Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

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 …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

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 …

We introduce contextual stochastic bilevel optimization (CSBO)--a stochastic bilevel
optimization framework with the lower-level problem minimizing an expectation conditioned …

In this paper, we propose a novel Hessian inverse free Fully Single Loop Algorithm (FSLA)
for bilevel optimization problems. Classic algorithms for bilevel optimization admit a double …

Most object detection methods require huge amounts of annotated data and can detect only
the categories that appear in the training set. However, in reality acquiring massive …

Learning adaptable policies is crucial for robots to operate autonomously in our complex
and quickly changing world. In this work, we present a new meta-learning method that …

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