This article surveys reinforcement learning from the perspective of optimization and control,
with a focus on continuous control applications. It reviews the general formulation …

Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many
signal processing and machine learning (ML) applications. It is used for solving optimization …

Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but
as LMs grow in size, backpropagation requires a prohibitively large amount of memory …

A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

The goal of a decision-based adversarial attack on a trained model is to generate
adversarial examples based solely on observing output labels returned by the targeted …

In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …

Face recognition has obtained remarkable progress in recent years due to the great
improvement of deep convolutional neural networks (CNNs). However, deep CNNs are …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

Direct policy gradient methods for reinforcement learning and continuous control problems
are a popular approach for a variety of reasons: 1) they are easy to implement without …

We present new algorithms for optimizing non-smooth, non-convex stochastic objectives
based on a novel analysis technique. This improves the current best-known complexity for …