn, Gergely Neu
Sufficient Exploration for Convex Q-learning. CoRR (2022)
Germano Gabbianelli, Matteo Papini, Gergely Neu
Online Learning with Off-Policy Feedback. CoRR (2022)
Gergely Neu
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent. COLT (2021)
Mastane Achab, Gergely Neu
Robustness and risk management via distributional dynamic programming. CoRR (2021)
Gergely Neu
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent. CoRR (2021)
Fan Lu, Prashant G. Mehta, Sean P. Meyn, Gergely Neu
Convex Q-Learning. ACC (2021)
Joan Bas-Serrano, Sebastian Curi, Andreas Krause, Gergely Neu
Logistic Q-Learning. AISTATS (2021)
Gergely Neu, Julia Olkhovskaya
Online learning in MDPs with linear function approximation and bandit feedback. NeurIPS (2021)
Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
Learning to maximize global influence from local observations. CoRR (2021)
Joan Bas-Serrano, Sebastian Curi, Andreas Krause, Gergely Neu
Logistic $Q$-Learning. CoRR (2020)
Gergely Neu, Nikita Zhivotovskiy
Fast Rates for Online Prediction with Abstention. CoRR (2020)
Gergely Neu, Ciara Pike-Burke
A Unifying View of Optimism in Episodic Reinforcement Learning. CoRR (2020)
Gergely Neu, Ciara Pike-Burke
A Unifying View of Optimism in Episodic Reinforcement Learning. NeurIPS (2020)
Aryeh Kontorovich, Gergely Neu
Algorithmic Learning Theory 2020: Preface. ALT (2020)
Gergely Neu, Julia Olkhovskaya
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits. CoRR (2020)
Gergely Neu, Nikita Zhivotovskiy
Fast Rates for Online Prediction with Abstention. COLT (2020)
Gergely Neu, Julia Olkhovskaya
Efficient and robust algorithms for adversarial linear contextual bandits. COLT (2020)
Gergely Neu, Julia Olkhovskaya
Online learning in MDPs with linear function approximation and bandit feedback. CoRR (2020)
Joan Bas-Serrano, Gergely Neu
Faster saddle-point optimization for solving large-scale Markov decision processes. L4DC (2020)
Francesc Wilhelmi, Cristina Cano, Gergely Neu, Boris Bellalta, Anders Jonsson, Sergio Barrachina-Muñoz
Collaborative Spatial Reuse in wireless networks via selfish Multi-Armed Bandits. Ad Hoc Networks 88 (2019)
Joan Bas-Serrano, Gergely Neu
Faster saddle-point optimization for solving large-scale Markov decision processes. CoRR (2019)
Wojciech Kotlowski, Gergely Neu
Bandit Principal Component Analysis. COLT (2019)
Nicole Mücke, Gergely Neu, Lorenzo Rosasco
Beating SGD Saturation with Tail-Averaging and Minibatching. CoRR (2019)
Wojciech Kotlowski, Gergely Neu
Bandit Principal Component Analysis. CoRR (2019)
Carlos Riquelme, Hugo Penedones, Damien Vincent, Hartmut Maennel, Sylvain Gelly, Timothy A. Mann, André Barreto, Gergely Neu
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. NeurIPS (2019)
Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson, Gergely Neu
Potential and pitfalls of Multi-Armed Bandits for decentralized Spatial Reuse in WLANs. J. Netw. Comput. Appl. 127 (2019)
Gábor Lugosi, Gergely Neu, Julia Olkhovskaya
Online Influence Maximization with Local Observations. ALT (2019)
Hugo Penedones, Carlos Riquelme, Damien Vincent, Hartmut Maennel, Timothy A. Mann, André Barreto, Sylvain Gelly, Gergely Neu
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. CoRR (2019)
Nicole Mücke, Gergely Neu, Lorenzo Rosasco
Beating SGD Saturation with Tail-Averaging and Minibatching. NeurIPS (2019)
Cristina Cano, Gergely Neu
Wireless Optimisation via Convex Bandits: Unlicensed LTE/WiFi Coexistence. CoRR (2018)
Francesc Wilhelmi, Sergio Barrachina-Muñoz, Cristina Cano, Boris Bellalta, Anders Jonsson, Gergely Neu
Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs. CoRR (2018)
Cristina Cano, Gergely Neu
Wireless Optimisation via Convex Bandits: Unlicensed LTE/WiFi Coexistence. NetAI@SIGCOMM (2018)
Gergely Neu, Lorenzo Rosasco
Iterate Averaging as Regularization for Stochastic Gradient Descent. COLT (2018)
Gergely Neu, Lorenzo Rosasco
Iterate averaging as regularization for stochastic gradient descent. CoRR (2018)
Julia Olkhovskaya, Gergely Neu, Gábor Lugosi
Online Influence Maximization with Local Observations. CoRR (2018)
Francesc Wilhelmi, Cristina Cano, Gergely Neu, Boris Bellalta, Anders Jonsson, Sergio Barrachina-Muñoz
