Aryan Mokhtari

research

∙ 06/17/2022

Generalized Frank-Wolfe Algorithm for Bilevel Optimization

In this paper, we study a class of bilevel optimization problems, also k...

Ruichen Jiang, et al. ∙

research

∙ 02/19/2022

Generalized Optimistic Methods for Convex-Concave Saddle Point Problems

The optimistic gradient method has seen increasing popularity as an effi...

Ruichen Jiang, et al. ∙

research

∙ 02/11/2022

The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance

We study convergence rates of AdaGrad-Norm as an exemplar of adaptive st...

Matthew Faw, et al. ∙

research

∙ 02/07/2022

MAML and ANIL Provably Learn Representations

Recent empirical evidence has driven conventional wisdom to believe that...

Liam Collins, et al. ∙

research

∙ 06/10/2021

Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach

In this paper, we study the application of quasi-Newton methods for solv...

Qiujiang Jin, et al. ∙

research

∙ 02/07/2021

Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks

In this paper, we study the generalization properties of Model-Agnostic ...

Alireza Fallah, et al. ∙

research

∙ 10/27/2020

Why Does MAML Outperform ERM? An Optimization Perspective

Model-Agnostic Meta-Learning (MAML) has demonstrated widespread success ...

Liam Collins, et al. ∙

research

∙ 07/02/2020

Federated Learning with Compression: Unified Analysis and Sharp Guarantees

In federated learning, communication cost is often a critical bottleneck...

Farzin Haddadpour, et al. ∙

research

∙ 06/07/2020

Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting

Mobile network operators store an enormous amount of information like lo...

Aryan Mokhtari, et al. ∙

research

∙ 03/30/2020

Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods

In this paper, we study the non-asymptotic superlinear convergence rate ...

Qiujiang Jin, et al. ∙

research

∙ 02/23/2020

Quantized Push-sum for Gossip and Decentralized Optimization over Directed Graphs

We consider a decentralized stochastic learning problem where data point...

Hossein Taheri, et al. ∙

research

∙ 02/19/2020

Personalized Federated Learning: A Meta-Learning Approach

The goal of federated learning is to design algorithms in which several ...

Alireza Fallah, et al. ∙

research

∙ 02/12/2020

Provably Convergent Policy Gradient Methods for Model-Agnostic Meta-Reinforcement Learning

We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcemen...

Alireza Fallah, et al. ∙

research

∙ 02/12/2020

Distribution-Agnostic Model-Agnostic Meta-Learning

The Model-Agnostic Meta-Learning (MAML) algorithm <cit.> has been celebr...

Liam Collins, et al. ∙

research

∙ 10/31/2019

A Decentralized Proximal Point-type Method for Saddle Point Problems

In this paper, we focus on solving a class of constrained non-convex non...

Weijie Liu, et al. ∙

research

∙ 10/10/2019

One Sample Stochastic Frank-Wolfe

One of the beauties of the projected gradient descent method lies in its...

Mingrui Zhang, et al. ∙

research

∙ 09/28/2019

FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization

Federated learning is a new distributed machine learning approach, where...

Amirhossein Reisizadeh, et al. ∙

research

∙ 08/27/2019

On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms

In this paper, we study the convergence theory of a class of gradient-ba...

Alireza Fallah, et al. ∙

research

∙ 07/24/2019

Robust and Communication-Efficient Collaborative Learning

We consider a decentralized learning problem, where a set of computing n...

Amirhossein Reisizadeh, et al. ∙

research

∙ 06/03/2019

Proximal Point Approximations Achieving a Convergence Rate of O(1/k) for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods

In this paper we analyze the iteration complexity of the optimistic grad...

Aryan Mokhtari, et al. ∙

research

∙ 02/19/2019

Stochastic Conditional Gradient++

In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the fi...

Hamed Hassani, et al. ∙

research

∙ 02/17/2019

Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization

How can we efficiently mitigate the overhead of gradient communications ...

Mingrui Zhang, et al. ∙

research

∙ 01/24/2019

A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach

We consider solving convex-concave saddle point problems. We focus on tw...

Aryan Mokhtari, et al. ∙

research

∙ 10/26/2018

Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy

In this paper, we propose a Distributed Accumulated Newton Conjugate gra...

Majid Jahani, et al. ∙

research

∙ 09/06/2018

Escaping Saddle Points in Constrained Optimization

In this paper, we focus on escaping from saddle points in smooth nonconv...

Aryan Mokhtari, et al. ∙

research

∙ 06/29/2018

Quantized Decentralized Consensus Optimization

We consider the problem of decentralized consensus optimization, where t...

Amirhossein Reisizadeh, et al. ∙

research

∙ 05/25/2018

Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication

Recently, the decentralized optimization problem is attracting growing a...

Zebang Shen, et al. ∙

research

∙ 05/01/2018

Direct Runge-Kutta Discretization Achieves Acceleration

We study gradient-based optimization methods obtained by directly discre...

Jingzhao Zhang, et al. ∙

research

∙ 04/24/2018

Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

This paper considers stochastic optimization problems for a large class ...

Aryan Mokhtari, et al. ∙

research

∙ 05/22/2017

Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method

We consider large scale empirical risk minimization (ERM) problems, wher...

Mark Eisen, et al. ∙

research

∙ 10/07/2016

Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation

Existing approaches to resource allocation for nowadays stochastic netwo...

Tianyi Chen, et al. ∙

research

∙ 06/15/2016

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning

We consider learning problems over training sets in which both, the numb...

Aryan Mokhtari, et al. ∙

research

∙ 01/29/2014

RES: Regularized Stochastic BFGS Algorithm

RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-S...

Aryan Mokhtari, et al. ∙

Aryan Mokhtari

Featured Co-authors