György, András

70 publications

NeurIPS 2025 DataRater: Meta-Learned Dataset Curation Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeff Dean, Hado van Hasselt, David Silver

ICLR 2025 Learning Continually by Spectral Regularization Alex Lewandowski, Michał Bortkiewicz, Saurabh Kumar, András György, Dale Schuurmans, Mateusz Ostaszewski, Marlos C. Machado

AISTATS 2025 Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits Nicolas Nguyen, Imad Aouali, András György, Claire Vernade

ICLR 2025 Toward Understanding In-Context vs. In-Weight Learning Bryan Chan, Xinyi Chen, András György, Dale Schuurmans

NeurIPS 2024 Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani

NeurIPS 2024 To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári

JMLR 2023 A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits Yasin Abbasi-Yadkori, András György, Nevena Lazić

COLT 2023 A Second-Order Method for Stochastic Bandit Convex Optimisation Tor Lattimore, András György

ICML 2023 Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost Sanae Amani, Tor Lattimore, András György, Lin Yang

NeurIPS 2023 Online RL in Linearly $q^\pi$-Realizable MDPs Is as Easy as in Linear MDPs if You Learn What to Ignore Gellert Weisz, András György, Csaba Szepesvari

NeurIPS 2023 Optimistic Meta-Gradients Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado P van Hasselt, András György, Satinder P. Singh

NeurIPS 2023 Optimistic Natural Policy Gradient: A Simple Efficient Policy Optimization Framework for Online RL Qinghua Liu, Gellert Weisz, András György, Chi Jin, Csaba Szepesvari

NeurIPSW 2023 Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models Amal Rannen-Triki, Jorg Bornschein, Razvan Pascanu, Alexandre Galashov, Michalis Titsias, Marcus Hutter, András György, Yee Whye Teh

ICML 2023 Understanding Self-Predictive Learning for Reinforcement Learning Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Avila Pires, Yash Chandak, Remi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko

AISTATS 2022 Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning Anant Raj, Pooria Joulani, Andras Gyorgy, Csaba Szepesvari

NeurIPS 2022 Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-Realizable MDPs Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvari

ICLR 2022 Defending Against Image Corruptions Through Adversarial Augmentations Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, András György, Timothy A Mann, Sven Gowal

ICMLW 2022 Improved Generalization Bounds for Transfer Learning via Neural Collapse Tomer Galanti, András György, Marcus Hutter

JMLR 2022 Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling Gábor Melis, András György, Phil Blunsom

ICLR 2022 On the Role of Neural Collapse in Transfer Learning Tomer Galanti, András György, Marcus Hutter

NeurIPSW 2022 Optimistic Meta-Gradients Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh

ALT 2022 TensorPlan and the Few Actions Lower Bound for Planning in MDPs Under Linear Realizability of Optimal Value Functions Gellért Weisz, Csaba Szepesvári, András György

AISTATS 2021 Confident Off-Policy Evaluation and Selection Through Self-Normalized Importance Weighting Ilja Kuzborskij, Claire Vernade, Andras Gyorgy, Csaba Szepesvari

ICML 2021 Adapting to Delays and Data in Adversarial Multi-Armed Bandits Andras Gyorgy, Pooria Joulani

COLT 2021 Improved Regret for Zeroth-Order Stochastic Convex Bandits Tor Lattimore, Andras Gyorgy

COLT 2021 Mirror Descent and the Information Ratio Tor Lattimore, Andras Gyorgy

NeurIPSW 2021 Towards Better Visual Explanations for Deep Image Classifiers Agnieszka Grabska-Barwinska, Amal Rannen-Triki, Omar Rivasplata, András György

ICLR 2020 A Framework for Robustness Certification of Smoothed Classifiers Using F-Divergences Krishnamurthy Dvijotham, Jamie Hayes, Borja Balle, Zico Kolter, Chongli Qin, Andras Gyorgy, Kai Xiao, Sven Gowal, Pushmeet Kohli

ICML 2020 A Simpler Approach to Accelerated Optimization: Iterative Averaging Meets Optimism Pooria Joulani, Anant Raj, Andras Gyorgy, Csaba Szepesvari

NeurIPS 2020 ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool Gellert Weisz, András György, Wei-I Lin, Devon Graham, Kevin Leyton-Brown, Csaba Szepesvari, Brendan Lucier

ICML 2020 Non-Stationary Delayed Bandits with Intermediate Observations Claire Vernade, Andras Gyorgy, Timothy Mann

AISTATS 2019 Adaptive MCMC via Combining Local Samplers Kiárash Shaloudegi, András György

