András György

ORCID

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 98

Top Venues

Publications

Yasin Abbasi-Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári
To Believe or Not to Believe Your LLM. CoRR (2024)
Nicolas Nguyen, Imad Aouali, András György, Claire Vernade
Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits. CoRR (2024)
Alex Lewandowski, Saurabh Kumar, Dale Schuurmans, András György, Marlos C. Machado
Learning Continually by Spectral Regularization. CoRR (2024)
Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models. CoRR (2024)
Yasin Abbasi-Yadkori, Ilja Kuzborskij, David Stutz, András György, Adam Fisch, Arnaud Doucet, Iuliya Beloshapka, Wei-Hung Weng, Yao-Yuan Yang, Csaba Szepesvári, Ali Taylan Cemgil, Nenad Tomasev
Mitigating LLM Hallucinations via Conformal Abstention. CoRR (2024)
Tor Lattimore, András György
A Second-Order Method for Stochastic Bandit Convex Optimisation. COLT (2023)
Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. CoRR (2023)
Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado Philip van Hasselt, András György, Satinder Singh
Optimistic Meta-Gradients. NeurIPS (2023)
Gellért Weisz, András György, Csaba Szepesvári
-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore. CoRR (2023)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. ICML (2023)
Sanae Amani, Tor Lattimore, András György, Lin Yang
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. ICML (2023)
Tor Lattimore, András György
A Second-Order Method for Stochastic Bandit Convex Optimisation. CoRR (2023)
Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh
Optimistic Meta-Gradients. CoRR (2023)
Gellért Weisz, András György, Csaba Szepesvári
-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore. NeurIPS (2023)
Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. NeurIPS (2023)
Sanae Amani, Tor Lattimore, András György, Lin F. Yang
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. CoRR (2022)
Gellért Weisz, Csaba Szepesvári, András György
TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions. ALT (2022)
Anant Raj, Pooria Joulani, András György, Csaba Szepesvári
Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning. AISTATS (2022)
Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári
-realizable MDPs. CoRR (2022)
Tomer Galanti, András György, Marcus Hutter
On the Role of Neural Collapse in Transfer Learning. ICLR (2022)
Gábor Melis, András György, Phil Blunsom
Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling. J. Mach. Learn. Res. 23 (2022)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR (2022)
Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, András György, Timothy A. Mann, Sven Gowal
Defending Against Image Corruptions Through Adversarial Augmentations. ICLR (2022)
Tomer Galanti, András György, Marcus Hutter
Generalization Bounds for Transfer Learning with Pretrained Classifiers. CoRR (2022)
Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs. NeurIPS (2022)
Mohammad Javad Azizi, Thang Duong, Yasin Abbasi-Yadkori, András György, Claire Vernade, Mohammad Ghavamzadeh
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms. CoRR (2022)
Yasin Abbasi-Yadkori, András György, Nevena Lazic
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits. CoRR (2022)
András György, Pooria Joulani
Adapting to Delays and Data in Adversarial Multi-Armed Bandits. ICML (2021)
Tor Lattimore, András György
Mirror Descent and the Information Ratio. COLT (2021)
Muhammad Zaid Hameed, András György
Perceptually Constrained Adversarial Attacks. CoRR (2021)
Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. AISTATS (2021)
Elif Tugce Ceran, Deniz Gündüz, András György
Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting. CoRR (2021)
Davide G. Cavezza, Dalal Alrajeh, András György
A Weakness Measure for GR(1) Formulae. Formal Aspects Comput. 33 (1) (2021)
Muhammad Zaid Hameed, András György, Deniz Gündüz
The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection. IEEE Trans. Inf. Forensics Secur. 16 (2021)
Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, András György, Csaba Szepesvári, Raia Hadsell, Nicolas Heess, Martin A. Riedmiller
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning. CoRR (2021)
