András György

176 papers A* 38A 10B 17C 1Journal 94Unranked 16

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	TBDFiltering: Sample-Efficient Tree-Based Data Filtering. CoRR Róbert Istvan Busa-Fekete, Julian Zimmert, Anne Xiangyi Zheng, Claudio Gentile, András György
2025	A*	`conf`	A Scalable Crawling Algorithm Utilizing Noisy Change-Indicating Signals. WWW Julian Zimmert, Róbert Busa-Fekete, András György, Linhai Qiu, Hyomin Choi, Tzu-Wei Sung, Hao Shen, Sharmila Subramaniam, Li Xiao
2025	J	`jnl`	A Scalable Crawling Algorithm Utilizing Noisy Change-Indicating Signals. CoRR Róbert Busa-Fekete, Julian Zimmert, András György, Linhai Qiu, Tzu-Wei Sung, Hao Shen, Hyomin Choi, Sharmila Subramaniam, Li Xiao
2025	J	`jnl`	Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence. CoRR András György, Tor Lattimore, Nevena Lazic, Csaba Szepesvári
2025	J	`jnl`	DataRater: Meta-Learned Dataset Curation. CoRR Dan A. Calian, Gregory Farquhar, Iurii Kemaev, Luisa M. Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeffrey Dean, Hado van Hasselt, David Silver
2025	J	`jnl`	Frontier LLMs Still Struggle with Simple Reasoning Tasks. CoRR Alan Malek, Jiawei Ge, Nevena Lazic, Chi Jin, András György, Csaba Szepesvári
2025	A*	`conf`	Learning Continually by Spectral Regularization. ICLR Alex Lewandowski, Michal Bortkiewicz, Saurabh Kumar, András György, Dale Schuurmans, Mateusz Ostaszewski, Marlos C. Machado
2025	J	`jnl`	Partition Tree Weighting for Non-Stationary Stochastic Bandits. CoRR Joel Veness, Marcus Hutter, András György, Jordi Grau-Moya
2025	A	`conf`	Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits. AISTATS Nicolas Nguyen, Imad Aouali, András György, Claire Vernade
2025	A*	`conf`	Toward Understanding In-context vs. In-weight Learning. ICLR Bryan Chan, Xinyi Chen, András György, Dale Schuurmans
2025	J	`jnl`	What Can Grokking Teach Us About Learning Under Nonstationarity? CoRR Clare Lyle, Ghada Sokar, Razvan Pascanu, András György
2024	A	`conf`	Approximating the Core via Iterative Coalition Sampling. AAMAS Ian Gemp, Marc Lanctot, Luke Marris, Yiran Mao, Edgar A. Duéñez-Guzmán, Sarah Perrin, András György, Romuald Elie, Georgios Piliouras, Michael Kaisers, Daniel Hennes, Kalesha Bullard, Kate Larson, Yoram Bachrach
2024	J	`jnl`	Approximating the Core via Iterative Coalition Sampling. CoRR Ian Gemp, Marc Lanctot, Luke Marris, Yiran Mao, Edgar A. Duéñez-Guzmán, Sarah Perrin, András György, Romuald Elie, Georgios Piliouras, Michael Kaisers, Daniel Hennes, Kalesha Bullard, Kate Larson, Yoram Bachrach
2024	J	`jnl`	Learning Continually by Spectral Regularization. CoRR Alex Lewandowski, Saurabh Kumar, Dale Schuurmans, András György, Marlos C. Machado
2024	J	`jnl`	Mitigating LLM Hallucinations via Conformal Abstention. CoRR 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
2024	A*	`conf`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. NeurIPS Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	J	`jnl`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. CoRR Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	J	`jnl`	Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms. RLJ Mohammad Javad Azizi, Thang Duong, Yasin Abbasi-Yadkori, András György, Claire Vernade, Mohammad Ghavamzadeh
2024	J	`jnl`	Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits. CoRR Nicolas Nguyen, Imad Aouali, András György, Claire Vernade
2024	J	`jnl`	Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models. CoRR Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias
2024	J	`jnl`	To Believe or Not to Believe Your LLM. CoRR Yasin Abbasi-Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári
2024	A*	`conf`	To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty. NeurIPS Yasin Abbasi-Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári
2024	J	`jnl`	Toward Understanding In-context vs. In-weight Learning. CoRR Bryan Chan, Xinyi Chen, András György, Dale Schuurmans
2023	J	`jnl`	A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits. J. Mach. Learn. Res. Yasin Abbasi-Yadkori, András György, Nevena Lazic
