228 citations · 868 across the 43 of their papers we have counts for
44 papers
Extragradient Type Methods for Riemannian Variational Inequality Problems
Zihao Hu, Guanghui Wang, Xi Wang +3
Riemannian convex optimization and minimax optimization have recently drawn considerable attention. Their appeal lies in their capacity to adeptly manage the non-convexity of the o…
On the Robustness of Epoch-Greedy in Multi-Agent Contextual Bandit Mechanisms
Yinglun Xu, Bhuvesh Kumar, Jacob Abernethy
Efficient learning in multi-armed bandit mechanisms such as pay-per-click (PPC) auctions typically involves three challenges: 1) inducing truthful bidding behavior (incentives), 2)…
Randomized Quantization is All You Need for Differential Privacy in Federated Learning
Yeojoon Youn, Zihao Hu, Juba Ziani +1
Federated learning (FL) is a common and practical framework for learning a machine model in a decentralized fashion. A primary motivation behind this decentralized approach is data…
A Mechanism for Sample-Efficient In-Context Learning for Sparse Retrieval Tasks
Jacob Abernethy, Alekh Agarwal, Teodor V. Marinov +1
We study the phenomenon of \textit{in-context learning} (ICL) exhibited by large language models, where they can adapt to a new learning task, given a handful of labeled examples,…
Riemannian Projection-free Online Learning
Zihao Hu, Guanghui Wang, Jacob Abernethy
The projection operation is a critical component in a wide range of optimization algorithms, such as online gradient descent (OGD), for enforcing constraints and achieving optimal…
Minimizing Dynamic Regret on Geodesic Metric Spaces
Zihao Hu, Guanghui Wang, Jacob Abernethy
In this paper, we consider the sequential decision problem where the goal is to minimize the general dynamic regret on a complete Riemannian manifold. The task of offline optimizat…