Stein's method compares probability distributions through the study of a class of linear
operators called Stein operators. While mainly studied in probability and used to underpin …

The empirical success of reinforcement learning (RL) in contact-rich manipulation leaves
much to be understood from a model-based perspective, where the key difficulties are often …

When legged robots perform complex tasks in unstructured environments, falls are
inevitable due to unknown external disturbances. However, current research mainly focuses …

We consider the problem of sufficient dimension reduction (SDR) for multi-index models.
The estimators of the central mean subspace in prior works either have slow (non …

While machine learning techniques have been successfully applied in several fields, the
black-box nature of the models presents challenges for interpreting and explaining the …

We consider estimating a high-dimensional vector from non-linear measurements where the
unknown vector is represented by a generative model $ G:\mathbb {R}^ k\rightarrow\mathbb …

In this article, we leverage over-parameterization to design regularization-free algorithms for
the high-dimensional single index model and provide theoretical guarantees for the induced …

Out of the rich family of generalized linear bandits, perhaps the most well studied ones are
logistic bandits that are used in problems with binary rewards: for instance, when the learner …

Existing nonconvex statistical optimization theory and methods crucially rely on the correct
specification of the underlying “true” statistical models. To address this issue, we take a first …

In this work, we focus on high-dimensional single index models with non-Gaussian sensing
vectors and generative priors. More specifically, our goal is to estimate the underlying signal …