2023	A*	`conf`	A Second-Order Method for Stochastic Bandit Convex Optimisation. COLT Tor Lattimore, András György
2023	J	`jnl`	A Second-Order Method for Stochastic Bandit Convex Optimisation. CoRR Tor Lattimore, András György
2023	A*	`conf`	Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. ICML Sanae Amani, Tor Lattimore, András György, Lin Yang
2023	A*	`conf`	Online RL in Linearly q NeurIPS Gellért Weisz, András György, Csaba Szepesvári
2023	J	`jnl`	Online RL in Linearly q CoRR Gellért Weisz, András György, Csaba Szepesvári
2023	A*	`conf`	Optimistic Meta-Gradients. NeurIPS Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado Philip van Hasselt, András György, Satinder Singh
2023	J	`jnl`	Optimistic Meta-Gradients. CoRR Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh
2023	A*	`conf`	Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. NeurIPS Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári
2023	J	`jnl`	Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. CoRR Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári
2023	A*	`conf`	Understanding Self-Predictive Learning for Reinforcement Learning. ICML 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
2022	J	`jnl`	A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits. CoRR Yasin Abbasi-Yadkori, András György, Nevena Lazic
2022	A*	`conf`	Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs. NeurIPS Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári
2022	J	`jnl`	Confident Approximate Policy Iteration for Efficient Local Planning in q CoRR Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári
2022	A*	`conf`	Defending Against Image Corruptions Through Adversarial Augmentations. ICLR Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, András György, Timothy A. Mann, Sven Gowal
2022	J	`jnl`	Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. CoRR Sanae Amani, Tor Lattimore, András György, Lin F. Yang
2022	A	`conf`	Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning. AISTATS Anant Raj, Pooria Joulani, András György, Csaba Szepesvári
2022	J	`jnl`	Generalization Bounds for Transfer Learning with Pretrained Classifiers. CoRR Tomer Galanti, András György, Marcus Hutter
2022	J	`jnl`	Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling. J. Mach. Learn. Res. Gábor Melis, András György, Phil Blunsom
2022	J	`jnl`	Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms. CoRR Mohammad Javad Azizi, Thang Duong, Yasin Abbasi-Yadkori, András György, Claire Vernade, Mohammad Ghavamzadeh
2022	A*	`conf`	On the Role of Neural Collapse in Transfer Learning. ICLR Tomer Galanti, András György, Marcus Hutter
2022	B	`conf`	TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions. ALT Gellért Weisz, Csaba Szepesvári, András György
2022	J	`jnl`	Understanding Self-Predictive Learning for Reinforcement Learning. CoRR 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
2021	J	`jnl`	A Reinforcement Learning Approach to Age of Information in Multi-User Networks With HARQ. IEEE J. Sel. Areas Commun. Elif Tugçe Ceran, Deniz Gündüz, András György
2021	J	`jnl`	A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ. CoRR Elif Tugce Ceran, Deniz Gündüz, András György
2021	J	`jnl`	A Weakness Measure for GR(1) Formulae. Formal Aspects Comput. Davide G. Cavezza, Dalal Alrajeh, András György
2021	A*	`conf`	Adapting to Delays and Data in Adversarial Multi-Armed Bandits. ICML András György, Pooria Joulani
2021	A	`conf`	Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. AISTATS Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári
2021	J	`jnl`	Defending Against Image Corruptions Through Adversarial Augmentations. CoRR Dan A. Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, András György, Timothy A. Mann, Sven Gowal
2021	A*	`conf`	Improved Regret for Zeroth-Order Stochastic Convex Bandits. COLT Tor Lattimore, András György
2021	J	`jnl`	Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting. CoRR Elif Tugce Ceran, Deniz Gündüz, András György
