15 citations · 94 across the 22 of their papers we have counts for
22 papers
Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning
Zebang Shen, Jiayuan Ye, Anmin Kang +2
Repeated parameter sharing in federated learning causes significant information leakage about private data, thus defeating its main purpose: data privacy. Mitigating the risk of th…
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
Zebang Shen, Hui Qian, Tongzhou Mu +1
Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications. In this p…
Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs
Zebang Shen, Zhenfu Wang
We extend the concept of self-consistency for the Fokker-Planck equation (FPE) to the more general McKean-Vlasov equation (MVE). While FPE describes the macroscopic behavior of par…
Self-Consistency of the Fokker-Planck Equation
Zebang Shen, Zhenfu Wang, Satyen Kale +3
The Fokker-Planck equation (FPE) is the partial differential equation that governs the density evolution of the Itô process and is of great importance to the literature of statisti…
Straggler-Resilient Personalized Federated Learning
Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani +2
Federated Learning is an emerging learning paradigm that allows training models from samples distributed across a large network of clients while respecting privacy and communicatio…
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems
Jiahao Xie, Chao Zhang, Zebang Shen +2
Minimax problems arise in a wide range of important applications including robust adversarial learning and Generative Adversarial Network (GAN) training. Recently, algorithms for m…
Federated Functional Gradient Boosting
Zebang Shen, Hamed Hassani, Satyen Kale +1
In this paper, we initiate a study of functional minimization in Federated Learning. First, in the semi-heterogeneous setting, when the marginal distributions of the feature vector…
From One to All: Learning to Match Heterogeneous and Partially Overlapped Graphs
Weijie Liu, Hui Qian, Chao Zhang +3
Recent years have witnessed a flurry of research activity in graph matching, which aims at finding the correspondence of nodes across two graphs and lies at the heart of many artif…
Sinkhorn Natural Gradient for Generative Models
Zebang Shen, Zhenfu Wang, Alejandro Ribeiro +1
We consider the problem of minimizing a functional over a parametric family of probability measures, where the parameterization is characterized via a push-forward structure. An im…
Sinkhorn Barycenter via Functional Gradient Descent
Zebang Shen, Zhenfu Wang, Alejandro Ribeiro +1
In this paper, we consider the problem of computing the barycenter of a set of probability distributions under the Sinkhorn divergence. This problem has recently found applications…
Safe Learning under Uncertain Objectives and Constraints
Mohammad Fereydounian, Zebang Shen, Aryan Mokhtari +2
In this paper, we consider non-convex optimization problems under \textit{unknown} yet safety-critical constraints. Such problems naturally arise in a variety of domains including…
A Decentralized Proximal Point-type Method for Saddle Point Problems
Weijie Liu, Aryan Mokhtari, Asuman Ozdaglar +3
In this paper, we focus on solving a class of constrained non-convex non-concave saddle point problems in a decentralized manner by a group of nodes in a network. Specifically, we…
Efficient Projection-Free Online Methods with Stochastic Recursive Gradient
Jiahao Xie, Zebang Shen, Chao Zhang +2
This paper focuses on projection-free methods for solving smooth Online Convex Optimization (OCO) problems. Existing projection-free methods either achieve suboptimal regret bounds…
Aggregated Gradient Langevin Dynamics
Chao Zhang, Jiahao Xie, Zebang Shen +3
In this paper, we explore a general Aggregated Gradient Langevin Dynamics framework (AGLD) for the Markov Chain Monte Carlo (MCMC) sampling. We investigate the nonasymptotic conver…
One Sample Stochastic Frank-Wolfe
Mingrui Zhang, Zebang Shen, Aryan Mokhtari +2
One of the beauties of the projected gradient descent method lies in its rather simple mechanism and yet stable behavior with inexact, stochastic gradients, which has led to its wi…
A Stochastic Trust Region Method for Non-convex Minimization
Zebang Shen, Pan Zhou, Cong Fang +1
We target the problem of finding a local minimum in non-convex finite-sum minimization. Towards this goal, we first prove that the trust region method with inexact gradient and Hes…
Stochastic Conditional Gradient++
Hamed Hassani, Amin Karbasi, Aryan Mokhtari +1
In this paper, we consider the general non-oblivious stochastic optimization where the underlying stochasticity may change during the optimization procedure and depends on the poin…
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen, Aryan Mokhtari, Tengfei Zhou +2
Recently, the decentralized optimization problem is attracting growing attention. Most existing methods are deterministic with high per-iteration cost and have a convergence rate q…
Accelerated Variance Reduced Block Coordinate Descent
Zebang Shen, Hui Qian, Chao Zhang +1
Algorithms with fast convergence, small number of data access, and low per-iteration complexity are particularly favorable in the big data era, due to the demand for obtaining \emp…
Accelerated Stochastic ADMM with Variance Reduction
Chao Zhang, Zebang Shen, Hui Qian +3
Alternating Direction Method of Multipliers (ADMM) is a popular method for solving large-scale Machine Learning problems. Stochastic ADMM was proposed to reduce the per iteration c…
Riemannian Tensor Completion with Side Information
Tengfei Zhou, Hui Qian, Zebang Shen +1
By restricting the iterate on a nonlinear manifold, the recently proposed Riemannian optimization methods prove to be both efficient and effective in low rank tensor completion pro…
Kinetic Energy Plus Penalty Functions for Sparse Estimation
Zhihua Zhang, Shibo Zhao, Zebang Shen +1
In this paper we propose and study a family of sparsity-inducing penalty functions. Since the penalty functions are related to the kinetic energy in special relativity, we call the…