Aryan Mokhtari

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...
06/10/2021 ∙ by Qiujiang Jin, et al. ∙ 0 ∙ share

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

In this paper, we study the generalization properties of Model-Agnostic ...
02/07/2021 ∙ by Alireza Fallah, et al. ∙ 0 ∙ share

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Why Does MAML Outperform ERM? An Optimization Perspective

Model-Agnostic Meta-Learning (MAML) has demonstrated widespread success ...
10/27/2020 ∙ by Liam Collins, et al. ∙ 0 ∙ share

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Federated Learning with Compression: Unified Analysis and Sharp Guarantees

In federated learning, communication cost is often a critical bottleneck...
07/02/2020 ∙ by Farzin Haddadpour, et al. ∙ 0 ∙ share

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

Mobile network operators store an enormous amount of information like lo...
06/07/2020 ∙ by Aryan Mokhtari, et al. ∙ 23 ∙ share

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Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods

In this paper, we study the non-asymptotic superlinear convergence rate ...
03/30/2020 ∙ by Qiujiang Jin, et al. ∙ 0 ∙ share

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

We consider a decentralized stochastic learning problem where data point...
02/23/2020 ∙ by Hossein Taheri, et al. ∙ 0 ∙ share

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Personalized Federated Learning: A Meta-Learning Approach

The goal of federated learning is to design algorithms in which several ...
02/19/2020 ∙ by Alireza Fallah, et al. ∙ 4 ∙ share

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

We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcemen...
02/12/2020 ∙ by Alireza Fallah, et al. ∙ 1 ∙ share

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Distribution-Agnostic Model-Agnostic Meta-Learning

The Model-Agnostic Meta-Learning (MAML) algorithm <cit.> has been celebr...
02/12/2020 ∙ by Liam Collins, et al. ∙ 21 ∙ share

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A Decentralized Proximal Point-type Method for Saddle Point Problems

In this paper, we focus on solving a class of constrained non-convex non...
10/31/2019 ∙ by Weijie Liu, et al. ∙ 0 ∙ share

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One Sample Stochastic Frank-Wolfe

One of the beauties of the projected gradient descent method lies in its...
10/10/2019 ∙ by Mingrui Zhang, et al. ∙ 7 ∙ share

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

Federated learning is a new distributed machine learning approach, where...
09/28/2019 ∙ by Amirhossein Reisizadeh, et al. ∙ 18 ∙ share

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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...
08/27/2019 ∙ by Alireza Fallah, et al. ∙ 0 ∙ share

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Robust and Communication-Efficient Collaborative Learning

We consider a decentralized learning problem, where a set of computing n...
07/24/2019 ∙ by Amirhossein Reisizadeh, et al. ∙ 0 ∙ share

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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...
06/03/2019 ∙ by Aryan Mokhtari, et al. ∙ 0 ∙ share

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Stochastic Conditional Gradient++

In this paper, we develop Stochastic Continuous Greedy++ (SCG++), the fi...
02/19/2019 ∙ by Hamed Hassani, et al. ∙ 0 ∙ share

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Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization

How can we efficiently mitigate the overhead of gradient communications ...
02/17/2019 ∙ by Mingrui Zhang, et al. ∙ 0 ∙ share

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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...
01/24/2019 ∙ by Aryan Mokhtari, et al. ∙ 0 ∙ share

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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...
10/26/2018 ∙ by Majid Jahani, et al. ∙ 0 ∙ share

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Escaping Saddle Points in Constrained Optimization

In this paper, we focus on escaping from saddle points in smooth nonconv...
09/06/2018 ∙ by Aryan Mokhtari, et al. ∙ 0 ∙ share

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Quantized Decentralized Consensus Optimization

We consider the problem of decentralized consensus optimization, where t...
06/29/2018 ∙ by Amirhossein Reisizadeh, et al. ∙ 0 ∙ share

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

Recently, the decentralized optimization problem is attracting growing a...
05/25/2018 ∙ by Zebang Shen, et al. ∙ 0 ∙ share

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Direct Runge-Kutta Discretization Achieves Acceleration

We study gradient-based optimization methods obtained by directly discre...
05/01/2018 ∙ by Jingzhao Zhang, et al. ∙ 0 ∙ share

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Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization

This paper considers stochastic optimization problems for a large class ...
04/24/2018 ∙ by Aryan Mokhtari, et al. ∙ 0 ∙ share

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Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method

We consider large scale empirical risk minimization (ERM) problems, wher...
05/22/2017 ∙ by Mark Eisen, et al. ∙ 0 ∙ share

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Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation

Existing approaches to resource allocation for nowadays stochastic netwo...
10/07/2016 ∙ by Tianyi Chen, et al. ∙ 0 ∙ share

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

We consider learning problems over training sets in which both, the numb...
06/15/2016 ∙ by Aryan Mokhtari, et al. ∙ 0 ∙ share

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RES: Regularized Stochastic BFGS Algorithm

RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-S...
01/29/2014 ∙ by Aryan Mokhtari, et al. ∙ 0 ∙ share

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Aryan Mokhtari

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