Aryan Mokhtari

ORCID

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 162

Top Venues

Publications

Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate. CoRR (2024)
Qiujiang Jin, Tongzheng Ren, Nhat Ho, Aryan Mokhtari
Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models. Trans. Mach. Learn. Res. 2024 (2024)
Ruichen Jiang, Ali Kavis, Qiujiang Jin, Sujay Sanghavi, Aryan Mokhtari
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization. CoRR (2024)
Ruichen Jiang, Michal Derezinski, Aryan Mokhtari
Stochastic Newton Proximal Extragradient Method. CoRR (2024)
Devyani Maladkar, Ruichen Jiang, Aryan Mokhtari
Convergence Analysis of Adaptive Gradient Methods under Refined Smoothness and Noise Assumptions. CoRR (2024)
Ruichen Jiang, Parameswaran Raman, Shoham Sabach, Aryan Mokhtari, Mingyi Hong, Volkan Cevher
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate. AISTATS (2024)
Liam Collins, Advait Parulekar, Aryan Mokhtari, Sujay Sanghavi, Sanjay Shakkottai
In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness. CoRR (2024)
Jincheng Cao, Ruichen Jiang, Erfan Yazdandoost Hamedani, Aryan Mokhtari
An Accelerated Gradient Method for Simple Bilevel Optimization with Convex Lower-level Problem. CoRR (2024)
Nived Rajaraman, Devvrit, Aryan Mokhtari, Kannan Ramchandran
Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing. NeurIPS (2023)
Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari, Erfan Yazdandoost Hamedani
A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem. AISTATS (2023)
Parikshit Hegde, Gustavo de Veciana, Aryan Mokhtari
Network Adaptive Federated Learning: Congestion and Lossy Compression. CoRR (2023)
Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani, Hamed Hassani, Aryan Mokhtari
Straggler-Resilient Personalized Federated Learning. Trans. Mach. Learn. Res. 2023 (2023)
Ruichen Jiang, Aryan Mokhtari
Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization. NeurIPS (2023)
Ruichen Jiang, Qiujiang Jin, Aryan Mokhtari
Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence. CoRR (2023)
Ruichen Jiang, Aryan Mokhtari
Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization. CoRR (2023)
Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai
Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks. CoRR (2023)
Parikshit Hegde, Gustavo de Veciana, Aryan Mokhtari
Network Adaptive Federated Learning: Congestion and Lossy Compression. INFOCOM (2023)
Jincheng Cao, Ruichen Jiang, Nazanin Abolfazli, Erfan Yazdandoost Hamedani, Aryan Mokhtari
Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem. NeurIPS (2023)
Zhan Gao, Aryan Mokhtari, Alec Koppel
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate. CoRR (2023)
Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai
InfoNCE Loss Provably Learns Cluster-Preserving Representations. COLT (2023)
Jincheng Cao, Ruichen Jiang, Nazanin Abolfazli, Erfan Yazdandoost Hamedani, Aryan Mokhtari
Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem. CoRR (2023)
Nived Rajaraman, Devvrit, Aryan Mokhtari, Kannan Ramchandran
Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing and Neural Networks with Quadratic Activations. CoRR (2023)
Jerry Gu, Liam Collins, Debashri Roy, Aryan Mokhtari, Sanjay Shakkottai, Kaushik R. Chowdhury
Meta-Learning for Image-Guided Millimeter-Wave Beam Selection in Unseen Environments. ICASSP (2023)
Ruichen Jiang, Qiujiang Jin, Aryan Mokhtari
Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence. COLT (2023)
Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai
InfoNCE Loss Provably Learns Cluster-Preserving Representations. CoRR (2023)
Qiujiang Jin, Aryan Mokhtari
Non-asymptotic superlinear convergence of standard quasi-Newton methods. Math. Program. 200 (1) (2023)
Mohammad Fereydounian, Aryan Mokhtari, Ramtin Pedarsani, Hamed Hassani
Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach. IEEE Trans. Inf. Theory 69 (11) (2023)
Matthew Faw, Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari, Sanjay Shakkottai, Rachel Ward
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance. COLT (2022)
Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
How Does the Task Landscape Affect MAML Performance? CoLLAs (2022)
Mohammad Fereydounian, Aryan Mokhtari, Ramtin Pedarsani, Hamed Hassani
Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach. CoRR (2022)
Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari
Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. ICML (2022)
Arman Adibi, Aryan Mokhtari, Hamed Hassani
Minimax Optimization: The Case of Convex-Submodular. AISTATS (2022)
Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari, Ramtin Pedarsani
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity. IEEE J. Sel. Areas Inf. Theory 3 (2) (2022)
Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai
MAML and ANIL Provably Learn Representations. CoRR (2022)
Matthew Faw, Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari, Sanjay Shakkottai, Rachel Ward
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance. CoRR (2022)
Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning. NeurIPS (2022)
Ruichen Jiang, Aryan Mokhtari
Generalized Optimistic Methods for Convex-Concave Saddle Point Problems. CoRR (2022)
Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu
Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems. UAI (2022)
Isidoros Tziotis, Zebang Shen, Ramtin Pedarsani, Hamed Hassani, Aryan Mokhtari
Straggler-Resilient Personalized Federated Learning. CoRR (2022)
Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems. CoRR (2022)
Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari, Ramtin Pedarsani
Adaptive Node Participation for Straggler-Resilient Federated Learning. ICASSP (2022)
Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai
MAML and ANIL Provably Learn Representations. ICML (2022)
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 Shroff, Sanjay Shakkottai, Kannan Srinivasan, Ameet Talwalkar, Aylin Yener, Lei Ying
Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. CIC (2022)
Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari, Erfan Yazdandoost Hamedani
Generalized Frank-Wolfe Algorithm for Bilevel Optimization. CoRR (2022)
Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning. CoRR (2022)
Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai
Exploiting Shared Representations for Personalized Federated Learning. CoRR (2021)
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, Mehrdad Mahdavi
Federated Learning with Compression: Unified Analysis and Sharp Guarantees. AISTATS (2021)
Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks. CoRR (2021)
Arman Adibi, Aryan Mokhtari, Hamed Hassani