2021	A*	`conf`	Mirror Descent and the Information Ratio. COLT Tor Lattimore, András György
2021	J	`jnl`	On Multi-objective Policy Optimization as a Tool for Reinforcement Learning. CoRR 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
2021	J	`jnl`	On the Role of Neural Collapse in Transfer Learning. CoRR Tomer Galanti, András György, Marcus Hutter
2021	J	`jnl`	Perceptually Constrained Adversarial Attacks. CoRR Muhammad Zaid Hameed, András György
2021	J	`jnl`	TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions. CoRR Gellért Weisz, Csaba Szepesvári, András György
2021	J	`jnl`	The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection. IEEE Trans. Inf. Forensics Secur. Muhammad Zaid Hameed, András György, Deniz Gündüz
2020	A*	`conf`	A Framework for robustness Certification of Smoothed Classifiers using F-Divergences. ICLR Krishnamurthy (Dj) Dvijotham, Jamie Hayes, Borja Balle, J. Zico Kolter, Chongli Qin, András György, Kai Xiao, Sven Gowal, Pushmeet Kohli
2020	J	`jnl`	A modular analysis of adaptive (non-)convex optimization: Optimism, composite objectives, variance reduction, and variational bounds. Theor. Comput. Sci. Pooria Joulani, András György, Csaba Szepesvári
2020	A*	`conf`	A simpler approach to accelerated optimization: iterative averaging meets optimism. ICML Pooria Joulani, Anant Raj, András György, Csaba Szepesvári
2020	J	`jnl`	Adapting to Delays and Data in Adversarial Multi-Armed Bandits. CoRR András György, Pooria Joulani
2020	J	`jnl`	Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. CoRR Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári
2020	A*	`conf`	ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool. NeurIPS Gellért Weisz, András György, Wei-I Lin, Devon R. Graham, Kevin Leyton-Brown, Csaba Szepesvári, Brendan Lucier
2020	—	`conf`	Minimal Assumptions Refinement for Realizable Specifications. FormaliSE@ICSE Davide G. Cavezza, Dalal Alrajeh, András György
2020	J	`jnl`	Mirror Descent and the Information Ratio. CoRR Tor Lattimore, András György
2020	J	`jnl`	Mutual Information Constraints for Monte-Carlo Objectives. CoRR Gábor Melis, András György, Phil Blunsom
2020	J	`jnl`	Non-Stationary Bandits with Intermediate Observations. CoRR Claire Vernade, András György, Timothy A. Mann
2020	A*	`conf`	Non-Stationary Delayed Bandits with Intermediate Observations. ICML Claire Vernade, András György, Timothy A. Mann
2019	A	`conf`	Adaptive MCMC via Combining Local Samplers. AISTATS Kiárash Shaloudegi, András György
2019	J	`jnl`	Average Age of Information With Hybrid ARQ Under a Resource Constraint. IEEE Trans. Wirel. Commun. Elif Tugce Ceran, Deniz Gündüz, András György
2019	A*	`conf`	CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration. ICML Gellért Weisz, András György, Csaba Szepesvári
2019	J	`jnl`	Communication without Interception: Defense against Deep-Learning-based Modulation Detection. CoRR Muhammad Zaid Hameed, András György, Deniz Gündüz
2019	—	`conf`	Communication without Interception: Defense against Modulation Detection. GlobalSIP Muhammad Zaid Hameed, András György, Deniz Gündüz
2019	C	`conf`	Degenerate Feedback Loops in Recommender Systems. AIES Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli
2019	J	`jnl`	Degenerate Feedback Loops in Recommender Systems. CoRR Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli
2019	A*	`conf`	Detecting Overfitting via Adversarial Examples. NeurIPS Roman Werpachowski, András György, Csaba Szepesvári
2019	J	`jnl`	Detecting Overfitting via Adversarial Examples. CoRR Roman Werpachowski, András György, Csaba Szepesvári
2019	A*	`conf`	Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems. ICML Timothy A. Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang, Balaji Lakshminarayanan, Prav Srinivasan
2019	J	`jnl`	Meta-learning of Sequential Strategies. CoRR 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 M. Botvinick, Shane Legg
2019	J	`jnl`	Minimal Assumptions Refinement for GR(1) Specifications. CoRR Davide G. Cavezza, Dalal Alrajeh, András György
