Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

In this paper, we make the key delineation on the roles of resolution and statistical
uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more …

A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

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 …

In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …

We consider the problem of provably optimal exploration in reinforcement learning for finite
horizon MDPs. We show that an optimistic modification to value iteration achieves a regret …

Performance of machine learning algorithms depends critically on identifying a good set of
hyperparameters. While recent approaches use Bayesian optimization to adaptively select …

Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

We study the stochastic contextual bandit problem, where the reward is generated from an
unknown function with additive noise. No assumption is made about the reward function …