Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …

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 …

Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of
expensive black box functions, which use introspective Bayesian models of the function to …

This paper considers the optimal adaptive allocation of measurement effort for identifying the
best among a finite set of options or designs. An experimenter sequentially chooses designs …

Top two algorithms arose as an adaptation of Thompson sampling to best arm identification
in multi-armed bandit models for parametric families of arms. They select the next arm to …

We investigate an active\emph {pure-exploration} setting, that includes\emph {best-arm
identification}, in the context of\emph {linear stochastic bandits}. While asymptotically optimal …

There is significant interest in improving the performance of batteries to increase
electrification of transportation and aviation. Recently, performance improvements have …

We investigate and provide new insights on the sampling rule called Top-Two Thompson
Sampling (TTTS). In particular, we justify its use for fixed-confidence best-arm identification …

We explore a new model of bandit experiments where a potentially nonstationary sequence
of contexts influences arms' performance. Context-unaware algorithms risk confounding …