Imperial College London

Dr András György

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6173a.gyorgy Website

 
 
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Location

 

1003Electrical EngineeringSouth Kensington Campus

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Summary

 

Summary

On leave at Deepmind, London, UK.


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.

Selected Publications

Journal Articles

Somuyiwa S, Gunduz D, Gyorgy A, A reinforcement-learning approach to proactive caching in wireless networks., IEEE Journal on Selected Areas in Communications, ISSN:0733-8716

Huang R, Lattimore T, Gyorgy A, 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

Hullár B, Laki S, György A, 2014, Efficient Methods for Early Protocol Identification, IEEE Journal on Selected Areas in Communications, Vol:32, ISSN:0733-8716, Pages:1907-1918

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

Neufeld J, György A, Szepesvári C, et al., 2014, Adaptive Monte Carlo via Bandit Allocation, Proceedings of the 31st International Conference on Machine Learning, Vol:32, Pages:1944–1952-1944–1952

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

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

Neu G, György A, Szepesvári C, 2012, The adversarial stochastic shortest path problem with unknown transition probabilities, Aistat, Pages:805-813

György A, Lugosi G, Ottucsák G, 2010, On-line sequential bin packing, Journal of Machine Learning Research, Vol:11, Pages:89-109

Kocsis L, György A, 2009, Efficient Multi-start Strategies for Local Search Algorithms, Vol:5781, Pages:705-720

Conference

Weisz G, Gyorgy A, Szepesvari C, 2018, LeapsAndBounds: a method for approximately optimal algorithm configuration, International Conference on Machine Learning, PMLR, Pages:5257-5265

Joulani P, Gyorgy A, Szepesvari C, A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds, 28th International Conference on Algorithmic Learning Theory

Shaloudegi K, Gyorgy A, Szepesvari C, 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.

Huang R, Lattimore T, Gyorgy A, et al., 2016, Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities, Advances in Neural Information Processing Systems 29 (NIPS 2016), Neutral Information Processing Systems Foundation, Inc.

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, Gyorgy A, Szepesvari C, 2015, Deterministic Independent Component Analysis, 32nd International Conference on Machine Learning

Joulani P, Gyorgy A, Szepesvari C, 2015, Fast Cross-Validation for Incremental Learning, 24th International Joint Conference on Artificial Intelligence

Wu Y, Gyorgy A, Szepesvari C, 2015, On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments, 32nd International Conference on Machine Learning

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

More Publications