Lake thermal dynamics have been considerably impacted by climate change, with potential
adverse effects on aquatic ecosystems. To better understand the potential impacts of future …

In this paper, we briefly review the development of ranking and selection (R&S) in the past
70 years, especially the theoretical achievements and practical applications in the past 20 …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

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 …

Consider a Markov decision process (MDP) that admits a set of state-action features, which
can linearly express the process's probabilistic transition model. We propose a parametric Q …

Strong worst-case performance bounds for episodic reinforcement learning exist but
fortunately in practice RL algorithms perform much better than such bounds would predict …

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 …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

The stochastic multi-armed bandit model is a simple abstraction that has proven useful in
many different contexts in statistics and machine learning. Whereas the achievable limit in …