Regularized stochastic BFGS algorithm. - Aryan Mokhtari; Alejandro Ribeiro (GlobalSIP, 2013)

A dual stochastic DFP algorithm for optimal resource allocation in wireless systems. - Aryan Mokhtari; Alejandro Ribeiro (SPAWC, 2013)

Network Newton. - Aryan Mokhtari; Qing Ling 001; Alejandro Ribeiro (ACSSC, 2014)

A quasi-Newton method for large scale support vector machines. - Aryan Mokhtari; Alejandro Ribeiro (ICASSP, 2014)

RES: Regularized Stochastic BFGS Algorithm. - Aryan Mokhtari; Alejandro Ribeiro (IEEE Trans. Signal Process., 2014)

Decentralized double stochastic averaging gradient. - Aryan Mokhtari; Alejandro Ribeiro (ACSSC, 2015)

Prediction-correction methods for time-varying convex optimization. - Andrea Simonetto; Alec Koppel; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro (ACSSC, 2015)

A decentralized prediction-correction method for networked time-varying convex optimization. - Andrea Simonetto; Aryan Mokhtari; Alec Koppel; Geert Leus; Alejandro Ribeiro (CAMSAP, 2015)

Target tracking with dynamic convex optimization. - Alec Koppel; Andrea Simonetto; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro (GlobalSIP, 2015)

Decentralized quadratically approximated alternating direction method of multipliers. - Aryan Mokhtari; Wei Shi 010; Qing Ling 001; Alejandro Ribeiro (GlobalSIP, 2015)

An approximate Newton method for distributed optimization. - Aryan Mokhtari; Qing Ling 001; Alejandro Ribeiro (ICASSP, 2015)

Global convergence of online limited memory BFGS. - Aryan Mokhtari; Alejandro Ribeiro (J. Mach. Learn. Res., 2015)

Doubly stochastic algorithms for large-scale optimization. - Alec Koppel; Aryan Mokhtari; Alejandro Ribeiro (ACSSC, 2016)

ESOM: Exact second-order method for consensus optimization. - Aryan Mokhtari; Wei Shi 010; Qing Ling 001 (ACSSC, 2016)

Doubly random parallel stochastic methods for large scale learning. - Aryan Mokhtari; Alec Koppel; Alejandro Ribeiro (ACC, 2016)

A decentralized quasi-Newton method for dual formulations of consensus optimization. - Mark Eisen; Aryan Mokhtari; Alejandro Ribeiro (CDC, 2016)

Online optimization in dynamic environments: Improved regret rates for strongly convex problems. - Aryan Mokhtari; Shahin Shahrampour; Ali Jadbabaie; Alejandro Ribeiro (CDC, 2016)

A decentralized Second-Order Method for Dynamic Optimization. - Aryan Mokhtari; Wei Shi 010; Qing Ling 001; Alejandro Ribeiro (CDC, 2016)

A Quasi-newton prediction-correction method for decentralized dynamic convex optimization. - Andrea Simonetto; Alec Koppel; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro (ECC, 2016)

A data-driven approach to stochastic network optimization. - Tianyi Chen; Aryan Mokhtari; Xin Wang 003; Alejandro Ribeiro; Georgios B. Giannakis (GlobalSIP, 2016)

An asynchronous Quasi-Newton method for consensus optimization. - Mark Eisen; Aryan Mokhtari; Alejandro Ribeiro (GlobalSIP, 2016)

Decentralized constrained consensus optimization with primal dual splitting projection. - Han Zhang; Wei Shi 010; Aryan Mokhtari; Alejandro Ribeiro; Qing Ling 001 (GlobalSIP, 2016)

Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy. - Aryan Mokhtari; Hadi Daneshmand; Aurélien Lucchi; Thomas Hofmann 001; Alejandro Ribeiro (NIPS, 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; Alejandro Ribeiro (CoRR, 2016)

DSA: Decentralized Double Stochastic Averaging Gradient Algorithm. - Aryan Mokhtari; Alejandro Ribeiro (J. Mach. Learn. Res., 2016)

A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization. - Aryan Mokhtari; Wei Shi 010; Qing Ling 001; Alejandro Ribeiro (IEEE Trans. Signal Inf. Process. over Networks, 2016)

DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers. - Aryan Mokhtari; Wei Shi 010; Qing Ling 001; Alejandro Ribeiro (IEEE Trans. Signal Process., 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)

A primal-dual Quasi-Newton method for consensus optimization. - Mark Eisen; Aryan Mokhtari; Alejandro Ribeiro (ACSSC, 2017)

An incremental quasi-Newton method with a local superlinear convergence rate. - Aryan Mokhtari; Mark Eisen; Alejandro Ribeiro (ICASSP, 2017)

A double incremental aggregated gradient method with linear convergence rate for large-scale optimization. - Aryan Mokhtari; Mert Gürbüzbalaban; Alejandro Ribeiro (ICASSP, 2017)

A Diagonal-Augmented quasi-Newton method with application to factorization machines. - Aryan Mokhtari; Amir Ingber (ICASSP, 2017)

Large-scale nonconvex stochastic optimization by Doubly Stochastic Successive Convex approximation. - Aryan Mokhtari; Alec Koppel; Gesualdo Scutari; Alejandro Ribeiro (ICASSP, 2017)

First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization. - Aryan Mokhtari; Alejandro Ribeiro (NIPS, 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)

Stochastic Averaging for Constrained Optimization With Application to Online Resource Allocation. - Tianyi Chen; Aryan Mokhtari; Xin Wang 003; Alejandro Ribeiro; Georgios B. Giannakis (IEEE Trans. Signal Process., 2017)

Decentralized Quasi-Newton Methods. - Mark Eisen; Aryan Mokhtari; Alejandro Ribeiro (IEEE Trans. Signal Process., 2017)

Network Newton Distributed Optimization Methods. - Aryan Mokhtari; Qing Ling 001; Alejandro Ribeiro (IEEE Trans. Signal Process., 2017)

Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method. - Mark Eisen; Aryan Mokhtari; Alejandro Ribeiro (AISTATS, 2018)

Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap. - Aryan Mokhtari; Hamed Hassani; Amin Karbasi (AISTATS, 2018)

A Newton Method for Faster Navigation in Cluttered Environments. - Santiago Paternain; Aryan Mokhtari; Alejandro Ribeiro (CDC, 2018)

Quantized Decentralized Consensus Optimization. - Amirhossein Reisizadeh; Aryan Mokhtari; Hamed Hassani; Ramtin Pedarsani (CDC, 2018)

Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning. - Alec Koppel; Aryan Mokhtari; Alejandro Ribeiro (ICASSP, 2018)

Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings. - Aryan Mokhtari; Hamed Hassani; Amin Karbasi (ICML, 2018)

Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication. - Zebang Shen; Aryan Mokhtari; Tengfei Zhou; Peilin Zhao; Hui Qian 001 (ICML, 2018)

Escaping Saddle Points in Constrained Optimization. - Aryan Mokhtari; Asuman E. Ozdaglar; Ali Jadbabaie (NeurIPS, 2018)

Direct Runge-Kutta Discretization Achieves Acceleration. - Jingzhao Zhang; Aryan Mokhtari; Suvrit Sra; Ali Jadbabaie (NeurIPS, 2018)

IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate. - Aryan Mokhtari; Mark Eisen; Alejandro Ribeiro (SIAM J. Optim., 2018)

Surpassing Gradient Descent Provably: A Cyclic Incremental Method with Linear Convergence Rate. - Aryan Mokhtari; Mert Gürbüzbalaban; Alejandro Ribeiro (SIAM J. Optim., 2018)

Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods. - Aryan Mokhtari; Asuman E. Ozdaglar; Ali Jadbabaie (AISTATS, 2019)

Achieving Acceleration in Distributed Optimization via Direct Discretization of the Heavy-Ball ODE. - Jingzhao Zhang; César A. Uribe; Aryan Mokhtari; Ali Jadbabaie (ACC, 2019)

Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match. - Amin Karbasi; Hamed Hassani; Aryan Mokhtari; Zebang Shen (NeurIPS, 2019)

Robust and Communication-Efficient Collaborative Learning. - Amirhossein Reisizadeh; Hossein Taheri; Aryan Mokhtari; Hamed Hassani; Ramtin Pedarsani (NeurIPS, 2019)

