93 citations · 269 across the 50 of their papers we have counts for
59 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…
Decentralized Online Riemannian Optimization Beyond Hadamard Manifolds
Emre Sahinoglu, Shahin Shahrampour
We study decentralized online Riemannian optimization over manifolds with possibly positive curvature, going beyond the Hadamard manifold setting. Decentralized optimization techni…
Local Linear Convergence of Infeasible Optimization with Orthogonal Constraints
Youbang Sun, Shixiang Chen, Alfredo Garcia +1
Many classical and modern machine learning algorithms require solving optimization tasks under orthogonality constraints. Solving these tasks with feasible methods requires a gradi…
Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization
Emre Sahinoglu, Shahin Shahrampour
We investigate the finite-time analysis of finding ()-stationary points for nonsmooth nonconvex objectives in decentralized stochastic optimization. A set of agents aim at min…
Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization
Youbang Sun, Tao Liu, P. R. Kumar +1
This work focuses on the entropy-regularized independent natural policy gradient (NPG) algorithm in multi-agent reinforcement learning. In this work, agents are assumed to have acc…
Retraction-Free Decentralized Non-convex Optimization with Orthogonal Constraints
Youbang Sun, Shixiang Chen, Alfredo Garcia +1
In this paper, we investigate decentralized non-convex optimization with orthogonal constraints. Conventional algorithms for this setting require either manifold retractions or oth…
Tracking Dynamic Gaussian Density with a Theoretically Optimal Sliding Window Approach
Yinsong Wang, Yu Ding, Shahin Shahrampour
Dynamic density estimation is ubiquitous in many applications, including computer vision and signal processing. One popular method to tackle this problem is the "sliding window" ke…
Regret Analysis of Policy Optimization over Submanifolds for Linearly Constrained Online LQG
Ting-Jui Chang, Shahin Shahrampour
Recent advancement in online optimization and control has provided novel tools to study online linear quadratic regulator (LQR) problems, where cost matrices are time-varying and u…
Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games
Youbang Sun, Tao Liu, Ruida Zhou +2
This work studies an independent natural policy gradient (NPG) algorithm for the multi-agent reinforcement learning problem in Markov potential games. It is shown that, under mild…
Regret Analysis of Distributed Online Control for LTI Systems with Adversarial Disturbances
Ting-Jui Chang, Shahin Shahrampour
This paper addresses the distributed online control problem over a network of linear time-invariant (LTI) systems (with possibly unknown dynamics) in the presence of adversarial pe…
Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems
Ting-Jui Chang, Sapana Chaudhary, Dileep Kalathil +1
This paper addresses safe distributed online optimization over an unknown set of linear safety constraints. A network of agents aims at jointly minimizing a global, time-varying fu…
TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Modality
Yinsong Wang, Shahin Shahrampour
This paper addresses a cross-modal learning framework, where the objective is to enhance the performance of supervised learning in the primary modality using an unlabeled, unpaired…
On the Stability Analysis of Open Federated Learning Systems
Youbang Sun, Heshan Fernando, Tianyi Chen +1
We consider the open federated learning (FL) systems, where clients may join and/or leave the system during the FL process. Given the variability of the number of present clients,…
Distributed Online System Identification for LTI Systems Using Reverse Experience Replay
Ting-Jui Chang, Shahin Shahrampour
Identification of linear time-invariant (LTI) systems plays an important role in control and reinforcement learning. Both asymptotic and finite-time offline system identification a…
TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic Density Estimation
Yinsong Wang, Yu Ding, Shahin Shahrampour
Real-time density estimation is ubiquitous in many applications, including computer vision and signal processing. Kernel density estimation is arguably one of the most commonly use…
A Sparse Expansion For Deep Gaussian Processes
Liang Ding, Rui Tuo, Shahin Shahrampour
In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models c…
On Centralized and Distributed Mirror Descent: Convergence Analysis Using Quadratic Constraints
Youbang Sun, Mahyar Fazlyab, Shahin Shahrampour
Mirror descent (MD) is a powerful first-order optimization technique that subsumes several optimization algorithms including gradient descent (GD). In this work, we develop a semi-…
Regret Analysis of Distributed Online LQR Control for Unknown LTI Systems
Ting-Jui Chang, Shahin Shahrampour
Online optimization has recently opened avenues to study optimal control for time-varying cost functions that are unknown in advance. Inspired by this line of research, we study th…
Decentralized Riemannian Gradient Descent on the Stiefel Manifold
Shixiang Chen, Alfredo Garcia, Mingyi Hong +1
We consider a distributed non-convex optimization where a network of agents aims at minimizing a global function over the Stiefel manifold. The global function is represented as a…