14 citations · 46 across the 34 of their papers we have counts for
52 papers
Curvature-Independent Regret Bounds for Distributed Online Optimization on Hadamard Manifolds
Zhanyuan Cai, Emre Sahinoglu, Shahin Shahrampour
This work addresses decentralized online Riemannian optimization on Hadamard manifolds. Prior work under geodesic convexity (g-convexity) may require curvature information in the o…
Decentralized Online Riemannian Optimization for Strongly Geodesically Convex Functions
Zhanyuan Cai, Emre Sahinoglu, Shahin Shahrampour
We study decentralized online optimization for strongly geodesically convex (strongly g-convex) losses on Riemannian manifolds with bounded sectional curvature, including positivel…
Theoretical Analysis of Measure Consistency Regularization for Partially Observed Data
Yinsong Wang, Shahin Shahrampour
The problem of corrupted data, missing features, or missing modalities continues to plague the modern machine learning landscape. To address this issue, a class of regularization m…
Finite-Time Analysis of Stochastic Nonconvex Nonsmooth Optimization on the Riemannian Manifolds
Emre Sahinoglu, Youbang Sun, Shahin Shahrampour
This work addresses the finite-time analysis of nonsmooth nonconvex stochastic optimization under Riemannian manifold constraints. We adapt the notion of Goldstein stationarity to…
ADARL: Adaptive Low-Rank Structures for Robust Policy Learning under Uncertainty
Chenliang Li, Junyu Leng, Jiaxiang Li +4
Robust reinforcement learning (Robust RL) seeks to handle epistemic uncertainty in environment dynamics, but existing approaches often rely on nested min--max optimization, which i…
Online Optimization on Hadamard Manifolds: Curvature Independent Regret Bounds on Horospherically Convex Objectives
Emre Sahinoglu, Shahin Shahrampour
We study online Riemannian optimization on Hadamard manifolds under the framework of horospherical convexity (h-convexity). Prior work mostly relies on the geodesic convexity (g-co…