Online capacity estimation of lithium-ion batteries plays an important role in battery
management systems. Accurate estimation of the current capacity of the battery is helpful for …

We review distributionally robust optimization (DRO), a principled approach for constructing
statistical estimators that hedge against the impact of deviations in the expected loss …

Algorithmic recourse aims to recommend an informative feedback to overturn an
unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a …

Counterfactual explanations are attracting significant attention due to the flourishing
applications of machine learning models in consequential domains. A counterfactual plan …

Modern kernel-based two-sample tests have shown great success in distinguishing
complex, high-dimensional distributions by learning appropriate kernels (or, as a special …

We study the problem of transfer learning, observing that previous efforts to understand its
information-theoretic limits do not fully exploit the geometric structure of the source and …

Algorithmic recourse emerges as a prominent technique to promote the explainability,
transparency and hence ethics of machine learning models. Existing algorithmic recourse …

In this paper we develop a Stochastic Gradient Langevin Dynamics (SGLD) algorithm
tailored for solving a certain class of non-convex distributionally robust optimisation …

Distributionally robust optimization has emerged as an attractive way to train robust machine
learning models, capturing data uncertainty and distribution shifts. Recent statistical …

Empirical risk minimization often fails to provide robustness against adversarial attacks in
test data, causing poor out-of-sample performance. Adversarially robust optimization (ARO) …