Quantized Frank-Wolfe: Communication-Efficient Distributed Optimization. - Mingrui Zhang; Lin Chen 003; Aryan Mokhtari; Hamed Hassani; Amin Karbasi (CoRR, 2019)

Stochastic Conditional Gradient++. - Hamed Hassani; Amin Karbasi; Aryan Mokhtari; Zebang Shen (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)

A Decentralized Proximal Point-type Method for Saddle Point Problems. - Weijie Liu 006; Aryan Mokhtari; Asuman E. Ozdaglar; Sarath Pattathil; Zebang Shen; Nenggan Zheng (CoRR, 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 Primal-Dual Quasi-Newton Method for Exact Consensus Optimization. - Mark Eisen; Aryan Mokhtari; Alejandro Ribeiro (IEEE Trans. Signal Process., 2019)

An Exact Quantized Decentralized Gradient Descent Algorithm. - Amirhossein Reisizadeh; Aryan Mokhtari; Hamed Hassani; Ramtin Pedarsani (IEEE Trans. Signal Process., 2019)

On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. - Alireza Fallah 001; Aryan Mokhtari; Asuman E. Ozdaglar (AISTATS, 2020)

Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy. - Majid Jahani; Xi He 004; Chenxin Ma; Aryan Mokhtari; Dheevatsa Mudigere; Alejandro Ribeiro; Martin Takác 001 (AISTATS, 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 (AISTATS, 2020)

FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. - Amirhossein Reisizadeh; Aryan Mokhtari; Hamed Hassani; Ali Jadbabaie; Ramtin Pedarsani (AISTATS, 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 (AISTATS, 2020)

Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free. - Mingrui Zhang; Lin Chen 003; Aryan Mokhtari; Hamed Hassani; Amin Karbasi (AISTATS, 2020)

One Sample Stochastic Frank-Wolfe. - Mingrui Zhang; Zebang Shen; Aryan Mokhtari; Hamed Hassani; Amin Karbasi (AISTATS, 2020)

Quantized Decentralized Stochastic Learning over Directed Graphs. - Hossein Taheri; Aryan Mokhtari; Hamed Hassani; Ramtin Pedarsani (ICML, 2020)

Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. - Alireza Fallah 001; Aryan Mokhtari; Asuman E. Ozdaglar (NeurIPS, 2020)

Submodular Meta-Learning. - Arman Adibi; Aryan Mokhtari; Hamed Hassani (NeurIPS, 2020)

Task-Robust Model-Agnostic Meta-Learning. - Liam Collins; Aryan Mokhtari; Sanjay Shakkottai (NeurIPS, 2020)

Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking. - Isidoros Tziotis; Constantine Caramanis; Aryan Mokhtari (NeurIPS, 2020)

Distribution-Agnostic Model-Agnostic Meta-Learning. - Liam Collins; Aryan Mokhtari; Sanjay Shakkottai (CoRR, 2020)

Provably Convergent Policy Gradient Methods for Model-Agnostic Meta-Reinforcement Learning. - Alireza Fallah 001; Aryan Mokhtari; Asuman E. Ozdaglar (CoRR, 2020)

Personalized Federated Learning: A Meta-Learning Approach. - Alireza Fallah 001; Aryan Mokhtari; Asuman E. Ozdaglar (CoRR, 2020)

Quantized Push-sum for Gossip and Decentralized Optimization over Directed Graphs. - Hossein Taheri; Aryan Mokhtari; Hamed Hassani; Ramtin Pedarsani (CoRR, 2020)

Safe Learning under Uncertain Objectives and Constraints. - Mohammad Fereydounian; Zebang Shen; Aryan Mokhtari; Amin Karbasi; Hamed Hassani (CoRR, 2020)

Why Does MAML Outperform ERM? An Optimization Perspective. - Liam Collins; Aryan Mokhtari; Sanjay Shakkottai (CoRR, 2020)

Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization. - Aryan Mokhtari; Hamed Hassani; Amin Karbasi (J. Mach. Learn. Res., 2020)

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. - Aryan Mokhtari; Alec Koppel; Martin Takác 001; Alejandro Ribeiro (J. Mach. Learn. Res., 2020)

