7 citations · 7 across the 6 of their papers we have counts for
6 papers
Riemannian Optimistic Algorithms
Xi Wang, Deming Yuan, Yiguang Hong +3
In this paper, we consider Riemannian online convex optimization with dynamic regret. First, we propose two novel algorithms, namely the Riemannian Online Optimistic Gradient Desce…
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…
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…
Adaptive Oracle-Efficient Online Learning
Guanghui Wang, Zihao Hu, Vidya Muthukumar +1
The classical algorithms for online learning and decision-making have the benefit of achieving the optimal performance guarantees, but suffer from computational complexity limitati…
Supervised Hashing based on Energy Minimization
Zihao Hu, Xiyi Luo, Hongtao Lu +1
Recently, supervised hashing methods have attracted much attention since they can optimize retrieval speed and storage cost while preserving semantic information. Because hashing c…