2019	—	`conf`	Multicast-Aware Proactive Caching in Wireless Networks with Deep Reinforcement Learning. SPAWC Samuel O. Somuyiwa, András György, Deniz Gündüz
2019	—	`conf`	Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost. INFOCOM Workshops Elif Tugçe Ceran, Deniz Gündüz, András György
2019	J	`jnl`	Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost. CoRR Elif Tugçe Ceran, Deniz Gündüz, András György
2019	A*	`conf`	Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging. NeurIPS Pooria Joulani, András György, Csaba Szepesvári
2018	B	`conf`	A Reinforcement Learning Approach to Age of Information in Multi-User Networks. PIMRC Elif Tugce Ceran, Deniz Gündüz, András György
2018	J	`jnl`	A Reinforcement Learning Approach to Age of Information in Multi-User Networks. CoRR Elif Tugçe Ceran, Deniz Gündüz, András György
2018	J	`jnl`	A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks. IEEE J. Sel. Areas Commun. Samuel O. Somuyiwa, András György, Deniz Gündüz
2018	B	`conf`	A Weakness Measure for GR(1) Formulae. FM Davide Giacomo Cavezza, Dalal Alrajeh, András György
2018	J	`jnl`	A Weakness Measure for GR(1) Formulae. CoRR Davide G. Cavezza, Dalal Alrajeh, András György
2018	J	`jnl`	Adaptive MCMC via Combining Local Samplers. CoRR Kiarash Shaloudegi, András György
2018	B	`conf`	Average age of information with hybrid ARQ under a resource constraint. WCNC Elif Tugce Ceran, Deniz Gündüz, András György
2018	J	`jnl`	Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection. CoRR Andrea Paudice, Luis Muñoz-González, András György, Emil C. Lupu
2018	A*	`conf`	LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration. ICML Gellért Weisz, András György, Csaba Szepesvári
2018	J	`jnl`	LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration. CoRR Gellért Weisz, András György, Csaba Szepesvári
2018	J	`jnl`	Learning from Delayed Outcomes with Intermediate Observations. CoRR Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, András György
2018	—	`conf`	Reinforcement Learning for Proactive Caching of Contents with Different Demand Probabilities. ISWCS Samuel O. Somuyiwa, Deniz Gündüz, András György
2017	B	`conf`	A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds. ALT Pooria Joulani, András György, Csaba Szepesvári
2017	J	`jnl`	A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds. CoRR Pooria Joulani, András György, Csaba Szepesvári
2017	J	`jnl`	A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks. CoRR Samuel O. Somuyiwa, András György, Deniz Gündüz
2017	J	`jnl`	Average Age of Information with Hybrid ARQ under a Resource Constraint. CoRR Elif Tugce Ceran, Deniz Gündüz, András György
2017	J	`jnl`	Energy-Efficient Wireless Content Delivery with Proactive Caching. CoRR Samuel O. Somuyiwa, András György, Deniz Gündüz
2017	B	`conf`	Energy-efficient wireless content delivery with proactive caching. WiOpt Samuel O. Somuyiwa, András György, Deniz Gündüz
2017	J	`jnl`	Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. CoRR Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
2017	J	`jnl`	Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities. J. Mach. Learn. Res. Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
2017	B	`conf`	Improved policy representation and policy search for proactive content caching in wireless networks. WiOpt Samuel O. Somuyiwa, András György, Deniz Gündüz
2016	A	`conf`	(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. AISTATS Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári
2016	J	`jnl`	(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. CoRR Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári
2016	J	`jnl`	Chaining Bounds for Empirical Risk Minimization. CoRR Gábor Balázs, András György, Csaba Szepesvári
2016	A*	`conf`	Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms. AAAI Pooria Joulani, András György, Csaba Szepesvári
2016	—	`conf`	Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. NIPS Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári
2016	J	`jnl`	Max-affine estimators for convex stochastic programming. CoRR Gábor Balázs, András György, Csaba Szepesvári
2016	—	`conf`	SDP Relaxation with Randomized Rounding for Energy Disaggregation. NIPS Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu
2016	J	`jnl`	SDP Relaxation with Randomized Rounding for Energy Disaggregation. CoRR Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu
2016	A*	`conf`	Shifting Regret, Mirror Descent, and Matrices. ICML András György, Csaba Szepesvári
2015	A*	`conf`	Deterministic Independent Component Analysis. ICML Ruitong Huang, András György, Csaba Szepesvári
2015	A	`conf`	Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM. AISTATS Roshan Shariff, András György, Csaba Szepesvári
2015	A*	`conf`	Fast Cross-Validation for Incremental Learning. IJCAI Pooria Joulani, András György, Csaba Szepesvári
2015	J	`jnl`	Fast Cross-Validation for Incremental Learning. CoRR Pooria Joulani, András György, Csaba Szepesvári
2015	A	`conf`	Near-optimal max-affine estimators for convex regression. AISTATS Gábor Balázs, András György, Csaba Szepesvári
2015	A*	`conf`	On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments. ICML Yifan Wu, András György, Csaba Szepesvári
2015	—	`conf`	Online Learning with Gaussian Payoffs and Side Observations. NIPS Yifan Wu, András György, Csaba Szepesvári
2015	J	`jnl`	Online Learning with Gaussian Payoffs and Side Observations. CoRR Yifan Wu, András György, Csaba Szepesvári
2015	—	`conf`	Scalable Metric Learning for Co-Embedding. ECML/PKDD (1) Farzaneh Mirzazadeh, Martha White, András György, Dale Schuurmans
2014	A*	`conf`	Adaptive Monte Carlo via Bandit Allocation. ICML James Neufeld, András György, Csaba Szepesvári, Dale Schuurmans
2014	J	`jnl`	Adaptive Monte Carlo via Bandit Allocation. CoRR James Neufeld, András György, Dale Schuurmans, Csaba Szepesvári
2014	J	`jnl`	Efficient Methods for Early Protocol Identification. IEEE J. Sel. Areas Commun. Béla Hullár, Sándor Laki, András György
2014	J	`jnl`	Efficient Multi-Start Strategies for Local Search Algorithms. CoRR András György, Levente Kocsis
2014	J	`jnl`	Near-Optimal Rates for Limited-Delay Universal Lossy Source Coding. IEEE Trans. Inf. Theory András György, Gergely Neu
2014	B	`conf`	On Learning the Optimal Waiting Time. ALT Tor Lattimore, András György, Csaba Szepesvári
2014	A*	`conf`	Online Learning in Markov Decision Processes with Changing Cost Sequences. ICML Travis Dick, András György, Csaba Szepesvári
2014	J	`jnl`	Online Markov Decision Processes Under Bandit Feedback. IEEE Trans. Autom. Control. Gergely Neu, András György, Csaba Szepesvári, András Antos
2013	—	`conf`	A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning. ICML (1) Arash Afkanpour, András György, Csaba Szepesvári, Michael Bowling
2013	J	`jnl`	BoostingTree: parallel selection of weak learners in boosting, with application to ranking. Mach. Learn. Levente Kocsis, András György, Andrea N. Bán
2013	—	`conf`	Online Learning under Delayed Feedback. ICML (3) Pooria Joulani, András György, Csaba Szepesvári
2013	J	`jnl`	Online Learning under Delayed Feedback. CoRR Pooria Joulani, András György, Csaba Szepesvári
2013	—	`conf`	Online Learning with Costly Features and Labels. NIPS Navid Zolghadr, Gábor Bartók, Russell Greiner, András György, Csaba Szepesvári
2013	B	`conf`	Partition Tree Weighting. DCC Joel Veness, Martha White, Michael Bowling, András György
2012	J	`jnl`	A Randomized Strategy for Learning to Combine Many Features CoRR Arash Afkanpour, András György, Csaba Szepesvári, Michael H. Bowling
2012	J	`jnl`	Efficient Tracking of Large Classes of Experts. IEEE Trans. Inf. Theory András György, Tamás Linder, Gábor Lugosi
2012	B	`conf`	Efficient tracking of large classes of experts. ISIT András György, Tamás Linder, Gábor Lugosi
2012	J	`jnl`	Partition Tree Weighting CoRR Joel Veness, Martha White, Michael Bowling, András György
2012	A	`conf`	The adversarial stochastic shortest path problem with unknown transition probabilities. AISTATS Gergely Neu, András György, Csaba Szepesvári
