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András György 0001
András György Békés
Person information
- affiliation: Deepmind, London, UK
- affiliation: Imperial College London, London, UK
- affiliation (former): University of Alberta, Edmonton, Canada
- affiliation (Ph.D., 2003): Budapest University of Technology and Economics, Hungary
Other persons with the same name
- András György 0002
— New York University Abu Dhabi, Abu Dhabi, UAE (and 1 more)
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2020 – today
- 2024
- [j27]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. RLJ 5: 2461-2491 (2024) - [i59]Nicolas Nguyen, Imad Aouali, András György, Claire Vernade:
Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits. CoRR abs/2402.05878 (2024) - [i58]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 abs/2403.01518 (2024) - [i57]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 abs/2405.01563 (2024) - [i56]Yasin Abbasi-Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári:
To Believe or Not to Believe Your LLM. CoRR abs/2406.02543 (2024) - [i55]Alex Lewandowski, Saurabh Kumar, Dale Schuurmans, András György, Marlos C. Machado:
Learning Continually by Spectral Regularization. CoRR abs/2406.06811 (2024) - [i54]Bryan Chan, Xinyi Chen, András György, Dale Schuurmans:
Toward Understanding In-context vs. In-weight Learning. CoRR abs/2410.23042 (2024) - 2023
- [j26]Yasin Abbasi-Yadkori, András György, Nevena Lazic:
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits. J. Mach. Learn. Res. 24: 288:1-288:37 (2023) - [c78]Tor Lattimore, András György:
A Second-Order Method for Stochastic Bandit Convex Optimisation. COLT 2023: 2067-2094 - [c77]Sanae Amani, Tor Lattimore, András György, Lin Yang:
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. ICML 2023: 691-717 - [c76]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: 33632-33656 - [c75]Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado Philip van Hasselt, András György, Satinder Singh:
Optimistic Meta-Gradients. NeurIPS 2023 - [c74]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 - [c73]Gellért Weisz, András György, Csaba Szepesvári:
Online RL in Linearly qπ-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore. NeurIPS 2023 - [i53]Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh:
Optimistic Meta-Gradients. CoRR abs/2301.03236 (2023) - [i52]Tor Lattimore, András György:
A Second-Order Method for Stochastic Bandit Convex Optimisation. CoRR abs/2302.05371 (2023) - [i51]Qinghua Liu, Gellért Weisz, András György, Chi Jin, Csaba Szepesvári:
Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL. CoRR abs/2305.11032 (2023) - [i50]Gellért Weisz, András György, Csaba Szepesvári:
Online RL in Linearly qπ-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore. CoRR abs/2310.07811 (2023) - 2022
- [j25]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: 75:1-75:36 (2022) - [c72]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: 7176-7186 - [c71]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: 1097-1137 - [c70]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 - [c69]Tomer Galanti, András György, Marcus Hutter:
On the Role of Neural Collapse in Transfer Learning. ICLR 2022 - [c68]Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári:
Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs. NeurIPS 2022 - [i49]Yasin Abbasi-Yadkori, András György, Nevena Lazic:
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits. CoRR abs/2201.06532 (2022) - [i48]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 abs/2202.13001 (2022) - [i47]Sanae Amani, Tor Lattimore, András György, Lin F. Yang
