
Metalearning of Sequential Strategies
In this report we review memorybased metalearning as a tool for buildi...
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Detecting Overfitting via Adversarial Examples
The repeated reuse of test sets in popular benchmark problems raises dou...
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Communication without Interception: Defense against DeepLearningbased Modulation Detection
We consider a communication scenario, in which an intruder, employing a ...
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Learning from Delayed Outcomes with Intermediate Observations
Optimizing for long term value is desirable in many practical applicatio...
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A Reinforcement Learning Approach to Age of Information in MultiUser Networks
Scheduling the transmission of timesensitive data to multiple users ove...
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A Modular Analysis of Adaptive (Non)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds
Recently, much work has been done on extending the scope of online learn...
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(Bandit) Convex Optimization with Biased Noisy Gradient Oracles
Algorithms for bandit convex optimization and online learning often rely...
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Chaining Bounds for Empirical Risk Minimization
This paper extends the standard chaining technique to prove excess risk ...
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Adaptive Monte Carlo via Bandit Allocation
We consider the problem of sequentially choosing between a set of unbias...
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Online Learning with Gaussian Payoffs and Side Observations
We consider a sequential learning problem with Gaussian payoffs and side...
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Fast CrossValidation for Incremental Learning
Crossvalidation (CV) is one of the main tools for performance estimatio...
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Efficient MultiStart Strategies for Local Search Algorithms
Local search algorithms applied to optimization problems often suffer fr...
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Online Learning under Delayed Feedback
Online learning with delayed feedback has received increasing attention ...
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Partition Tree Weighting
This paper introduces the Partition Tree Weighting technique, an efficie...
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A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning
We consider the problem of simultaneously learning to linearly combine a...
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A ReinforcementLearning Approach to Proactive Caching in Wireless Networks
We consider a mobile user accessing contents in a dynamic environment, w...
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Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
Machine learning has become an important component for many systems and ...
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LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
We consider the problem of configuring generalpurpose solvers to run ef...
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A Weakness Measure for GR(1) Formulae
In spite of the theoretical and algorithmic developments for system synt...
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Adaptive MCMC via Combining Local Samplers
Markov chain Monte Carlo (MCMC) methods are widely used in machine learn...
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Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost
The time average expected age of information (AoI) is studied for status...
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Minimal Assumptions Refinement for GR(1) Specifications
Reactive synthesis is concerned with finding a correctbyconstruction c...
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NonStationary Bandits with Intermediate Observations
Online recommender systems often face long delays in receiving feedback,...
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Confident OffPolicy Evaluation and Selection through SelfNormalized Importance Weighting
We consider offpolicy evaluation in the contextual bandit setting for t...
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András György
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Ph.D. degree in technical informatics from the Budapest University of Technology and Economics in 2003, Visiting Research Scholar in the Department of Electrical and Computer Engineering, University of California, San Diego, USA, in spring of 1998, Computer and Automation Research Institute of the Hungarian Academy of Sciences from 20022011, Senior Researcher and Head of the Machine Learning Research Group in 2006, NATO Science Fellow in the Department of Mathematics and Statistics, Queen's University 20032004, parttime research position at GusGus Capital Llc., Budapest, Hungary, in 20062011, researcher in the Department of Computing Science, University of Alberta, Edmonton, AB, Canada 20122015, Senior Lecturer in the Department of Electrical and Electronic Engineering of Imperial College London since 2006.