Minimax Optimization: The Case of Convex-Submodular. CoRR (2021)
Qiujiang Jin, Aryan Mokhtari
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach. NeurIPS (2021)
Alireza Fallah, Kristian Georgiev, Aryan Mokhtari, Asuman E. Ozdaglar
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning. NeurIPS (2021)
Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai
Exploiting Shared Representations for Personalized Federated Learning. ICML (2021)
Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar
Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks. NeurIPS (2021)
Qiujiang Jin, Aryan Mokhtari
Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach. CoRR (2021)
Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar
Provably Convergent Policy Gradient Methods for Model-Agnostic Meta-Reinforcement Learning. CoRR (2020)
Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach. AISTATS (2020)
Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani
Quantized Decentralized Stochastic Learning over Directed Graphs. ICML (2020)
Arman Adibi, Aryan Mokhtari, Hamed Hassani
Submodular Meta-Learning. NeurIPS (2020)
Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Gürbüzbalaban
DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. AISTATS (2020)
Aryan Mokhtari, Alec Koppel
High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation. IEEE Trans. Signal Process. 68 (2020)
Arman Adibi, Aryan Mokhtari, Hamed Hassani
Submodular Meta-Learning. CoRR (2020)
Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. NeurIPS (2020)
Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Zebang Shen
Stochastic Conditional Gradient++: (Non)Convex Minimization and Continuous Submodular Maximization. SIAM J. Optim. 30 (4) (2020)
Aryan Mokhtari, Leyla Sadighi, Behnam Bahrak, Mojtaba Eshghie
Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting. CoRR (2020)
Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
Task-Robust Model-Agnostic Meta-Learning. NeurIPS (2020)
Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari, Ramtin Pedarsani
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity. CoRR (2020)
Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar
Personalized Federated Learning: A Meta-Learning Approach. CoRR (2020)
Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil
Convergence Rate of 풪(1/k) for Optimistic Gradient and Extragradient Methods in Smooth Convex-Concave Saddle Point Problems. SIAM J. Optim. 30 (4) (2020)
Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi
One Sample Stochastic Frank-Wolfe. AISTATS (2020)
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization. J. Mach. Learn. Res. 21 (2020)
Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking. NeurIPS (2020)
Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
Distribution-Agnostic Model-Agnostic Meta-Learning. CoRR (2020)
Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free. AISTATS (2020)
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, Mehrdad Mahdavi
Federated Learning with Compression: Unified Analysis and Sharp Guarantees. CoRR (2020)
Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takác
Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. AISTATS (2020)
Aryan Mokhtari, Alec Koppel, Martin Takác, Alejandro Ribeiro
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. J. Mach. Learn. Res. 21 (2020)
Aryan Mokhtari, Alejandro Ribeiro
Stochastic Quasi-Newton Methods. Proc. IEEE 108 (11) (2020)
Mohammad Fereydounian, Zebang Shen, Aryan Mokhtari, Amin Karbasi, Hamed Hassani
Safe Learning under Uncertain Objectives and Constraints. CoRR (2020)
Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani
Quantized Push-sum for Gossip and Decentralized Optimization over Directed Graphs. CoRR (2020)
Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. AISTATS (2020)
Qiujiang Jin, Aryan Mokhtari
Non-asymptotic Superlinear Convergence of Standard Quasi-Newton Methods. CoRR (2020)
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, Ramtin Pedarsani
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. AISTATS (2020)
Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
Why Does MAML Outperform ERM? An Optimization Perspective. CoRR (2020)
Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani
Robust and Communication-Efficient Collaborative Learning. NeurIPS (2019)
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, Ramtin Pedarsani
FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. CoRR (2019)
Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil
Proximal Point Approximations Achieving a Convergence Rate of O(1/k) for Smooth Convex-Concave Saddle Point Problems: Optimistic Gradient and Extra-gradient Methods. CoRR (2019)
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani
An Exact Quantized Decentralized Gradient Descent Algorithm. IEEE Trans. Signal Process. 67 (19) (2019)
Weijie Liu, Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil, Zebang Shen, Nenggan Zheng
A Decentralized Proximal Point-type Method for Saddle Point Problems. CoRR (2019)
Jingzhao Zhang, César A. Uribe, Aryan Mokhtari, Ali Jadbabaie
Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE. ACC (2019)
Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro
A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization. IEEE Trans. Signal Process. 67 (23) (2019)
Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi
One Sample Stochastic Frank-Wolfe. CoRR (2019)
Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani
Robust and Communication-Efficient Collaborative Learning. CoRR (2019)
Amin Karbasi, Hamed Hassani, Aryan Mokhtari, Zebang Shen
Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match. NeurIPS (2019)
Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar
On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. CoRR (2019)
Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie
Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods. AISTATS (2019)
Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil
A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach. CoRR (2019)
Santiago Paternain, Aryan Mokhtari, Alejandro Ribeiro
A Newton-Based Method for Nonconvex Optimization with Fast Evasion of Saddle Points. SIAM J. Optim. 29 (1) (2019)
Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi
Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization. CoRR (2019)
Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Zebang Shen
Stochastic Conditional Gradient++. CoRR (2019)
Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie
Escaping Saddle Points in Constrained Optimization. NeurIPS (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Aryan Mokhtari