7 papers
Non-Stationary Bandit Convex Optimization: An Optimal Algorithm with Two-Point Feedback
Chang He, Bo Jiang, Shuzhong Zhang
This paper studies bandit convex optimization in non-stationary environments with two-point feedback, using dynamic regret as the performance measure. We propose an algorithm based…
New Results on the Polyak Stepsize: Tight Convergence Analysis and Universal Function Classes
Chang He, Wenzhi Gao, Bo Jiang +2
In this paper, we revisit a classical adaptive stepsize strategy for gradient descent: the Polyak stepsize (PolyakGD), originally proposed in Polyak (1969). We study the convergenc…
Adaptive Single-Loop Methods for Stochastic Minimax Optimization on Riemannian Manifolds
Hongye Wang, Chang He, Bo Jiang
Stochastic minimax optimization on Riemannian manifolds has recently attracted significant attention due to its broad range of applications, such as robust training of neural netwo…
On Approximation Algorithms for Commutative Quaternion Polynomial Optimization
Chang He, Bo Jiang, Hongye Wang +1
Quaternion optimization has attracted significant interest due to its broad applications, including color face recognition, video compression, and signal processing. Despite the gr…
The Second-Order Tâtonnement: Decentralized Interior-Point Methods for Market Equilibrium
Chuwen Zhang, Chang He, Bo Jiang +1
The tâtonnement process and Smale's process are two classical approaches to compute market equilibrium in exchange economies. While the tâtonnement process can be seen as a first…
Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach
Hongye Wang, Zhaoye Pan, Chang He +2
Federated learning (FL) has emerged as a powerful paradigm for collaborative model training across distributed clients while preserving data privacy. However, existing FL algorithm…