Elif Tugçe Ceran, Deniz Gündüz, András György
A Reinforcement Learning Approach to Age of Information in Multi-User Networks With HARQ. IEEE J. Sel. Areas Commun. 39 (5) (2021)
Tomer Galanti, András György, Marcus Hutter
On the Role of Neural Collapse in Transfer Learning. CoRR (2021)
Dan A. Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, András György, Timothy A. Mann, Sven Gowal
Defending Against Image Corruptions Through Adversarial Augmentations. CoRR (2021)
Tor Lattimore, András György
Improved Regret for Zeroth-Order Stochastic Convex Bandits. COLT (2021)
Elif Tugce Ceran, Deniz Gündüz, András György
A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ. CoRR (2021)
Gellért Weisz, Csaba Szepesvári, András György
TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions. CoRR (2021)
Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. CoRR (2020)
Gábor Melis, András György, Phil Blunsom
Mutual Information Constraints for Monte-Carlo Objectives. CoRR (2020)
Gellért Weisz, András György, Wei-I Lin, Devon R. Graham, Kevin Leyton-Brown, Csaba Szepesvári, Brendan Lucier
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool. NeurIPS (2020)
András György, Pooria Joulani
Adapting to Delays and Data in Adversarial Multi-Armed Bandits. CoRR (2020)
Davide G. Cavezza, Dalal Alrajeh, András György
Minimal Assumptions Refinement for Realizable Specifications. FormaliSE@ICSE (2020)
Tor Lattimore, András György
Mirror Descent and the Information Ratio. CoRR (2020)
Claire Vernade, András György, Timothy A. Mann
Non-Stationary Delayed Bandits with Intermediate Observations. ICML (2020)
Pooria Joulani, András György, Csaba Szepesvári
A modular analysis of adaptive (non-)convex optimization: Optimism, composite objectives, variance reduction, and variational bounds. Theor. Comput. Sci. 808 (2020)
Pooria Joulani, Anant Raj, András György, Csaba Szepesvári
A simpler approach to accelerated optimization: iterative averaging meets optimism. ICML (2020)
Krishnamurthy (Dj) Dvijotham, Jamie Hayes, Borja Balle, J. Zico Kolter, Chongli Qin, András György, Kai Xiao, Sven Gowal, Pushmeet Kohli
A Framework for robustness Certification of Smoothed Classifiers using F-Divergences. ICLR (2020)
Claire Vernade, András György, Timothy A. Mann
Non-Stationary Bandits with Intermediate Observations. CoRR (2020)
Roman Werpachowski, András György, Csaba Szepesvári
Detecting Overfitting via Adversarial Examples. CoRR (2019)
Gellért Weisz, András György, Csaba Szepesvári
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration. ICML (2019)
Timothy A. Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan
Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems. ICML (2019)
Davide G. Cavezza, Dalal Alrajeh, András György
Minimal Assumptions Refinement for GR(1) Specifications. CoRR (2019)
Samuel O. Somuyiwa, András György, Deniz Gündüz
Multicast-Aware Proactive Caching in Wireless Networks with Deep Reinforcement Learning. SPAWC (2019)
Pooria Joulani, András György, Csaba Szepesvári
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging. NeurIPS (2019)
Elif Tugçe Ceran, Deniz Gündüz, András György
Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost. CoRR (2019)
Elif Tugce Ceran, Deniz Gündüz, András György
Average Age of Information With Hybrid ARQ Under a Resource Constraint. IEEE Trans. Wirel. Commun. 18 (3) (2019)
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli
Degenerate Feedback Loops in Recommender Systems. AIES (2019)
Kiárash Shaloudegi, András György
Adaptive MCMC via Combining Local Samplers. AISTATS (2019)
Muhammad Zaid Hameed, András György, Deniz Gündüz
Communication without Interception: Defense against Deep-Learning-based Modulation Detection. CoRR (2019)
Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli
Degenerate Feedback Loops in Recommender Systems. CoRR (2019)
Elif Tugçe Ceran, Deniz Gündüz, András György
Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost. INFOCOM Workshops (2019)
Muhammad Zaid Hameed, András György, Deniz Gündüz
Communication without Interception: Defense against Modulation Detection. GlobalSIP (2019)