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost. CoRR abs/2205.13170 (2022) - [i46]Gellért Weisz, András György, Tadashi Kozuno, Csaba Szepesvári:
Confident Approximate Policy Iteration for Efficient Local Planning in qπ-realizable MDPs. CoRR abs/2210.15755 (2022) - [i45]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 abs/2212.03319 (2022) - [i44]Tomer Galanti, András György, Marcus Hutter:
Generalization Bounds for Transfer Learning with Pretrained Classifiers. CoRR abs/2212.12532 (2022) - 2021
- [j24]Davide G. Cavezza
A Weakness Measure for GR(1) Formulae. Formal Aspects Comput. 33(1): 27-63 (2021) - [j23]Elif Tugçe Ceran
A Reinforcement Learning Approach to Age of Information in Multi-User Networks With HARQ. IEEE J. Sel. Areas Commun. 39(5): 1412-1426 (2021) - [j22]Muhammad Zaid Hameed
The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection. IEEE Trans. Inf. Forensics Secur. 16: 1074-1087 (2021) - [c67]Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári:
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. AISTATS 2021: 640-648 - [c66]Tor Lattimore, András György:
Improved Regret for Zeroth-Order Stochastic Convex Bandits. COLT 2021: 2938-2964 - [c65]Tor Lattimore, András György:
Mirror Descent and the Information Ratio. COLT 2021: 2965-2992 - [c64]András György, Pooria Joulani:
Adapting to Delays and Data in Adversarial Multi-Armed Bandits. ICML 2021: 3988-3997 - [i43]Muhammad Zaid Hameed, András György:
Perceptually Constrained Adversarial Attacks. CoRR abs/2102.07140 (2021) - [i42]Elif Tugce Ceran, Deniz Gündüz, András György:
A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ. CoRR abs/2102.09774 (2021) - [i41]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 abs/2104.01086 (2021) - [i40]Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning. CoRR abs/2106.08199 (2021) - [i39]Elif Tugce Ceran, Deniz Gündüz, András György:
Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting. CoRR abs/2106.16037 (2021) - [i38]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 abs/2110.02195 (2021) - [i37]Tomer Galanti, András György, Marcus Hutter:
On the Role of Neural Collapse in Transfer Learning. CoRR abs/2112.15121 (2021) - 2020
- [j21]Pooria Joulani, András György
A modular analysis of adaptive (non-)convex optimization: Optimism, composite objectives, variance reduction, and variational bounds. Theor. Comput. Sci. 808: 108-138 (2020) - [c63]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 - [c62]Pooria Joulani, Anant Raj, András György, Csaba Szepesvári:
A simpler approach to accelerated optimization: iterative averaging meets optimism. ICML 2020: 4984-4993 - [c61]Claire Vernade, András György, Timothy A. Mann:
Non-Stationary Delayed Bandits with Intermediate Observations. ICML 2020: 9722-9732 - [c60]Davide G. Cavezza, Dalal Alrajeh, András György:
Minimal Assumptions Refinement for Realizable Specifications. FormaliSE@ICSE 2020: 66-76 - [c59]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 - [i36]Claire Vernade, András György, Timothy A. Mann:
Non-Stationary Bandits with Intermediate Observations. CoRR abs/2006.02119 (2020) - [i35]Ilja Kuzborskij, Claire Vernade, András György, Csaba Szepesvári:
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting. CoRR abs/2006.10460 (2020) - [i34]Tor Lattimore, András György:
Mirror Descent and the Information Ratio. CoRR abs/2009.12228 (2020) - [i33]András György, Pooria Joulani:
Adapting to Delays and Data in Adversarial Multi-Armed Bandits. CoRR abs/2010.06022 (2020) - [i32]Gábor Melis, András György, Phil Blunsom:
Mutual Information Constraints for Monte-Carlo Objectives. CoRR abs/2012.00708 (2020)
2010 – 2019
- 2019
- [j20]Elif Tugce Ceran
