To address this gap, we study deterministic/stochastic versions of SAM with practical configurations (i.e., constant

All models discussed in the paper use ReLU non-linearity.

The supplemental material is organized as follows. Section A provides the results of all the additional synthetic experiments and real data results for various kernel-based methods and the detailed settings. Section B describes the algorithm details we use in Section A. In Section C, we provide some useful lemmas and all the technical proofs of the theoretical results in the main text. In this section, we provide more experiment results, including KRR (Section A.1), KQR for various Section A.7. A.1 Kernel ridge regression For the squared loss, we consider the following two examples. TIRW estimator still performs significantly better. A.2 Kernel quantile regression For the check loss, we consider the following two examples.

This paper investigates the asymptotic distribution of the maximum-likelihood estimate (MLE) in multinomial logistic models in the high-dimensional regime where dimension and sample size are of the same order.

What are the right computational primitives for exploration?

Further, we are interested in algorithms that enjoy sample efficiency while leveraging (value) function approximation.

We study the problem of online adaptive policy selection for nonlinear time-varying discrete-time dynamical systems.