András György

Orcid: 0000-0003-0586-4337

Affiliations:
  • Deepmind, London, UK
  • Imperial College London, London, UK
  • University of Alberta, Edmonton, Canada (former)
  • Budapest University of Technology and Economics, Hungary (Ph.D., 2003)


According to our database1, András György authored at least 118 papers between 1999 and 2024.

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Bibliography

2024
Learning Continually by Spectral Regularization.
CoRR, 2024

To Believe or Not to Believe Your LLM.
CoRR, 2024

Mitigating LLM Hallucinations via Conformal Abstention.
CoRR, 2024

Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models.
CoRR, 2024

Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits.
CoRR, 2024

Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms.
Proceedings of the 1st Reinforcement Learning Conference, 2024

2023
A New Look at Dynamic Regret for Non-Stationary Stochastic Bandits.
J. Mach. Learn. Res., 2023

Online RL in Linearly q<sup>π</sup>-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Optimistic Meta-Gradients.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Understanding Self-Predictive Learning for Reinforcement Learning.
Proceedings of the International Conference on Machine Learning, 2023

Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost.
Proceedings of the International Conference on Machine Learning, 2023

A Second-Order Method for Stochastic Bandit Convex Optimisation.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling.
J. Mach. Learn. Res., 2022

Generalization Bounds for Transfer Learning with Pretrained Classifiers.
CoRR, 2022

Confident Approximate Policy Iteration for Efficient Local Planning in q<sup>π</sup>-realizable MDPs.
CoRR, 2022

Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On the Role of Neural Collapse in Transfer Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Defending Against Image Corruptions Through Adversarial Augmentations.
Proceedings of the Tenth International Conference on Learning Representations, 2022

TensorPlan and the Few Actions Lower Bound for Planning in MDPs under Linear Realizability of Optimal Value Functions.
Proceedings of the International Conference on Algorithmic Learning Theory, 29 March, 2022

Faster Rates, Adaptive Algorithms, and Finite-Time Bounds for Linear Composition Optimization and Gradient TD Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection.
IEEE Trans. Inf. Forensics Secur., 2021

A Reinforcement Learning Approach to Age of Information in Multi-User Networks With HARQ.
IEEE J. Sel. Areas Commun., 2021

A Weakness Measure for GR(1) Formulae.
Formal Aspects Comput., 2021

Learning to Minimize Age of Information over an Unreliable Channel with Energy Harvesting.
CoRR, 2021

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning.
CoRR, 2021

Perceptually Constrained Adversarial Attacks.
CoRR, 2021

Adapting to Delays and Data in Adversarial Multi-Armed Bandits.
Proceedings of the 38th International Conference on Machine Learning, 2021

Mirror Descent and the Information Ratio.
Proceedings of the Conference on Learning Theory, 2021

Improved Regret for Zeroth-Order Stochastic Convex Bandits.
Proceedings of the Conference on Learning Theory, 2021

Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
A modular analysis of adaptive (non-)convex optimization: Optimism, composite objectives, variance reduction, and variational bounds.
Theor. Comput. Sci., 2020

Mutual Information Constraints for Monte-Carlo Objectives.
CoRR, 2020

Non-Stationary Bandits with Intermediate Observations.
CoRR, 2020

ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Minimal Assumptions Refinement for Realizable Specifications.
Proceedings of the FormaliSE@ICSE 2020: 8th International Conference on Formal Methods in Software Engineering, 2020

Non-Stationary Delayed Bandits with Intermediate Observations.
Proceedings of the 37th International Conference on Machine Learning, 2020

A simpler approach to accelerated optimization: iterative averaging meets optimism.
Proceedings of the 37th International Conference on Machine Learning, 2020

A Framework for robustness Certification of Smoothed Classifiers using F-Divergences.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Average Age of Information With Hybrid ARQ Under a Resource Constraint.
IEEE Trans. Wirel. Commun., 2019

Minimal Assumptions Refinement for GR(1) Specifications.
CoRR, 2019

Meta-learning of Sequential Strategies.
CoRR, 2019

Communication without Interception: Defense against Deep-Learning-based Modulation Detection.
CoRR, 2019

Multicast-Aware Proactive Caching in Wireless Networks with Deep Reinforcement Learning.
Proceedings of the 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications, 2019

Detecting Overfitting via Adversarial Examples.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost.
Proceedings of the IEEE INFOCOM 2019, 2019

CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration.
Proceedings of the 36th International Conference on Machine Learning, 2019

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems.
Proceedings of the 36th International Conference on Machine Learning, 2019

Communication without Interception: Defense against Modulation Detection.
Proceedings of the 2019 IEEE Global Conference on Signal and Information Processing, 2019

Adaptive MCMC via Combining Local Samplers.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Degenerate Feedback Loops in Recommender Systems.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

2018
A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks.
IEEE J. Sel. Areas Commun., 2018

Learning from Delayed Outcomes with Intermediate Observations.
CoRR, 2018

Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection.
CoRR, 2018

A Reinforcement Learning Approach to Age of Information in Multi-User Networks.
Proceedings of the 29th IEEE Annual International Symposium on Personal, 2018

Reinforcement Learning for Proactive Caching of Contents with Different Demand Probabilities.
Proceedings of the 15th International Symposium on Wireless Communication Systems, 2018

LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities.
J. Mach. Learn. Res., 2017

Improved policy representation and policy search for proactive content caching in wireless networks.
Proceedings of the 15th International Symposium on Modeling and Optimization in Mobile, 2017

Energy-efficient wireless content delivery with proactive caching.
Proceedings of the 15th International Symposium on Modeling and Optimization in Mobile, 2017