Average Age of Information With Hybrid ARQ Under a Resource Constraint. IEEE Trans. Wirel. Commun. 18(3): 1900-1913 (2019) - [c58]Ray Jiang, Silvia Chiappa, Tor Lattimore, András György
Degenerate Feedback Loops in Recommender Systems. AIES 2019: 383-390 - [c57]Kiárash Shaloudegi, András György:
Adaptive MCMC via Combining Local Samplers. AISTATS 2019: 2701-2710 - [c56]Muhammad Zaid Hameed
Communication without Interception: Defense against Modulation Detection. GlobalSIP 2019: 1-5 - [c55]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: 4324-4332 - [c54]Gellért Weisz, András György, Csaba Szepesvári:
CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration. ICML 2019: 6707-6715 - [c53]Elif Tugçe Ceran
Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost. INFOCOM Workshops 2019: 656-661 - [c52]Roman Werpachowski, András György, Csaba Szepesvári:
Detecting Overfitting via Adversarial Examples. NeurIPS 2019: 7856-7866 - [c51]Pooria Joulani, András György, Csaba Szepesvári:
Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging. NeurIPS 2019: 12225-12235 - [c50]Samuel O. Somuyiwa, András György
Multicast-Aware Proactive Caching in Wireless Networks with Deep Reinforcement Learning. SPAWC 2019: 1-5 - [i31]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 abs/1902.09467 (2019) - [i30]Muhammad Zaid Hameed, András György, Deniz Gündüz:
Communication without Interception: Defense against Deep-Learning-based Modulation Detection. CoRR abs/1902.10674 (2019) - [i29]Ray Jiang, Silvia Chiappa, Tor Lattimore, András György, Pushmeet Kohli:
Degenerate Feedback Loops in Recommender Systems. CoRR abs/1902.10730 (2019) - [i28]Roman Werpachowski
Detecting Overfitting via Adversarial Examples. CoRR abs/1903.02380 (2019) - [i27]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:
Meta-learning of Sequential Strategies. CoRR abs/1905.03030 (2019) - [i26]Davide G. Cavezza, Dalal Alrajeh, András György:
Minimal Assumptions Refinement for GR(1) Specifications. CoRR abs/1910.05558 (2019) - 2018
- [j19]Samuel O. Somuyiwa
A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks. IEEE J. Sel. Areas Commun. 36(6): 1331-1344 (2018) - [c49]Davide Giacomo Cavezza, Dalal Alrajeh, András György
A Weakness Measure for GR(1) Formulae. FM 2018: 110-128 - [c48]Gellért Weisz, András György, Csaba Szepesvári:
LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration. ICML 2018: 5254-5262 - [c47]Samuel O. Somuyiwa, Deniz Gündüz
Reinforcement Learning for Proactive Caching of Contents with Different Demand Probabilities. ISWCS 2018: 1-6 - [c46]Elif Tugce Ceran
A Reinforcement Learning Approach to Age of Information in Multi-User Networks. PIMRC 2018: 1967-1971 - [c45]Elif Tugce Ceran
Average age of information with hybrid ARQ under a resource constraint. WCNC 2018: 1-6 - [i25]Andrea Paudice, Luis Muñoz-González, András György, Emil C. Lupu:
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection. CoRR abs/1802.03041 (2018) - [i24]Davide G. Cavezza, Dalal Alrajeh, András György:
A Weakness Measure for GR(1) Formulae. CoRR abs/1805.03151 (2018) - [i23]Elif Tugçe Ceran, Deniz Gündüz, András György:
A Reinforcement Learning Approach to Age of Information in Multi-User Networks. CoRR abs/1806.00336 (2018) - [i22]Kiarash Shaloudegi, András György:
Adaptive MCMC via Combining Local Samplers. CoRR abs/1806.03816 (2018) - [i21]Gellért Weisz, András György, Csaba Szepesvári:
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration. CoRR abs/1807.00755 (2018) - [i20]Timothy A. Mann, Sven Gowal, Ray Jiang, Huiyi Hu, Balaji Lakshminarayanan, András György:
Learning from Delayed Outcomes with Intermediate Observations. CoRR abs/1807.09387 (2018) - 2017
- [j18]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: 145:1-145:31 (2017) - [c44]Pooria Joulani, András György, Csaba Szepesvári:
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds. ALT 2017: 681-720 - [c43]Samuel O. Somuyiwa, András György
Energy-efficient wireless content delivery with proactive caching. WiOpt 2017: 1-6 - [c42]Samuel O. Somuyiwa, András György
Improved policy representation and policy search for proactive content caching in wireless networks. WiOpt 2017: 1-8 - [i19]Ruitong Huang, Tor Lattimore, András György, Csaba Szepesvári:
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities. CoRR abs/1702.03040 (2017) - [i18]Samuel O. Somuyiwa, András György, Deniz Gündüz:
Energy-Efficient Wireless Content Delivery with Proactive Caching. CoRR abs/1702.06382 (2017) - [i17]Pooria Joulani, András György, Csaba Szepesvári:
A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds. CoRR abs/1709.02726 (2017) - [i16]Elif Tugce Ceran, Deniz Gündüz, András György:
Average Age of Information with Hybrid ARQ under a Resource Constraint. CoRR abs/1710.04971 (2017) - [i15]Samuel O. Somuyiwa, András György, Deniz Gündüz:
A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks. CoRR abs/1712.07084 (2017) - 2016
- [c41]Pooria Joulani, András György, Csaba Szepesvári:
Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms. AAAI 2016: 1744-1750 - [c40]Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári:
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. AISTATS 2016: 819-828 - [c39]András György, Csaba Szepesvári:
Shifting Regret, Mirror Descent, and Matrices. ICML 2016: 2943-2951 - [c38]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: 4970-4978 - [c37]Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu:
SDP Relaxation with Randomized Rounding for Energy Disaggregation. NIPS 2016: 4979-4987 - [i14]Gábor Balázs, András György, Csaba Szepesvári:
Chaining Bounds for Empirical Risk Minimization. CoRR abs/1609.01872 (2016) - [i13]Gábor Balázs, András György, Csaba Szepesvári:
Max-affine estimators for convex stochastic programming. CoRR abs/1609.06331 (2016) - [i12]Xiaowei Hu, Prashanth L. A., András György, Csaba Szepesvári:
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles. CoRR abs/1609.07087 (2016) - [i11]Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu:
SDP Relaxation with Randomized Rounding for Energy Disaggregation. CoRR abs/1610.09491 (2016) - 2015
- [c36]Gábor Balázs, András György, Csaba Szepesvári:
Near-optimal max-affine estimators for convex regression. AISTATS 2015 - [c35]Roshan Shariff, András György, Csaba Szepesvári:
Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM. AISTATS 2015 - [c34]Yifan Wu, András György, Csaba Szepesvári:
On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments. ICML 2015: 1283-1291 - [c33]Ruitong Huang, András György, Csaba Szepesvári:
Deterministic Independent Component Analysis. ICML 2015: 2521-2530 - [c32]Pooria Joulani, András György, Csaba Szepesvári:
Fast Cross-Validation for Incremental Learning. IJCAI 2015: 3597-3604 - [c31]Yifan Wu, András György, Csaba Szepesvári:
Online Learning with Gaussian Payoffs and Side Observations. NIPS 2015: 1360-1368 - [c30]Farzaneh Mirzazadeh, Martha White, András György
Scalable Metric Learning for Co-Embedding. ECML/PKDD (1) 2015: 625-642 - [i10]Pooria Joulani, András György, Csaba Szepesvári:
Fast Cross-Validation for Incremental Learning. CoRR abs/1507.00066 (2015) - [i9]Yifan Wu, András György, Csaba Szepesvári:
Online Learning with Gaussian Payoffs and Side Observations. CoRR abs/1510.08108 (2015) - 2014
- [j17]Béla Hullár, Sándor Laki
Efficient Methods for Early Protocol Identification. IEEE J. Sel. Areas Commun. 32(10): 1907-1918 (2014) - [j16]Gergely Neu
Online Markov Decision Processes Under Bandit Feedback. IEEE Trans. Autom. Control. 59(3): 676-691 (2014) - [j15]András György