ICML 2019 CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration Gellert Weisz, Andras Gyorgy, Csaba Szepesvari

NeurIPS 2019 Detecting Overfitting via Adversarial Examples Roman Werpachowski, András György, Csaba Szepesvari

ICML 2019 Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems Timothy Arthur Mann, Sven Gowal, Andras Gyorgy, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan

NeurIPS 2019 Think Out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging Pooria Joulani, András György, Csaba Szepesvari

ICML 2018 LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration Gellert Weisz, Andras Gyorgy, Csaba Szepesvari

ALT 2017 A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds Pooria Joulani, András György, Csaba Szepesvári

JMLR 2017 Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári

AISTATS 2016 (Bandit) Convex Optimization with Biased Noisy Gradient Oracles Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári

AAAI 2016 Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms Pooria Joulani, András György, Csaba Szepesvári

NeurIPS 2016 Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvari

NeurIPS 2016 SDP Relaxation with Randomized Rounding for Energy Disaggregation Kiarash Shaloudegi, András György, Csaba Szepesvari, Wilsun Xu

ICML 2016 Shifting Regret, Mirror Descent, and Matrices Andras Gyorgy, Csaba Szepesvari

ICML 2015 Deterministic Independent Component Analysis Ruitong Huang, Andras Gyorgy, Csaba Szepesvári

AISTATS 2015 Exploiting Symmetries to Construct Efficient MCMC Algorithms with an Application to SLAM Roshan Shariff, András György, Csaba Szepesvári

IJCAI 2015 Fast Cross-Validation for Incremental Learning Pooria Joulani, András György, Csaba Szepesvári

AISTATS 2015 Near-Optimal Max-Affine Estimators for Convex Regression Gábor Balázs, András György, Csaba Szepesvári

ICML 2015 On Identifying Good Options Under Combinatorially Structured Feedback in Finite Noisy Environments Yifan Wu, Andras Gyorgy, Csaba Szepesvari

NeurIPS 2015 Online Learning with Gaussian Payoffs and Side Observations Yifan Wu, András György, Csaba Szepesvari

ECML-PKDD 2015 Scalable Metric Learning for Co-Embedding Farzaneh Mirzazadeh, Martha White, András György, Dale Schuurmans

ICML 2014 Adaptive Monte Carlo via Bandit Allocation James Neufeld, Andras Gyorgy, Csaba Szepesvari, Dale Schuurmans

ALT 2014 On Learning the Optimal Waiting Time Tor Lattimore, András György, Csaba Szepesvári

ICML 2014 Online Learning in Markov Decision Processes with Changing Cost Sequences Travis Dick, Andras Gyorgy, Csaba Szepesvari

ICML 2013 A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning Arash Afkanpour, András György, Csaba Szepesvari, Michael Bowling

MLJ 2013 BoostingTree: Parallel Selection of Weak Learners in Boosting, with Application to Ranking Levente Kocsis, András György, Andrea N. Bán

ICML 2013 Online Learning Under Delayed Feedback Pooria Joulani, Andras Gyorgy, Csaba Szepesvari

NeurIPS 2013 Online Learning with Costly Features and Labels Navid Zolghadr, Gabor Bartok, Russell Greiner, András György, Csaba Szepesvari

AISTATS 2012 The Adversarial Stochastic Shortest Path Problem with Unknown Transition Probabilities Gergely Neu, Andras Gyorgy, Csaba Szepesvari

JAIR 2011 Efficient Multi-Start Strategies for Local Search Algorithms András György, Levente Kocsis

AISTATS 2010 A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping Peter Torma, András György, Csaba Szepesvári

JMLR 2010 On-Line Sequential Bin Packing András György, Gábor Lugosi, György Ottucsàk

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

ECML-PKDD 2009 Efficient Multi-Start Strategies for Local Search Algorithms Levente Kocsis, András György

COLT 2008 On-Line Sequential Bin Packing András György, Gábor Lugosi, György Ottucsák

IJCAI 2007 Continuous Time Associative Bandit Problems András György, Levente Kocsis, Ivett Szabó, Csaba Szepesvári

JMLR 2007 The On-Line Shortest Path Problem Under Partial Monitoring András György, Tamás Linder, Gábor Lugosi, György Ottucsák

COLT 2006 The Shortest Path Problem Under Partial Monitoring András György, Tamás Linder, György Ottucsák

COLT 2005 Tracking the Best of Many Experts András György, Tamás Linder, Gábor Lugosi