Stochastic nested optimization, including stochastic compositional, min-max, and bilevel
optimization, is gaining popularity in many machine learning applications. While the three …

Bilevel optimization has been widely applied in many important machine learning
applications such as hyperparameter optimization and meta-learning. Recently, several …

Adversarial training (AT) is a widely recognized defense mechanism to gain the robustness
of deep neural networks against adversarial attacks. It is built on min-max optimization …

This paper proposes a new algorithm--the\underline {S} ingle-timescale Do\underline {u} ble-
momentum\underline {St} ochastic\underline {A} pprox\underline {i} matio\underline …

Recently, bilevel optimization (BLO) has taken center stage in some very exciting
developments in the area of signal processing (SP) and machine learning (ML). Roughly …

Deep learning has achieved remarkable success in many machine learning tasks such as
image classification, speech recognition, and game playing. However, these breakthroughs …

Stochastic nested optimization, including stochastic compositional, min-max and bilevel
optimization, is gaining popularity in many machine learning applications. While the three …

Stochastic approximation (SA) with multiple coupled sequences has found broad
applications in machine learning such as bilevel learning and reinforcement learning (RL) …

Bi-level optimization, where the objective function depends on the solution of an inner
optimization problem, provides a flexible framework for solving a rich class of problems such …

In recent years, there has been a surge of machine learning applications developed with
hierarchical structure, which can be approached from Bi-Level Optimization (BLO) …