Aryan Mokhtari

According to our database, Aryan Mokhtari authored at least 119 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness.

Liam Collins, Advait Parulekar, Aryan Mokhtari, Sujay Sanghavi, Sanjay Shakkottai

CoRR, 2024

An Accelerated Gradient Method for Simple Bilevel Optimization with Convex Lower-level Problem.

Jincheng Cao, Ruichen Jiang, Erfan Yazdandoost Hamedani, Aryan Mokhtari

CoRR, 2024

Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate.

Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher

CoRR, 2024

2023

Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach.

Mohammad Fereydounian, Aryan Mokhtari, Ramtin Pedarsani, Hamed Hassani

IEEE Trans. Inf. Theory, November, 2023

Non-asymptotic superlinear convergence of standard quasi-Newton methods.

Qiujiang Jin, Aryan Mokhtari

Math. Program., 2023

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks.

Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai

CoRR, 2023

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

Zhan Gao, Aryan Mokhtari, Alec Koppel

CoRR, 2023

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

Nived Rajaraman, Devvrit, Aryan Mokhtari, Kannan Ramchandran

CoRR, 2023

Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing.

Nived Rajaraman, Devvrit, Aryan Mokhtari, Kannan Ramchandran

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Ruichen Jiang, Aryan Mokhtari

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Jincheng Cao, Ruichen Jiang, Nazanin Abolfazli, Erfan Yazdandoost Hamedani, Aryan Mokhtari

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Network Adaptive Federated Learning: Congestion and Lossy Compression.

Parikshit Hegde, Gustavo de Veciana, Aryan Mokhtari

Proceedings of the IEEE INFOCOM 2023, 2023

Meta-Learning for Image-Guided Millimeter-Wave Beam Selection in Unseen Environments.

Jerry Gu, Liam Collins, Debashri Roy, Aryan Mokhtari, Sanjay Shakkottai, Kaushik R. Chowdhury

Proceedings of the IEEE International Conference on Acoustics, 2023

InfoNCE Loss Provably Learns Cluster-Preserving Representations.

Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

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

Ruichen Jiang, Qiujiang Jin, Aryan Mokhtari

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem.

Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari, Erfan Yazdandoost Hamedani

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity.

Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari, Ramtin Pedarsani

IEEE J. Sel. Areas Inf. Theory, 2022

Generalized Frank-Wolfe Algorithm for Bilevel Optimization.

Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari, Erfan Yazdandoost Hamedani

CoRR, 2022

Straggler-Resilient Personalized Federated Learning.

Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani, Hamed Hassani, Aryan Mokhtari

CoRR, 2022

Generalized Optimistic Methods for Convex-Concave Saddle Point Problems.

Ruichen Jiang, Aryan Mokhtari

CoRR, 2022

Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems.

Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu

Proceedings of the Uncertainty in Artificial Intelligence, 2022

FedAvg with Fine Tuning: Local Updates Lead to Representation Learning.

Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood.

Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari

Proceedings of the International Conference on Machine Learning, 2022

MAML and ANIL Provably Learn Representations.

Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai

Proceedings of the International Conference on Machine Learning, 2022

Adaptive Node Participation for Straggler-Resilient Federated Learning.

Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari, Ramtin Pedarsani

Proceedings of the IEEE International Conference on Acoustics, 2022

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

Matthew Faw, Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari, Sanjay Shakkottai, Rachel A. Ward

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

How Does the Task Landscape Affect MAML Performance?

Liam Collins, Aryan Mokhtari, Sanjay Shakkottai

Proceedings of the Conference on Lifelong Learning Agents, 2022

Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks.

Sen Lin, Ming Shi, Anish Arora, Raef Bassily, Elisa Bertino, Constantine Caramanis, Kaushik R. Chowdhury, Eylem Ekici, Atilla Eryilmaz, Stratis Ioannidis, Nan Jiang, Gauri Joshi, Jim Kurose, Yingbin Liang, Zhiqiang Lin, Jia Liu, Mingyan Liu, Tommaso Melodia, Aryan Mokhtari, Rob Nowak, Sewoong Oh, Srini Parthasarathy, Chunyi Peng, Hulya Seferoglu, Ness B. Shroff, Sanjay Shakkottai, Kannan Srinivasan, Ameet Talwalkar, Aylin Yener, Lei Ying

Proceedings of the 8th IEEE International Conference on Collaboration and Internet Computing, 2022

Minimax Optimization: The Case of Convex-Submodular.

Arman Adibi, Aryan Mokhtari, Hamed Hassani

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

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

Qiujiang Jin, Aryan Mokhtari

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning.

Alireza Fallah, Kristian Georgiev, Aryan Mokhtari, Asuman E. Ozdaglar

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Exploiting Shared Representations for Personalized Federated Learning.

Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai

Proceedings of the 38th International Conference on Machine Learning, 2021

Federated Learning with Compression: Unified Analysis and Sharp Guarantees.

Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, Mehrdad Mahdavi

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation.

Aryan Mokhtari, Alec Koppel

IEEE Trans. Signal Process., 2020

Convergence Rate of 풪(1/k) for Optimistic Gradient and Extragradient Methods in Smooth Convex-Concave Saddle Point Problems.

Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil

SIAM J. Optim., 2020

Stochastic Conditional Gradient++: (Non)Convex Minimization and Continuous Submodular Maximization.

Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Zebang Shen

SIAM J. Optim., 2020

Stochastic Quasi-Newton Methods.

Aryan Mokhtari, Alejandro Ribeiro

Proc. IEEE, 2020

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

Aryan Mokhtari, Alec Koppel, Martin Takác, Alejandro Ribeiro

J. Mach. Learn. Res., 2020

Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization.

Aryan Mokhtari, Hamed Hassani, Amin Karbasi

J. Mach. Learn. Res., 2020

Why Does MAML Outperform ERM? An Optimization Perspective.

Liam Collins, Aryan Mokhtari, Sanjay Shakkottai

CoRR, 2020

Safe Learning under Uncertain Objectives and Constraints.

Mohammad Fereydounian, Zebang Shen, Aryan Mokhtari, Amin Karbasi, Hamed Hassani

CoRR, 2020

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

Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

CoRR, 2020

Personalized Federated Learning: A Meta-Learning Approach.

Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar

CoRR, 2020

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

Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar

CoRR, 2020

Distribution-Agnostic Model-Agnostic Meta-Learning.

Liam Collins, Aryan Mokhtari, Sanjay Shakkottai

CoRR, 2020

Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking.

Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Task-Robust Model-Agnostic Meta-Learning.

Liam Collins, Aryan Mokhtari, Sanjay Shakkottai

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Submodular Meta-Learning.

Arman Adibi, Aryan Mokhtari, Hamed Hassani

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach.

Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Quantized Decentralized Stochastic Learning over Directed Graphs.

Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

Proceedings of the 37th International Conference on Machine Learning, 2020

One Sample Stochastic Frank-Wolfe.

Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free.

Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate.

Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Gürbüzbalaban

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, Ramtin Pedarsani

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

An Exact Quantized Decentralized Gradient Descent Algorithm.

Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

IEEE Trans. Signal Process., 2019

A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization.

Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro

IEEE Trans. Signal Process., 2019

A Newton-Based Method for Nonconvex Optimization with Fast Evasion of Saddle Points.

Santiago Paternain, Aryan Mokhtari, Alejandro Ribeiro

SIAM J. Optim., 2019

A Decentralized Proximal Point-type Method for Saddle Point Problems.

Weijie Liu, Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil, Zebang Shen, Nenggan Zheng

CoRR, 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.

Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil

CoRR, 2019

Stochastic Conditional Gradient++.

Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Zebang Shen

CoRR, 2019

Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization.

Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi

CoRR, 2019

Robust and Communication-Efficient Collaborative Learning.

Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match.

Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE.

Jingzhao Zhang, César A. Uribe, Aryan Mokhtari, Ali Jadbabaie

Proceedings of the 2019 American Control Conference, 2019

Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods.

Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate.

Aryan Mokhtari, Mert Gürbüzbalaban, Alejandro Ribeiro

SIAM J. Optim., 2018

IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate.

Aryan Mokhtari, Mark Eisen, Alejandro Ribeiro

SIAM J. Optim., 2018

Direct Runge-Kutta Discretization Achieves Acceleration.

Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Escaping Saddle Points in Constrained Optimization.

Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

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

Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian

Proceedings of the 35th International Conference on Machine Learning, 2018

Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings.

Aryan Mokhtari, Hamed Hassani, Amin Karbasi

Proceedings of the 35th International Conference on Machine Learning, 2018

Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning.

Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Quantized Decentralized Consensus Optimization.

Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

A Newton Method for Faster Navigation in Cluttered Environments.

Santiago Paternain, Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap.

Aryan Mokhtari, Hamed Hassani, Amin Karbasi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method.

Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Network Newton Distributed Optimization Methods.

Aryan Mokhtari, Qing Ling, Alejandro Ribeiro

IEEE Trans. Signal Process., 2017

Decentralized Quasi-Newton Methods.

Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro

IEEE Trans. Signal Process., 2017

Stochastic Averaging for Constrained Optimization With Application to Online Resource Allocation.

Tianyi Chen, Aryan Mokhtari, Xin Wang, Alejandro Ribeiro, Georgios B. Giannakis

IEEE Trans. Signal Process., 2017

Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization.

Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro

IEEE Trans. Autom. Control., 2017

First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization.

Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Large-scale nonconvex stochastic optimization by Doubly Stochastic Successive Convex approximation.

Aryan Mokhtari, Alec Koppel, Gesualdo Scutari, Alejandro Ribeiro

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

A Diagonal-Augmented quasi-Newton method with application to factorization machines.