2011	—	`conf`	Early Identification of Peer-to-Peer Traffic. ICC Béla Hullár, Sándor Laki, András György
2011	J	`jnl`	Efficient Multi-Start Strategies for Local Search Algorithms. J. Artif. Intell. Res. András György, Levente Kocsis
2011	J	`jnl`	Efficient Tracking of Large Classes of Experts CoRR András György, Tamás Linder, Gábor Lugosi
2011	B	`conf`	Near-optimal rates for limited-delay universal lossy source coding. ISIT András György, Gergely Neu
2010	A	`conf`	A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping. AISTATS Péter Torma, András György, Csaba Szepesvári
2010	J	`jnl`	On-Line Sequential Bin Packing. J. Mach. Learn. Res. András György, Gábor Lugosi, György Ottucsák
2010	—	`conf`	Online Markov Decision Processes under Bandit Feedback. NIPS Gergely Neu, András György, Csaba Szepesvári, András Antos
2010	A*	`conf`	The Online Loop-free Stochastic Shortest-Path Problem. COLT Gergely Neu, András György, Csaba Szepesvári
2009	—	`conf`	Efficient Multi-start Strategies for Local Search Algorithms. ECML/PKDD (1) Levente Kocsis, András György
2008	A*	`conf`	On-line Sequential Bin Packing. COLT András György, Gábor Lugosi, György Ottucsák
2008	J	`jnl`	Tracking the Best Quantizer. IEEE Trans. Inf. Theory András György, Tamás Linder, Gábor Lugosi
2007	A*	`conf`	Continuous Time Associative Bandit Problems. IJCAI András György, Levente Kocsis, Ivett Szabó, Csaba Szepesvári
2007	J	`jnl`	The On-Line Shortest Path Problem Under Partial Monitoring. J. Mach. Learn. Res. András György, Tamás Linder, Gábor Lugosi, György Ottucsák
2007	J	`jnl`	The on-line shortest path problem under partial monitoring CoRR András György, Tamás Linder, Gábor Lugosi, György Ottucsák
2006	J	`jnl`	Adaptive Routing Using Expert Advice. Comput. J. András György, György Ottucsák
2006	A*	`conf`	The Shortest Path Problem Under Partial Monitoring. COLT András György, Tamás Linder, György Ottucsák
2006	B	`conf`	The Shortest Path Problem in the Bandit Setting. ITW András György, Tamás Linder, Gábor Lugosi
2005	J	`jnl`	Individual convergence rates in empirical vector quantizer design. IEEE Trans. Inf. Theory András Antos, László Györfi, András György
2005	—	`conf`	Limited-Delay Coding of Individual Sequences with Piecewise Different Behavior. CDC/ECC András György, Tamás Linder, Gábor Lugosi
2005	A*	`conf`	Tracking the Best of Many Experts. COLT András György, Tamás Linder, Gábor Lugosi
2005	B	`conf`	Tracking the best quantizer. ISIT András György, Tamás Linder, Gábor Lugosi
2004	B	`conf`	A "Follow the Perturbed Leader"-type Algorithm for Zero-Delay Quantization of Individual Sequence. Data Compression Conference András György, Tamás Linder, Gábor Lugosi
2004	J	`jnl`	Efficient adaptive algorithms and minimax bounds for zero-delay lossy source coding. IEEE Trans. Signal Process. András György, Tamás Linder, Gábor Lugosi
2004	B	`conf`	Efficient algorithms and minimax bounds for zero-delay lossy source coding. ISIT András György, Tamás Linder, Gábor Lugosi
2004	B	`conf`	Improved convergence rates in empirical vector quantizer design. ISIT András Antos, László Györfi, András György
2003	J	`jnl`	Codecell convexity in optimal entropy-constrained vector quantization. IEEE Trans. Inf. Theory András György, Tamás Linder
2003	J	`jnl`	Do optimal entropy-constrained quantizers have a finite or infinite number of codewords? IEEE Trans. Inf. Theory András György, Tamás Linder, Philip A. Chou, Bradley J. Betts
2002	J	`jnl`	On the structure of optimal entropy-constrained scalar quantizers. IEEE Trans. Inf. Theory András György, Tamás Linder
2001	B	`conf`	Estimates on the packet loss ratio via queue tail probabilities. GLOBECOM András György, Tamás Borsos
2000	J	`jnl`	Optimal entropy-constrained scalar quantization of a uniform source. IEEE Trans. Inf. Theory András György, Tamás Linder
1999	J	`jnl`	On the rate-distortion function of random vectors and stationary sources with mixed distributions. IEEE Trans. Inf. Theory András György, Tamás Linder, Kenneth Zeger