Fréchet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables.

In this work, we investigate an alternative setting for tuning regularization parameters, namely data-driven algorithm design, following the previous line of work by Balcan et al. [

The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems.

Our framework may be thought of as a generalization of the synthetic control and synthetic interventions frameworks, where data is collected via an adaptive intervention assignment policy.

Our framework may be thought of as a generalization of the synthetic control and synthetic interventions frameworks, where data is collected via an adaptive intervention assignment policy.

Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process.

We present a novel language-driven ordering alignment method for ordinal classification.

In many scientific applications, we observe a system in different conditions in which its components may change, rather than in isolation.