Stochastic Quasi-Newton Methods. - Aryan Mokhtari; Alejandro Ribeiro (Proc. IEEE, 2020)

Stochastic Conditional Gradient++: (Non)Convex Minimization and Continuous Submodular Maximization. - Hamed Hassani; Amin Karbasi; Aryan Mokhtari; Zebang Shen (SIAM J. Optim., 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)

High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation. - Aryan Mokhtari; Alec Koppel (IEEE Trans. Signal Process., 2020)

Federated Learning with Compression: Unified Analysis and Sharp Guarantees. - Farzin Haddadpour; Mohammad Mahdi Kamani; Aryan Mokhtari; Mehrdad Mahdavi (AISTATS, 2021)

Exploiting Shared Representations for Personalized Federated Learning. - Liam Collins; Hamed Hassani; Aryan Mokhtari; Sanjay Shakkottai (ICML, 2021)

On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning. - Alireza Fallah 001; Kristian Georgiev; Aryan Mokhtari; Asuman E. Ozdaglar (NeurIPS, 2021)

Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks. - Alireza Fallah 001; Aryan Mokhtari; Asuman E. Ozdaglar (NeurIPS, 2021)

Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach. - Qiujiang Jin; Aryan Mokhtari (NeurIPS, 2021)

Minimax Optimization: The Case of Convex-Submodular. - Arman Adibi; Aryan Mokhtari; Hamed Hassani (AISTATS, 2022)

Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. - Sen Lin 001; Ming Shi 003; Anish Arora; Raef Bassily; Elisa Bertino; Constantine Caramanis; Kaushik R. Chowdhury; Eylem Ekici; Atilla Eryilmaz; Stratis Ioannidis; Nan Jiang 008; Gauri Joshi; et al. (CIC, 2022)

How Does the Task Landscape Affect MAML Performance? - Liam Collins; Aryan Mokhtari; Sanjay Shakkottai (CoLLAs, 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 (COLT, 2022)

Adaptive Node Participation for Straggler-Resilient Federated Learning. - Amirhossein Reisizadeh; Isidoros Tziotis; Hamed Hassani; Aryan Mokhtari; Ramtin Pedarsani (ICASSP, 2022)

MAML and ANIL Provably Learn Representations. - Liam Collins; Aryan Mokhtari; Sewoong Oh; Sanjay Shakkottai (ICML, 2022)

Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. - Qiujiang Jin; Alec Koppel; Ketan Rajawat; Aryan Mokhtari (ICML, 2022)

FedAvg with Fine Tuning: Local Updates Lead to Representation Learning. - Liam Collins; Hamed Hassani; Aryan Mokhtari; Sanjay Shakkottai (NeurIPS, 2022)

Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems. - Mao Ye 006; Ruichen Jiang; Haoxiang Wang 003; Dhruv Choudhary; Xiaocong Du; Bhargav Bhushanam; Aryan Mokhtari; Arun Kejariwal; Qiang Liu 001 (UAI, 2022)

Generalized Optimistic Methods for Convex-Concave Saddle Point Problems. - Ruichen Jiang; Aryan Mokhtari (CoRR, 2022)

Generalized Frank-Wolfe Algorithm for Bilevel Optimization. - Ruichen Jiang; Nazanin Abolfazli; Aryan Mokhtari; Erfan Yazdandoost Hamedani (CoRR, 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)

A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem. - Ruichen Jiang; Nazanin Abolfazli; Aryan Mokhtari; Erfan Yazdandoost Hamedani (AISTATS, 2023)

Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence. - Ruichen Jiang; Qiujiang Jin; Aryan Mokhtari (COLT, 2023)

InfoNCE Loss Provably Learns Cluster-Preserving Representations. - Advait Parulekar; Liam Collins; Karthikeyan Shanmugam; Aryan Mokhtari; Sanjay Shakkottai (COLT, 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 (ICASSP, 2023)

Network Adaptive Federated Learning: Congestion and Lossy Compression. - Parikshit Hegde; Gustavo de Veciana; Aryan Mokhtari (INFOCOM, 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 (NeurIPS, 2023)

Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization. - Ruichen Jiang; Aryan Mokhtari (NeurIPS, 2023)

Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing. - Nived Rajaraman; Devvrit; Aryan Mokhtari; Kannan Ramchandran (NeurIPS, 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)

Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate. - Zhan Gao; Aryan Mokhtari; Alec Koppel (CoRR, 2023)

Non-asymptotic superlinear convergence of standard quasi-Newton methods. - Qiujiang Jin; Aryan Mokhtari (Math. Program., 2023)

Straggler-Resilient Personalized Federated Learning. - Isidoros Tziotis; Zebang Shen; Ramtin Pedarsani; Hamed Hassani; Aryan Mokhtari (Trans. Mach. Learn. Res., 2023)

Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach. - Mohammad Fereydounian; Aryan Mokhtari; Ramtin Pedarsani; Hamed Hassani (IEEE Trans. Inf. Theory, 2023)

Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate. - Ruichen Jiang; Parameswaran Raman; Shoham Sabach; Aryan Mokhtari; Mingyi Hong 001; Volkan Cevher (AISTATS, 2024)

Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks. - Liam Collins; Hamed Hassani; Mahdi Soltanolkotabi; Aryan Mokhtari; Sanjay Shakkottai (ICML, 2024)

An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization. - Jincheng Cao; Ruichen Jiang; Erfan Yazdandoost Hamedani; Aryan Mokhtari (NeurIPS, 2024)

In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness. - Liam Collins; Advait Parulekar; Aryan Mokhtari; Sujay Sanghavi; Sanjay Shakkottai (NeurIPS, 2024)

Stochastic Newton Proximal Extragradient Method. - Ruichen Jiang; Michal Derezinski; Aryan Mokhtari (NeurIPS, 2024)

Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization. - Ruichen Jiang; Ali Kavis; Qiujiang Jin; Sujay Sanghavi; Aryan Mokhtari (NeurIPS, 2024)

Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search. - Qiujiang Jin; Ruichen Jiang; Aryan Mokhtari (NeurIPS, 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)

Convergence Analysis of Adaptive Gradient Methods under Refined Smoothness and Noise Assumptions. - Devyani Maladkar; Ruichen Jiang; Aryan Mokhtari (CoRR, 2024)

Online Learning Guided Quasi-Newton Methods with Global Non-Asymptotic Convergence. - Ruichen Jiang; Aryan Mokhtari (CoRR, 2024)

Meta-Learning Adaptable Foundation Models. - Jacob L. Block; Sundararajan Srinivasan; Liam Collins; Aryan Mokhtari; Sanjay Shakkottai (CoRR, 2024)

Learning Mixtures of Experts with EM. - Quentin Fruytier; Aryan Mokhtari; Sujay Sanghavi (CoRR, 2024)

Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models. - Qiujiang Jin; Tongzheng Ren; Nhat Ho; Aryan Mokhtari (Trans. Mach. Learn. Res., 2024)

Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization. - Ruichen Jiang; Devyani Maladkar; Aryan Mokhtari (COLT, 2025)

On the Crucial Role of Initialization for Matrix Factorization. - Bingcong Li; Liang Zhang; Aryan Mokhtari; Niao He (ICLR, 2025)

Improved Complexity for Smooth Nonconvex Optimization: A Two-Level Online Learning Approach with Quasi-Newton Methods. - Ruichen Jiang; Aryan Mokhtari; Francisco Patitucci (STOC, 2025)

Machine Unlearning under Overparameterization. - Jacob L. Block; Aryan Mokhtari; Sanjay Shakkottai (CoRR, 2025)

Online Learning-guided Learning Rate Adaptation via Gradient Alignment. - Ruichen Jiang; Ali Kavis; Aryan Mokhtari (CoRR, 2025)

Affine-Invariant Global Non-Asymptotic Convergence Analysis of BFGS under Self-Concordance. - Qiujiang Jin; Aryan Mokhtari (CoRR, 2025)

On the Complexity of Finding Stationary Points in Nonconvex Simple Bilevel Optimization. - Jincheng Cao; Ruichen Jiang; Erfan Yazdandoost Hamedani; Aryan Mokhtari (CoRR, 2025)