Publications (34)
DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers
Aryan Mokhtari, Wei Shi, Qing Ling +1
Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE
Jingzhao Zhang, César A. Uribe, Aryan Mokhtari +1
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Zebang Shen, Aryan Mokhtari, Tengfei Zhou +2
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy
Aryan Mokhtari, Alejandro Ribeiro
Doubly Random Parallel Stochastic Methods for Large Scale Learning
Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
Decentralized Quasi-Newton Methods
Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate
Saeed Soori, Konstantin Mischenko, Aryan Mokhtari +2
A Decentralized Quasi-Newton Method for Dual Formulations of Consensus Optimization
Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro
RES: Regularized Stochastic BFGS Algorithm
Aryan Mokhtari, Alejandro Ribeiro
DSA: Decentralized Double Stochastic Averaging Gradient Algorithm
Aryan Mokhtari, Alejandro Ribeiro
A Quasi-Newton Method for Large Scale Support Vector Machines
Aryan Mokhtari, Alejandro Ribeiro
A Newton-Based Method for Nonconvex Optimization with Fast Evasion of Saddle Points
Santiago Paternain, Aryan Mokhtari, Alejandro Ribeiro
Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization
Aryan Mokhtari, Alejandro Ribeiro
Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation
Tianyi Chen, Aryan Mokhtari, Xin Wang +2
Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization
Andrea Simonetto, Alec Koppel, Aryan Mokhtari +2
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning
Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro
Network Newton-Part I: Algorithm and Convergence
Aryan Mokhtari, Qing Ling, Alejandro Ribeiro
Direct Runge-Kutta Discretization Achieves Acceleration
Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra +1
Online Optimization in Dynamic Environments: Improved Regret Rates for Strongly Convex Problems
Aryan Mokhtari, Shahin Shahrampour, Ali Jadbabaie +1
A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization
Aryan Mokhtari, Wei Shi, Qing Ling +1
Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate
Aryan Mokhtari, Mert Gürbüzbalaban, Alejandro Ribeiro
Network Newton
Aryan Mokhtari, Qing Ling, Alejandro Ribeiro
Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method
Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro
A Decentralized Second-Order Method for Dynamic Optimization
Aryan Mokhtari, Wei Shi, Qing Ling +1
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
Decentralized Quadratically Approximated Alternating Direction Method of Multipliers
Aryan Mokhtari, Wei Shi, Qing Ling +1
Escaping Saddle Points in Constrained Optimization
Aryan Mokhtari, Asuman Ozdaglar, Ali Jadbabaie
Global Convergence of Online Limited Memory BFGS
Aryan Mokhtari, Alejandro Ribeiro
A Class of Prediction-Correction Methods for Time-Varying Convex Optimization
Andrea Simonetto, Aryan Mokhtari, Alec Koppel +2
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free
Mingrui Zhang, Lin Chen, Aryan Mokhtari +2
IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate
Aryan Mokhtari, Mark Eisen, Alejandro Ribeiro
Network Newton-Part II: Convergence Rate and Implementation
Aryan Mokhtari, Qing Ling, Alejandro Ribeiro