A fundamental challenge in interactive learning and decision making, ranging from bandit problems to reinforcement learning, is to provide sample-efficient, adaptive learning …
This paper provides a comprehensive survey of techniques for testing machine learning systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …
A Slivkins - Foundations and Trends® in Machine Learning, 2019 - nowpublishers.com
Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the …
Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian optimization to adaptively select …
Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL), with many naive approaches succumbing to exponential sample complexity …
Two central paradigms have emerged in the reinforcement learning (RL) community: online RL and offline RL. In the online RL setting, the agent has no prior knowledge of the …
K Jamieson, A Talwalkar - Artificial intelligence and statistics, 2016 - proceedings.mlr.press
Motivated by the task of hyperparameter optimization, we introduce the\em non-stochastic best-arm identification problem. We identify an attractive algorithm for this setting that makes …
A Garivier, E Kaufmann - Conference on Learning Theory, 2016 - proceedings.mlr.press
We give a complete characterization of the complexity of best-arm identification in one- parameter bandit problems. We prove a new, tight lower bound on the sample complexity …