16 citations · 38 across the 13 of their papers we have counts for
6 papers · 1 filter
Bilevel Optimization: Convergence Analysis and Enhanced Design
Kaiyi Ji, Junjie Yang, Yingbin Liang
Bilevel optimization has arisen as a powerful tool for many machine learning problems such as meta-learning, hyperparameter optimization, and reinforcement learning. In this paper,…
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback
Yan Zhang, Yi Zhou, Kaiyi Ji +1
Zeroth-order optimization (ZO) typically relies on two-point feedback to estimate the unknown gradient of the objective function. Nevertheless, two-point feedback can not be used f…
Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
Kaiyi Ji, Jason D. Lee, Yingbin Liang +1
Although model-agnostic meta-learning (MAML) is a very successful algorithm in meta-learning practice, it can have high computational cost because it updates all model parameters o…
Robust Stochastic Bandit Algorithms under Probabilistic Unbounded Adversarial Attack
Ziwei Guan, Kaiyi Ji, Donald J Bucci +4
The multi-armed bandit formalism has been extensively studied under various attack models, in which an adversary can modify the reward revealed to the player. Previous studies focu…
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization
Yi Zhou, Zhe Wang, Kaiyi Ji +2
Various types of parameter restart schemes have been proposed for accelerated gradient algorithms to facilitate their practical convergence in convex optimization. However, the con…
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning
Kaiyi Ji, Junjie Yang, Yingbin Liang
As a popular meta-learning approach, the model-agnostic meta-learning (MAML) algorithm has been widely used due to its simplicity and effectiveness. However, the convergence of the…