András György received the M.Sc. (Eng.) degree (with distinction) in technical informatics from the Technical University of Budapest, in 1999, the M.Sc. (Eng.) degree in mathematics and engineering from Queen's University, Kingston, ON, Canada, in 2001, and the Ph.D. degree in technical informatics from the Budapest University of Technology and Economics in 2003. He was a Visiting Research Scholar in the Department of Electrical and Computer Engineering, University of California, San Diego, USA, in spring of 1998. In 2002-2011 he was with the Computer and Automation Research Institute of the Hungarian Academy of Sciences, where, from 2006, he was a Senior Researcher and Head of the Machine Learning Research Group. In 2003-2004 ,he was also a NATO Science Fellow in the Department of Mathematics and Statistics, Queen's University. He also held a part-time research position at GusGus Capital Llc., Budapest, Hungary, in 2006-2011. In 2012-2015, he was a researcher in the Department of Computing Science, University of Alberta, Edmonton, AB, Canada. He joined the Department of Electrical and Electronic Engineering of Imperial College London, London, UK, where he is currently a Senior Lecturer.
His research interests include machine learning, statistical learning theory, online learning, adaptive systems, information theory, and optimization.
Dr. György received the Gyula Farkas prize of the János Bolyai Mathematical Society in 2001 and the Academic Golden Ring of the President of the Hungarian Republic in 2003.
Somuyiwa SO, Gyorgy A, Gunduz D, 2018, A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks, IEEE Journal on Selected Areas in Communications, ISSN:0733-8716
et al., 2017, Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities, Journal of Machine Learning Research, Vol:18(145), ISSN:1532-4435, Pages:1-31
György A, Neu G, 2014, Near-Optimal Rates for Limited-Delay Universal Lossy Source Coding, IEEE Transactions on Information Theory, Vol:60, ISSN:0018-9448, Pages:2823-2834
Hullar B, Laki S, Gyorgy A, 2014, Efficient Methods for Early Protocol Identification, IEEE Journal on Selected Areas in Communications, ISSN:0733-8716, Pages:1-1
György A, Lugosi G, Ottucsák G, 2010, On-line sequential bin packing, Journal of Machine Learning Research, Vol:11, ISSN:1532-4435, Pages:89-109
Neu G, György A, Szepesvári C, 2012, The adversarial stochastic shortest path problem with unknown transition probabilities, Journal of Machine Learning Research, Vol:22, ISSN:1532-4435, Pages:805-813
Gyorgy A, Linder T, Lugosi G, 2012, Efficient Tracking of Large Classes of Experts, IEEE Transactions on Information Theory, Vol:58, ISSN:0018-9448, Pages:6709-6725
Kocsis L, György A, Bán AN, 2013, BoostingTree: Parallel selection of weak learners in boosting, with application to ranking, Machine Learning, Vol:93, ISSN:0885-6125, Pages:293-320
György A, Kocsis L, 2011, Efficient Multi-Start Strategies for Local Search Algorithms., J. Artif. Intell. Res., Vol:41, Pages:407-444
et al., 2014, Adaptive Monte Carlo via bandit allocation, 31st International Conference on Machine Learning, Icml 2014, Vol:5, Pages:4010-4037
Joulani P, György A, Szepesvári C, 2017, A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds., PMLR, Pages:681-720
et al., 2016, SDP relaxation with randomized rounding for energy disaggregation, The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS), Neutral Information Processing Systems Foundation, Inc.
et al., 2017, Following the leader and fast rates in online linear prediction: Curved constraint sets and other regularities, ISSN:1532-4435
Joulani P, Gyorgy A, Szepesvari C, 2016, Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms, Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), AAAI
Wu Y, György A, Szepesvari C, 2015, Online Learning with Gaussian Payoffs and Side Observations, 29th Annual Conference on Neural Information Processing Systems (NIPS), Neural Information Processing Systems Foundation, Inc.
Huang R, György A, Szepesvári C, 2015, Deterministic independent component analysis, Pages:2511-2520
Wu Y, György A, Szepesvári C, 2015, On identifying good options under combinatorially structured feedback in finite noisy environments, Pages:1283-1291
Dick T, Gyorgy A, Szepesvari C, 2014, Online Learning in Markov Decision Processes with Changing Cost Sequences, Proceedings of The 31st International Conference on Machine Learning, Pages:512-520
Joulani P, Gyorgy A, Szepesvari C, 2013, Online Learning under Delayed Feedback, 30th International Conference on Machine Learning, ICML 2013, Pages:1453-1461
Joulani P, Gyorgy A, Szepesvari C, 2015, Fast Cross-Validation for Incremental Learning, 24th International Joint Conference on Artificial Intelligence