362 citations · 2.5k across the 76 of their papers we have counts for
11 papers · 1 filter
Conditional Gradient Methods
Gábor Braun, Alejandro Carderera, Cyrille W. Combettes +4
The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithm…
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
Mao Ye, Ruichen Jiang, Haoxiang Wang +6
One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and…
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…
A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem
Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari +1
In this paper, we study a class of bilevel optimization problems, also known as simple bilevel optimization, where we minimize a smooth objective function over the optimal solution…
Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models
Qiujiang Jin, Tongzheng Ren, Nhat Ho +1
The gradient descent (GD) method has been used widely to solve parameter estimation in generalized linear models (GLMs), a generalization of linear models when the link function ca…
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning
Liam Collins, Hamed Hassani, Aryan Mokhtari +1
The Federated Averaging (FedAvg) algorithm, which consists of alternating between a few local stochastic gradient updates at client nodes, followed by a model averaging update at t…
Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach
Mohammad Fereydounian, Aryan Mokhtari, Ramtin Pedarsani +1
In this work, we focus on solving a decentralized consensus problem in a private manner. Specifically, we consider a setting in which a group of nodes, connected through a network,…
Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood
Qiujiang Jin, Alec Koppel, Ketan Rajawat +1
Non-asymptotic analysis of quasi-Newton methods have gained traction recently. In particular, several works have established a non-asymptotic superlinear rate of $\mathcal{O}((1/\s…
Generalized Optimistic Methods for Convex-Concave Saddle Point Problems
Ruichen Jiang, Aryan Mokhtari
The optimistic gradient method has seen increasing popularity for solving convex-concave saddle point problems. To analyze its iteration complexity, a recent work [arXiv:1906.01115…
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance
Matthew Faw, Isidoros Tziotis, Constantine Caramanis +3
We study convergence rates of AdaGrad-Norm as an exemplar of adaptive stochastic gradient methods (SGD), where the step sizes change based on observed stochastic gradients, for min…
MAML and ANIL Provably Learn Representations
Liam Collins, Aryan Mokhtari, Sewoong Oh +1
Recent empirical evidence has driven conventional wisdom to believe that gradient-based meta-learning (GBML) methods perform well at few-shot learning because they learn an express…