Aryan Mokhtari

research

∙ 08/15/2023

Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem

In this paper, we study a class of stochastic bilevel optimization probl...

0 Jincheng Cao, et al. ∙

research

∙ 06/27/2023

Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate

Non-asymptotic convergence analysis of quasi-Newton methods has gained a...

0 Zhan Gao, et al. ∙

research

∙ 06/03/2023

Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization

In this paper, we propose an accelerated quasi-Newton proximal extragrad...

0 Ruichen Jiang, et al. ∙

research

∙ 03/20/2023

Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing and Neural Networks with Quadratic Activations

Pruning schemes have been widely used in practice to reduce the complexi...

0 Nived Rajaraman, et al. ∙

research

∙ 02/16/2023

Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence

Quasi-Newton algorithms are among the most popular iterative methods for...

0 Ruichen Jiang, et al. ∙

research

∙ 02/15/2023

InfoNCE Loss Provably Learns Cluster-Preserving Representations

The goal of contrasting learning is to learn a representation that prese...

0 Advait Parulekar, et al. ∙

research

∙ 01/11/2023

Network Adaptive Federated Learning: Congestion and Lossy Compression

In order to achieve the dual goals of privacy and learning across distri...

0 Parikshit Hegde, et al. ∙

research

∙ 09/02/2022

Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems

One of the key challenges of learning an online recommendation model is ...

15 Mao Ye, et al. ∙

research

∙ 06/17/2022

Generalized Frank-Wolfe Algorithm for Bilevel Optimization

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

0 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...

0 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...

14 Matthew Faw, et al. ∙

research

∙ 02/07/2022

MAML and ANIL Provably Learn Representations

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

0 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...

0 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 ...

0 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 ...

0 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...

0 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...

23 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 ...

0 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...

0 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 ...

4 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...

1 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...

21 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...

0 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...

7 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...

18 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...

0 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...

0 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...

0 Aryan Mokhtari, et al. ∙

research

∙ 02/19/2019

Stochastic Conditional Gradient++

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

0 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 ...

0 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...

0 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...

0 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...

0 Aryan Mokhtari, et al. ∙

research

∙ 06/29/2018

Quantized Decentralized Consensus Optimization

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

0 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...

0 Zebang Shen, et al. ∙

research

∙ 05/01/2018

Direct Runge-Kutta Discretization Achieves Acceleration

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

0 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 ...

0 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...

0 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...

0 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...

0 Aryan Mokhtari, et al. ∙

research

∙ 01/29/2014

RES: Regularized Stochastic BFGS Algorithm

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

0 Aryan Mokhtari, et al. ∙

Aryan Mokhtari

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