Pedro A. Ortega, Jane X. Wang, Mark Rowland, Tim Genewein, Zeb Kurth-Nelson, Razvan Pascanu, Nicolas Heess, Joel Veness, Alexander Pritzel, Pablo Sprechmann, Siddhant M. Jayakumar, Tom McGrath, Kevin J. Miller, Mohammad Gheshlaghi Azar, Ian Osband, Neil C. Rabinowitz, András György, Silvia Chiappa, Simon Osindero, Yee Whye Teh, Hado van Hasselt, Nando de Freitas, Matthew Botvinick, Shane Legg
Meta-learning of Sequential Strategies. CoRR (2019)
Roman Werpachowski, András György, Csaba Szepesvári
Detecting Overfitting via Adversarial Examples. NeurIPS (2019)
Gellért Weisz, András György, Csaba Szepesvári
LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration. ICML (2018)
Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, András György
Learning from Delayed Outcomes with Intermediate Observations. CoRR (2018)
Gellért Weisz, András György, Csaba Szepesvári
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration. CoRR (2018)
Samuel O. Somuyiwa, András György, Deniz Gündüz
A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks. IEEE J. Sel. Areas Commun. 36 (6) (2018)
Elif Tugce Ceran, Deniz Gündüz, András György
A Reinforcement Learning Approach to Age of Information in Multi-User Networks. PIMRC (2018)
Andrea Paudice, Luis Muñoz-González, András György, Emil C. Lupu
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection. CoRR (2018)
Davide G. Cavezza, Dalal Alrajeh, András György
A Weakness Measure for GR(1) Formulae. CoRR (2018)
Davide Giacomo Cavezza, Dalal Alrajeh, András György
A Weakness Measure for GR(1) Formulae. FM (2018)
Samuel O. Somuyiwa, Deniz Gündüz, András György
Reinforcement Learning for Proactive Caching of Contents with Different Demand Probabilities. ISWCS (2018)
Elif Tugce Ceran, Deniz Gündüz, András György
Average age of information with hybrid ARQ under a resource constraint. WCNC (2018)
Kiarash Shaloudegi, András György
Adaptive MCMC via Combining Local Samplers. CoRR (2018)
Elif Tugçe Ceran, Deniz Gündüz, András György
A Reinforcement Learning Approach to Age of Information in Multi-User Networks. CoRR (2018)
Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities. J. Mach. Learn. Res. 18 (2017)
Samuel O. Somuyiwa, András György, Deniz Gündüz
Energy-efficient wireless content delivery with proactive caching. WiOpt (2017)
Pooria Joulani, András György, Csaba Szepesvári
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds. CoRR (2017)
Elif Tugce Ceran, Deniz Gündüz, András György
Average Age of Information with Hybrid ARQ under a Resource Constraint. CoRR (2017)
Pooria Joulani, András György, Csaba Szepesvári
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds. ALT (2017)
Samuel O. Somuyiwa, András György, Deniz Gündüz
Energy-Efficient Wireless Content Delivery with Proactive Caching. CoRR (2017)
Samuel O. Somuyiwa, András György, Deniz Gündüz
Improved policy representation and policy search for proactive content caching in wireless networks. WiOpt (2017)
Samuel O. Somuyiwa, András György, Deniz Gündüz
A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks. CoRR (2017)
Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. CoRR (2017)
Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. NIPS (2016)
Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. AISTATS (2016)
Gábor Balázs, András György, Csaba Szepesvári
Max-affine estimators for convex stochastic programming. CoRR (2016)
Pooria Joulani, András György, Csaba Szepesvári
Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms. AAAI (2016)
Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. CoRR (2016)
Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu
SDP Relaxation with Randomized Rounding for Energy Disaggregation. CoRR (2016)
András György, Csaba Szepesvári
Shifting Regret, Mirror Descent, and Matrices. ICML (2016)
Gábor Balázs, András György, Csaba Szepesvári
Chaining Bounds for Empirical Risk Minimization. CoRR (2016)
Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu
SDP Relaxation with Randomized Rounding for Energy Disaggregation. NIPS (2016)
Gábor Balázs, András György, Csaba Szepesvári
Near-optimal max-affine estimators for convex regression. AISTATS (2015)
Pooria Joulani, András György, Csaba Szepesvári
Fast Cross-Validation for Incremental Learning. CoRR (2015)

András György.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

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András György