Near-Optimal Rates for Limited-Delay Universal Lossy Source Coding. IEEE Trans. Inf. Theory 60(5): 2823-2834 (2014) - [c29]Tor Lattimore, András György, Csaba Szepesvári:
On Learning the Optimal Waiting Time. ALT 2014: 200-214 - [c28]Travis Dick, András György, Csaba Szepesvári:
Online Learning in Markov Decision Processes with Changing Cost Sequences. ICML 2014: 512-520 - [c27]James Neufeld, András György, Csaba Szepesvári, Dale Schuurmans:
Adaptive Monte Carlo via Bandit Allocation. ICML 2014: 1944-1952 - [i8]András György, Levente Kocsis:
Efficient Multi-Start Strategies for Local Search Algorithms. CoRR abs/1401.3894 (2014) - [i7]James Neufeld, András György, Dale Schuurmans, Csaba Szepesvári:
Adaptive Monte Carlo via Bandit Allocation. CoRR abs/1405.3318 (2014) - 2013
- [j14]Levente Kocsis, András György
BoostingTree: parallel selection of weak learners in boosting, with application to ranking. Mach. Learn. 93(2-3): 293-320 (2013) - [c26]Joel Veness, Martha White, Michael Bowling, András György
Partition Tree Weighting. DCC 2013: 321-330 - [c25]Arash Afkanpour, András György, Csaba Szepesvári, Michael Bowling:
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning. ICML (1) 2013: 374-382 - [c24]Pooria Joulani, András György, Csaba Szepesvári:
Online Learning under Delayed Feedback. ICML (3) 2013: 1453-1461 - [c23]Navid Zolghadr, Gábor Bartók, Russell Greiner, András György, Csaba Szepesvári:
Online Learning with Costly Features and Labels. NIPS 2013: 1241-1249 - [i6]Pooria Joulani, András György, Csaba Szepesvári:
Online Learning under Delayed Feedback. CoRR abs/1306.0686 (2013) - 2012
- [j13]András György
Efficient Tracking of Large Classes of Experts. IEEE Trans. Inf. Theory 58(11): 6709-6725 (2012) - [c22]András György
Efficient tracking of large classes of experts. ISIT 2012: 885-889 - [c21]Gergely Neu, András György, Csaba Szepesvári:
The adversarial stochastic shortest path problem with unknown transition probabilities. AISTATS 2012: 805-813 - [i5]Arash Afkanpour, András György, Csaba Szepesvári, Michael H. Bowling:
A Randomized Strategy for Learning to Combine Many Features. CoRR abs/1205.0288 (2012) - [i4]Joel Veness, Martha White, Michael Bowling, András György:
Partition Tree Weighting. CoRR abs/1211.0587 (2012) - 2011
- [j12]András György
Efficient Multi-Start Strategies for Local Search Algorithms. J. Artif. Intell. Res. 41: 407-444 (2011) - [c20]Béla Hullár, Sándor Laki
Early Identification of Peer-to-Peer Traffic. ICC 2011: 1-6 - [c19]András György
Near-optimal rates for limited-delay universal lossy source coding. ISIT 2011: 2218-2222 - [i3]András György, Tamás Linder, Gábor Lugosi:
Efficient Tracking of Large Classes of Experts. CoRR abs/1110.2755 (2011) - 2010
- [j11]András György, Gábor Lugosi, György Ottucsák:
On-Line Sequential Bin Packing. J. Mach. Learn. Res. 11: 89-109 (2010) - [c18]Gergely Neu, András György, Csaba Szepesvári:
The Online Loop-free Stochastic Shortest-Path Problem. COLT 2010: 231-243 - [c17]Gergely Neu, András György, Csaba Szepesvári, András Antos:
Online Markov Decision Processes under Bandit Feedback. NIPS 2010: 1804-1812 - [c16]Péter Torma, András György, Csaba Szepesvári:
A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping. AISTATS 2010: 852-859
2000 – 2009
- 2009
- [c15]Levente Kovács, András György, Zsuzsa Almássy, Zoltán Benyó:
Analyzing a novel model of human blood glucose system at molecular levels. ECC 2009: 2494-2499 - [c14]András György, István Á. Harmati:
Motion planning algorithms for tactical actions in robot soccer. ECC 2009: 4445-4450 - [c13]Levente Kocsis, András György
Efficient Multi-start Strategies for Local Search Algorithms. ECML/PKDD (1) 2009: 705-720 - 2008