A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds.
Proceedings of the International Conference on Algorithmic Learning Theory, 2017

2016
Max-affine estimators for convex stochastic programming.
CoRR, 2016

Chaining Bounds for Empirical Risk Minimization.
CoRR, 2016

SDP Relaxation with Randomized Rounding for Energy Disaggregation.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Shifting Regret, Mirror Descent, and Matrices.
Proceedings of the 33nd International Conference on Machine Learning, 2016

(Bandit) Convex Optimization with Biased Noisy Gradient Oracles.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Delay-Tolerant Online Convex Optimization: Unified Analysis and Adaptive-Gradient Algorithms.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Scalable Metric Learning for Co-Embedding.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Online Learning with Gaussian Payoffs and Side Observations.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Fast Cross-Validation for Incremental Learning.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Deterministic Independent Component Analysis.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Exploiting Symmetries to Construct Efficient MCMC Algorithms With an Application to SLAM.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Near-optimal max-affine estimators for convex regression.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Near-Optimal Rates for Limited-Delay Universal Lossy Source Coding.
IEEE Trans. Inf. Theory, 2014

Online Markov Decision Processes Under Bandit Feedback.
IEEE Trans. Autom. Control., 2014

Efficient Methods for Early Protocol Identification.
IEEE J. Sel. Areas Commun., 2014

Adaptive Monte Carlo via Bandit Allocation.
Proceedings of the 31th International Conference on Machine Learning, 2014

Online Learning in Markov Decision Processes with Changing Cost Sequences.
Proceedings of the 31th International Conference on Machine Learning, 2014

On Learning the Optimal Waiting Time.
Proceedings of the Algorithmic Learning Theory - 25th International Conference, 2014

2013
BoostingTree: parallel selection of weak learners in boosting, with application to ranking.
Mach. Learn., 2013

Online Learning with Costly Features and Labels.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Online Learning under Delayed Feedback.
Proceedings of the 30th International Conference on Machine Learning, 2013

A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning.
Proceedings of the 30th International Conference on Machine Learning, 2013

Partition Tree Weighting.
Proceedings of the 2013 Data Compression Conference, 2013

2012
Efficient Tracking of Large Classes of Experts.
IEEE Trans. Inf. Theory, 2012

The adversarial stochastic shortest path problem with unknown transition probabilities.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

A Randomized Strategy for Learning to Combine Many Features
CoRR, 2012

2011
Efficient Multi-Start Strategies for Local Search Algorithms.
J. Artif. Intell. Res., 2011

Early Identification of Peer-to-Peer Traffic.
Proceedings of IEEE International Conference on Communications, 2011

2010
A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

On-Line Sequential Bin Packing.
J. Mach. Learn. Res., 2010

The Online Loop-free Stochastic Shortest-Path Problem.
Proceedings of the COLT 2010, 2010

2009
Analyzing a novel model of human blood glucose system at molecular levels.
Proceedings of the 10th European Control Conference, 2009

Motion planning algorithms for tactical actions in robot soccer.
Proceedings of the 10th European Control Conference, 2009

2008
Tracking the Best Quantizer.
IEEE Trans. Inf. Theory, 2008

2007
The On-Line Shortest Path Problem Under Partial Monitoring.
J. Mach. Learn. Res., 2007

Optimizing Queries in a Logic-based Information Integration System
CoRR, 2007

Continuous Time Associative Bandit Problems.
Proceedings of the IJCAI 2007, 2007

2006
Adaptive Routing Using Expert Advice.
Comput. J., 2006

The Shortest Path Problem in the Bandit Setting.
Proceedings of the 2006 IEEE Information Theory Workshop, 2006

The Shortest Path Problem Under Partial Monitoring.
Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

2005
Individual convergence rates in empirical vector quantizer design.
IEEE Trans. Inf. Theory, 2005

Optimizing Queries for Heterogeneous Information Sources.
Proceedings of the Logic Programming, 21st International Conference, 2005

Tracking the Best of Many Experts.
Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005

Limited-Delay Coding of Individual Sequences with Piecewise Different Behavior.
Proceedings of the 44th IEEE IEEE Conference on Decision and Control and 8th European Control Conference Control, 2005

2004
Efficient adaptive algorithms and minimax bounds for zero-delay lossy source coding.
IEEE Trans. Signal Process., 2004

Efficient algorithms and minimax bounds for zero-delay lossy source coding.
Proceedings of the 2004 IEEE International Symposium on Information Theory, 2004

Improved convergence rates in empirical vector quantizer design.
Proceedings of the 2004 IEEE International Symposium on Information Theory, 2004

A "Follow the Perturbed Leader"-type Algorithm for Zero-Delay Quantization of Individual Sequence.
Proceedings of the 2004 Data Compression Conference (DCC 2004), 2004

2003
Do optimal entropy-constrained quantizers have a finite or infinite number of codewords?
IEEE Trans. Inf. Theory, 2003

Codecell convexity in optimal entropy-constrained vector quantization.
IEEE Trans. Inf. Theory, 2003

2002
On the structure of optimal entropy-constrained scalar quantizers.
IEEE Trans. Inf. Theory, 2002

2001
Estimates on the packet loss ratio via queue tail probabilities.
Proceedings of the Global Telecommunications Conference, 2001

2000
Optimal entropy-constrained scalar quantization of a uniform source.
IEEE Trans. Inf. Theory, 2000

1999
On the rate-distortion function of random vectors and stationary sources with mixed distributions.
IEEE Trans. Inf. Theory, 1999


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