We stand with Ukraine

We stand with Ukraine

Gergely Neu

Orcid: 0000-0001-6287-3796

Affiliations:

Pompeu Fabra University, DTIC, Barcelona, Spain

According to our database¹, Gergely Neu authored at least 60 papers between 2007 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

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Links

Online presence:

On csauthors.net:

Bibliography

2024

Optimistic Information Directed Sampling.

[BibT_eX]

[DOI]

,

,

Ludovic Schwartz

CoRR, 2024

Dealing with unbounded gradients in stochastic saddle-point optimization.

[BibT_eX]

[DOI]

,

CoRR, 2024

2023

Adversarial Contextual Bandits Go Kernelized.

[BibT_eX]

[DOI]

,

Julia Olkhovskaya

,

CoRR, 2023

Importance-Weighted Offline Learning Done Right.

[BibT_eX]

[DOI]

Germano Gabbianelli

,

,

CoRR, 2023

Online-to-PAC Conversions: Generalization Bounds via Regret Analysis.

[BibT_eX]

[DOI]

,

CoRR, 2023

Offline Primal-Dual Reinforcement Learning for Linear MDPs.

[BibT_eX]

[DOI]

Germano Gabbianelli

,

,

,

CoRR, 2023

First- and Second-Order Bounds for Adversarial Linear Contextual Bandits.

[BibT_eX]

[DOI]

Julia Olkhovskaya

,

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Optimistic Planning by Regularized Dynamic Programming.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Machine Learning, 2023

Efficient Global Planning in Large MDPs via Stochastic Primal-Dual Optimization.

[BibT_eX]

[DOI]

,

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

Online Learning with Off-Policy Feedback.

[BibT_eX]

[DOI]

Germano Gabbianelli

,

,

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

Nonstochastic Contextual Combinatorial Bandits.

[BibT_eX]

[DOI]

,

Dirk van der Hoeven

,

Nicolò Cesa-Bianchi

,

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Sufficient Exploration for Convex Q-learning.

[BibT_eX]

[DOI]

,

Prashant G. Mehta

,

,

CoRR, 2022

Proximal Point Imitation Learning.

[BibT_eX]

[DOI]

,

Angeliki Kamoutsi

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits.

[BibT_eX]

[DOI]

,

Julia Olkhovskaya

,

,

Ludovic Schwartz

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Generalization Bounds via Convex Analysis.

[BibT_eX]

[DOI]

,

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Convex Analytic Theory for Convex Q-Learning.

[BibT_eX]

[DOI]

,

Prashant G. Mehta

,

,

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021

Robustness and risk management via distributional dynamic programming.

[BibT_eX]

[DOI]

,

CoRR, 2021

Learning to maximize global influence from local observations.

[BibT_eX]

[DOI]

,

,

Julia Olkhovskaya

CoRR, 2021

Online learning in MDPs with linear function approximation and bandit feedback.

[BibT_eX]

[DOI]

,

Julia Olkhovskaya

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Information-Theoretic Generalization Bounds for Stochastic Gradient Descent.

[BibT_eX]

[DOI]

Proceedings of the Conference on Learning Theory, 2021

Convex Q-Learning.

[BibT_eX]

[DOI]

,

Prashant G. Mehta

,

,

Proceedings of the 2021 American Control Conference, 2021

Logistic Q-Learning.

[BibT_eX]

[DOI]

Joan Bas-Serrano

,

,

,

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

A Unifying View of Optimism in Episodic Reinforcement Learning.

[BibT_eX]

[DOI]

,

Ciara Pike-Burke

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Faster saddle-point optimization for solving large-scale Markov decision processes.

[BibT_eX]

[DOI]

Joan Bas-Serrano

,

Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Fast Rates for Online Prediction with Abstention.

[BibT_eX]

[DOI]

,

Nikita Zhivotovskiy

Proceedings of the Conference on Learning Theory, 2020

Efficient and robust algorithms for adversarial linear contextual bandits.

[BibT_eX]

[DOI]

,

Julia Olkhovskaya

Proceedings of the Conference on Learning Theory, 2020

Algorithmic Learning Theory 2020: Preface.

[BibT_eX]

[DOI]

Aryeh Kontorovich

,

Proceedings of the Algorithmic Learning Theory, 2020

2019

Potential and pitfalls of Multi-Armed Bandits for decentralized Spatial Reuse in WLANs.

[BibT_eX]

[DOI]

Francesc Wilhelmi

,

Sergio Barrachina-Muñoz

,

,

,

,

J. Netw. Comput. Appl., 2019

Collaborative Spatial Reuse in wireless networks via selfish Multi-Armed Bandits.