- [j10]András György
Tracking the Best Quantizer. IEEE Trans. Inf. Theory 54(4): 1604-1625 (2008) - [c12]András György, Gábor Lugosi, György Ottucsák:
On-line Sequential Bin Packing. COLT 2008: 447-454 - 2007
- [j9]András György, Tamás Linder, Gábor Lugosi, György Ottucsák:
The On-Line Shortest Path Problem Under Partial Monitoring. J. Mach. Learn. Res. 8: 2369-2403 (2007) - [c11]András György, Levente Kocsis, Ivett Szabó, Csaba Szepesvári:
Continuous Time Associative Bandit Problems. IJCAI 2007: 830-835 - [i2]András György, Tamás Linder, Gábor Lugosi, György Ottucsák:
The on-line shortest path problem under partial monitoring. CoRR abs/0704.1020 (2007) - [i1]András György Békés, Péter Szeredi:
Optimizing Queries in a Logic-based Information Integration System. CoRR abs/0712.3113 (2007) - 2006
- [j8]András György
Adaptive Routing Using Expert Advice. Comput. J. 49(2): 180-189 (2006) - [c10]András György, Tamás Linder, György Ottucsák:
The Shortest Path Problem Under Partial Monitoring. COLT 2006: 468-482 - [c9]András György, Tamás Linder, Gábor Lugosi:
The Shortest Path Problem in the Bandit Setting. ITW 2006: 87-91 - 2005
- [j7]András Antos, László Györfi, András György
Individual convergence rates in empirical vector quantizer design. IEEE Trans. Inf. Theory 51(11): 4013-4022 (2005) - [c8]András György, Tamás Linder, Gábor Lugosi
Limited-Delay Coding of Individual Sequences with Piecewise Different Behavior. CDC/ECC 2005: 8185-8190 - [c7]András György, Tamás Linder, Gábor Lugosi:
Tracking the Best of Many Experts. COLT 2005: 204-216 - [c6]András György Békés:
Optimizing Queries for Heterogeneous Information Sources. ICLP 2005: 429-430 - [c5]András György, Tamás Linder, Gábor Lugosi:
Tracking the best quantizer. ISIT 2005: 1163-1167 - 2004
- [j6]András György
Efficient adaptive algorithms and minimax bounds for zero-delay lossy source coding. IEEE Trans. Signal Process. 52(8): 2337-2347 (2004) - [c4]András György, Tamás Linder, Gábor Lugosi:
A "Follow the Perturbed Leader"-type Algorithm for Zero-Delay Quantization of Individual Sequence. Data Compression Conference 2004: 342-351 - [c3]András Antos, László Györfi, András György:
Improved convergence rates in empirical vector quantizer design. ISIT 2004: 301 - [c2]András György, Tamás Linder, Gábor Lugosi:
Efficient algorithms and minimax bounds for zero-delay lossy source coding. ISIT 2004: 463 - 2003
- [j5]András György
Codecell convexity in optimal entropy-constrained vector quantization. IEEE Trans. Inf. Theory 49(7): 1821-1828 (2003) - [j4]András György
Do optimal entropy-constrained quantizers have a finite or infinite number of codewords? IEEE Trans. Inf. Theory 49(11): 3031-3037 (2003) - 2002
- [j3]András György
On the structure of optimal entropy-constrained scalar quantizers. IEEE Trans. Inf. Theory 48(2): 416-427 (2002) - 2001
- [c1]András György, Tamás Borsos:
Estimates on the packet loss ratio via queue tail probabilities. GLOBECOM 2001: 2407-2411 - 2000
- [j2]András György
Optimal entropy-constrained scalar quantization of a uniform source. IEEE Trans. Inf. Theory 46(7): 2704-2711 (2000)
1990 – 1999
- 1999
- [j1]András György
On the rate-distortion function of random vectors and stationary sources with mixed distributions. IEEE Trans. Inf. Theory 45(6): 2110-2115 (1999)
Coauthor Index
[i57] [i56] [c74] [c73] [i51] [i50] [c72] [c71] [c68] [i46] [c67] [i40] [i38] [j21] [c62] [c59] [i35] [c54] [c52] [c51] [i28] [c48] [i21] [j18] [c44] [i19] [i17] [c41] [c40] [c39] [c38] [c37] [i14] [i13] [i12] [i11] [c36] [c35] [c34] [c33] [c32] [c31] [i10] [i9] [j16] [c29] [c28] [c27] [i7] [c25] [c24] [c23] [i6] [c21] [i5] [c18] [c17] [c16] [c11]
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