Collaborative Spatial Reuse in Wireless Networks via Selfish Multi-Armed Bandits. CoRR (2017)
Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao
Algorithmic Stability and Hypothesis Complexity. ICML (2017)
Gergely Neu, Anders Jonsson, Vicenç Gómez
A unified view of entropy-regularized Markov decision processes. CoRR (2017)
Nicolò Cesa-Bianchi, Claudio Gentile, Gábor Lugosi, Gergely Neu
Boltzmann Exploration Done Right. CoRR (2017)
Gergely Neu, Vicenç Gómez
Fast rates for online learning in Linearly Solvable Markov Decision Processes. COLT (2017)
Tongliang Liu, Gábor Lugosi, Gergely Neu, Dacheng Tao
Algorithmic stability and hypothesis complexity. CoRR (2017)
Nicolò Cesa-Bianchi, Claudio Gentile, Gergely Neu, Gábor Lugosi
Boltzmann Exploration Done Right. NIPS (2017)
Gábor Lugosi, Mihalis G. Markakis, Gergely Neu
On the Hardness of Inventory Management with Censored Demand Data. CoRR (2017)
Gergely Neu, Vicenç Gómez
Fast rates for online learning in Linearly Solvable Markov Decision Processes. CoRR (2017)
Tomás Kocák, Gergely Neu, Michal Valko
Online Learning with Noisy Side Observations. AISTATS (2016)
Gergely Neu, Gábor Bartók
Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits. J. Mach. Learn. Res. 17 (2016)
Tomás Kocák, Gergely Neu, Michal Valko
Online learning with Erdos-Renyi side-observation graphs. UAI (2016)
Gergely Neu
Explore no more: improved high-probability regret bounds for non-stochastic bandits. CoRR (2015)
Gergely Neu, Gábor Bartók
Importance weighting without importance weights: An efficient algorithm for combinatorial semi-bandits. CoRR (2015)
Gergely Neu
Explore no more: Improved high-probability regret bounds for non-stochastic bandits. NIPS (2015)
Gergely Neu
First-order regret bounds for combinatorial semi-bandits. COLT (2015)
Luc Devroye, Gábor Lugosi, Gergely Neu
Random-Walk Perturbations for Online Combinatorial Optimization. IEEE Trans. Inf. Theory 61 (7) (2015)
Gergely Neu
First-order regret bounds for combinatorial semi-bandits. CoRR (2015)
Gergely Neu, Michal Valko
Online combinatorial optimization with stochastic decision sets and adversarial losses. NIPS (2014)
András György, Gergely Neu
Near-Optimal Rates for Limited-Delay Universal Lossy Source Coding. IEEE Trans. Inf. Theory 60 (5) (2014)
Yasin Abbasi-Yadkori, Gergely Neu
Online learning in MDPs with side information. CoRR (2014)
Amir Sani, Gergely Neu, Alessandro Lazaric
Exploiting easy data in online optimization. NIPS (2014)
Gergely Neu, András György, Csaba Szepesvári, András Antos
Online Markov Decision Processes Under Bandit Feedback. IEEE Trans. Autom. Control. 59 (3) (2014)
Tomás Kocák, Gergely Neu, Michal Valko, Rémi Munos
Efficient learning by implicit exploration in bandit problems with side observations. NIPS (2014)
Alexander Zimin, Gergely Neu
Online learning in episodic Markovian decision processes by relative entropy policy search. NIPS (2013)
Gergely Neu, Gábor Bartók
An Efficient Algorithm for Learning with Semi-bandit Feedback. ALT (2013)
Gergely Neu, Gábor Bartók
An efficient algorithm for learning with semi-bandit feedback CoRR (2013)
Luc Devroye, Gábor Lugosi, Gergely Neu
Prediction by Random-Walk Perturbation CoRR (2013)
Luc Devroye, Gábor Lugosi, Gergely Neu
Prediction by random-walk perturbation. COLT (2013)
Gergely Neu, András György, Csaba Szepesvári
The adversarial stochastic shortest path problem with unknown transition probabilities. AISTATS (2012)
Gergely Neu, Csaba Szepesvári
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods CoRR (2012)
András György, Gergely Neu
Near-optimal rates for limited-delay universal lossy source coding. ISIT (2011)
Gergely Neu, András György, Csaba Szepesvári, András Antos
Online Markov Decision Processes under Bandit Feedback. NIPS (2010)
Gergely Neu, András György, Csaba Szepesvári
The Online Loop-free Stochastic Shortest-Path Problem. COLT (2010)
Gergely Neu, Csaba Szepesvári
Training parsers by inverse reinforcement learning. Mach. Learn. 77 (2-3) (2009)
Gergely Neu, Csaba Szepesvári
Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods. UAI (2007)

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Publication Activity (10 Years)

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Gergely Neu