Aryan Mokhtari, Amir Ingber

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

A double incremental aggregated gradient method with linear convergence rate for large-scale optimization.

Aryan Mokhtari, Mert Gürbüzbalaban, Alejandro Ribeiro

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

An incremental quasi-Newton method with a local superlinear convergence rate.

Aryan Mokhtari, Mark Eisen, Alejandro Ribeiro

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

A primal-dual Quasi-Newton method for consensus optimization.

Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the 51st Asilomar Conference on Signals, Systems, and Computers, 2017

2016

A Class of Prediction-Correction Methods for Time-Varying Convex Optimization.

Andrea Simonetto, Aryan Mokhtari, Alec Koppel, Geert Leus, Alejandro Ribeiro

IEEE Trans. Signal Process., 2016

DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers.

Aryan Mokhtari, Wei Shi, Qing Ling, Alejandro Ribeiro

IEEE Trans. Signal Process., 2016

A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization.

Aryan Mokhtari, Wei Shi, Qing Ling, Alejandro Ribeiro

IEEE Trans. Signal Inf. Process. over Networks, 2016

DSA: Decentralized Double Stochastic Averaging Gradient Algorithm.

Aryan Mokhtari, Alejandro Ribeiro

J. Mach. Learn. Res., 2016

Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy.

Aryan Mokhtari, Alejandro Ribeiro

CoRR, 2016

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

Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro

CoRR, 2016

Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy.

Aryan Mokhtari, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Alejandro Ribeiro

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Decentralized constrained consensus optimization with primal dual splitting projection.

Han Zhang, Wei Shi, Aryan Mokhtari, Alejandro Ribeiro, Qing Ling

Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

An asynchronous Quasi-Newton method for consensus optimization.

Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

A data-driven approach to stochastic network optimization.

Tianyi Chen, Aryan Mokhtari, Xin Wang, Alejandro Ribeiro, Georgios B. Giannakis

Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

A Quasi-newton prediction-correction method for decentralized dynamic convex optimization.

Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro

Proceedings of the 15th European Control Conference, 2016

A decentralized Second-Order Method for Dynamic Optimization.

Aryan Mokhtari, Wei Shi, Qing Ling, Alejandro Ribeiro

Proceedings of the 55th IEEE Conference on Decision and Control, 2016

Online optimization in dynamic environments: Improved regret rates for strongly convex problems.

Aryan Mokhtari, Shahin Shahrampour, Ali Jadbabaie, Alejandro Ribeiro

Proceedings of the 55th IEEE Conference on Decision and Control, 2016

A decentralized quasi-Newton method for dual formulations of consensus optimization.

Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the 55th IEEE Conference on Decision and Control, 2016

Doubly random parallel stochastic methods for large scale learning.

Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro

Proceedings of the 2016 American Control Conference, 2016

ESOM: Exact second-order method for consensus optimization.

Aryan Mokhtari, Wei Shi, Qing Ling

Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

Doubly stochastic algorithms for large-scale optimization.

Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016

2015

Global convergence of online limited memory BFGS.

Aryan Mokhtari, Alejandro Ribeiro

J. Mach. Learn. Res., 2015

An approximate Newton method for distributed optimization.

Aryan Mokhtari, Qing Ling, Alejandro Ribeiro

Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Decentralized quadratically approximated alternating direction method of multipliers.

Aryan Mokhtari, Wei Shi, Qing Ling, Alejandro Ribeiro

Proceedings of the 2015 IEEE Global Conference on Signal and Information Processing, 2015

Target tracking with dynamic convex optimization.

Alec Koppel, Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro

Proceedings of the 2015 IEEE Global Conference on Signal and Information Processing, 2015

A decentralized prediction-correction method for networked time-varying convex optimization.

Andrea Simonetto, Aryan Mokhtari, Alec Koppel, Geert Leus, Alejandro Ribeiro

Proceedings of the 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2015

Prediction-correction methods for time-varying convex optimization.

Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro

Proceedings of the 49th Asilomar Conference on Signals, Systems and Computers, 2015

Decentralized double stochastic averaging gradient.

Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the 49th Asilomar Conference on Signals, Systems and Computers, 2015

2014

RES: Regularized Stochastic BFGS Algorithm.

Aryan Mokhtari, Alejandro Ribeiro

IEEE Trans. Signal Process., 2014

A quasi-Newton method for large scale support vector machines.

Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the IEEE International Conference on Acoustics, 2014

Network Newton.

Aryan Mokhtari, Qing Ling, Alejandro Ribeiro

Proceedings of the 48th Asilomar Conference on Signals, Systems and Computers, 2014

2013

A dual stochastic DFP algorithm for optimal resource allocation in wireless systems.

Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the 14th IEEE Workshop on Signal Processing Advances in Wireless Communications, 2013

Regularized stochastic BFGS algorithm.

Aryan Mokhtari, Alejandro Ribeiro

Proceedings of the IEEE Global Conference on Signal and Information Processing, 2013