[BibT_eX]

[DOI]

Francesc Wilhelmi

,

,

,

,

,

Sergio Barrachina-Muñoz

Ad Hoc Networks, 2019

Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates.

[BibT_eX]

[DOI]

Carlos Riquelme

,

,

,

Hartmut Maennel

,

,

Timothy A. Mann

,

,

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Beating SGD Saturation with Tail-Averaging and Minibatching.

[BibT_eX]

[DOI]

,

,

Lorenzo Rosasco

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bandit Principal Component Analysis.

[BibT_eX]

[DOI]

Wojciech Kotlowski

,

Proceedings of the Conference on Learning Theory, 2019

Online Influence Maximization with Local Observations.

[BibT_eX]

[DOI]

,

,

Julia Olkhovskaya

Proceedings of the Algorithmic Learning Theory, 2019

2018

Wireless Optimisation via Convex Bandits: Unlicensed LTE/WiFi Coexistence.

[BibT_eX]

[DOI]

,

Proceedings of the 2018 Workshop on Network Meets AI & ML, 2018

Iterate Averaging as Regularization for Stochastic Gradient Descent.

[BibT_eX]

[DOI]

,

Lorenzo Rosasco

Proceedings of the Conference On Learning Theory, 2018

2017

On the Hardness of Inventory Management with Censored Demand Data.

[BibT_eX]

[DOI]

,

Mihalis G. Markakis

,

CoRR, 2017

A unified view of entropy-regularized Markov decision processes.

[BibT_eX]

[DOI]

,

,

CoRR, 2017

Boltzmann Exploration Done Right.

[BibT_eX]

[DOI]

Nicolò Cesa-Bianchi

,

Claudio Gentile

,

,

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Algorithmic Stability and Hypothesis Complexity.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the 34th International Conference on Machine Learning, 2017

Fast rates for online learning in Linearly Solvable Markov Decision Processes.

[BibT_eX]

[DOI]

,

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits.

[BibT_eX]

[DOI]

,

J. Mach. Learn. Res., 2016

Online learning with Erdos-Renyi side-observation graphs.

[BibT_eX]

[DOI]

,

,

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Online Learning with Noisy Side Observations.

[BibT_eX]

[DOI]

,

,

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Random-Walk Perturbations for Online Combinatorial Optimization.

[BibT_eX]

[DOI]

,

,

IEEE Trans. Inf. Theory, 2015

Explore no more: Improved high-probability regret bounds for non-stochastic bandits.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

First-order regret bounds for combinatorial semi-bandits.

[BibT_eX]

[DOI]

Proceedings of The 28th Conference on Learning Theory, 2015

2014

Near-Optimal Rates for Limited-Delay Universal Lossy Source Coding.

[BibT_eX]

[DOI]

András György

,

IEEE Trans. Inf. Theory, 2014

Online Markov Decision Processes Under Bandit Feedback.

[BibT_eX]

[DOI]

,

András György

,

Csaba Szepesvári

,

IEEE Trans. Autom. Control., 2014

Online learning in MDPs with side information.

[BibT_eX]

[DOI]

Yasin Abbasi-Yadkori

,

CoRR, 2014

Exploiting easy data in online optimization.

[BibT_eX]

[DOI]

,

,

Alessandro Lazaric

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Online combinatorial optimization with stochastic decision sets and adversarial losses.

[BibT_eX]

[DOI]

,

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Efficient learning by implicit exploration in bandit problems with side observations.

[BibT_eX]

[DOI]

,

,

,

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013

Online tanulás nemstacionárius Markov döntési folyamatokban

[BibT_eX]

[DOI]

PhD thesis, 2013

Online learning in episodic Markovian decision processes by relative entropy policy search.

[BibT_eX]

[DOI]

Alexander Zimin

,

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Prediction by random-walk perturbation.

[BibT_eX]

[DOI]

,

,

Proceedings of the COLT 2013, 2013

An Efficient Algorithm for Learning with Semi-bandit Feedback.

[BibT_eX]

[DOI]

,

Proceedings of the Algorithmic Learning Theory - 24th International Conference, 2013

2012

The adversarial stochastic shortest path problem with unknown transition probabilities.

[BibT_eX]

[DOI]

,

András György

,

Csaba Szepesvári

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

2010

The Online Loop-free Stochastic Shortest-Path Problem.

[BibT_eX]

[DOI]

,

András György

,

Csaba Szepesvári

Proceedings of the COLT 2010, 2010

2009

Training parsers by inverse reinforcement learning.

[BibT_eX]

[DOI]

,

Csaba Szepesvári

Mach. Learn., 2009

2007

Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods.

[BibT_eX]

[DOI]

,

Csaba Szepesvári

Proceedings of the UAI 2007, 2007

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