Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …

Standard federated optimization methods successfully apply to stochastic problems with
single-level structure. However, many contemporary ML problems-including adversarial …

Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …

Bilevel optimization have gained growing interests, with numerous applications found in
meta learning, minimax games, reinforcement learning, and nested composition …

In recent years, domains such as natural language processing and image recognition have
popularized the paradigm of using large datasets to pretrain representations that can be …

Imitation learning (IL) aims to mimic the behavior of an expert policy in a sequential decision-
making problem given only demonstrations. In this paper, we focus on understanding the …

This survey is focused on certain sequential decision-making problems that involve
optimizing over probability functions. We discuss the relevance of these problems for …

To act in the world, robots rely on a representation of salient task aspects: for example, to
carry a coffee mug, a robot may consider movement efficiency or mug orientation in its …

Bilevel optimization has recently attracted growing interests due to its wide applications in
modern machine learning problems. Although recent studies have characterized the …

The aim in imitation learning is to learn effective policies by utilizing near-optimal expert
demonstrations. However, high-quality demonstrations from human experts can be …