| 2026 | COLT | Statistical Learning from Attribution Sets. | Lorne Applebaum, Rbert Busa-Fekete, August Y. Chen, Claudio Gentile, Tomer Koren, Aryan Mokhtari |
| 2026 | COLT | Adaptive Matrix Online Learning through Smoothing with Guarantees for Nonsmooth Nonconvex Optimization. | Ruichen Jiang, Zakaria Mhammedi, Mehryar Mohri, Aryan Mokhtari |
| 2025 | COLT | Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization. | Ruichen Jiang, Devyani Maladkar, Aryan Mokhtari |
| 2025 | ICLR | On the Crucial Role of Initialization for Matrix Factorization. | Bingcong Li, Liang Zhang, Aryan Mokhtari, Niao He |
| 2025 | ICML | Learning Mixtures of Experts with EM: A Mirror Descent Perspective. | Quentin Fruytier, Aryan Mokhtari, Sujay Sanghavi |
| 2025 | STOC | Improved Complexity for Smooth Nonconvex Optimization: A Two-Level Online Learning Approach with Quasi-Newton Methods. | Ruichen Jiang, Aryan Mokhtari, Francisco Patitucci |
| 2024 | AISTATS | 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 |
| 2024 | ICML | Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks. | Liam Collins, Hamed Hassani, Mahdi Soltanolkotabi, Aryan Mokhtari, Sanjay Shakkottai |
| 2023 | AISTATS | A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem. | Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari, Erfan Yazdandoost Hamedani |
| 2023 | COLT | Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence. | Ruichen Jiang, Qiujiang Jin, Aryan Mokhtari |
| 2023 | COLT | InfoNCE Loss Provably Learns Cluster-Preserving Representations. | Advait Parulekar, Liam Collins, Karthikeyan Shanmugam, Aryan Mokhtari, Sanjay Shakkottai |
| 2023 | ICASSP | 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 |
| 2023 | INFOCOM | Network Adaptive Federated Learning: Congestion and Lossy Compression. | Parikshit Hegde, Gustavo de Veciana, Aryan Mokhtari |
| 2022 | AISTATS | Minimax Optimization: The Case of Convex-Submodular. | Arman Adibi, Aryan Mokhtari, Hamed Hassani |
| 2022 | COLT | 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 |
| 2022 | ICASSP | Adaptive Node Participation for Straggler-Resilient Federated Learning. | Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani, Aryan Mokhtari, Ramtin Pedarsani |
| 2022 | ICML | MAML and ANIL Provably Learn Representations. | Liam Collins, Aryan Mokhtari, Sewoong Oh, Sanjay Shakkottai |
| 2022 | ICML | Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. | Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari |
| 2022 | UAI | 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 |
| 2021 | AISTATS | Federated Learning with Compression: Unified Analysis and Sharp Guarantees. | Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, Mehrdad Mahdavi |
| 2021 | ICML | Exploiting Shared Representations for Personalized Federated Learning. | Liam Collins, Hamed Hassani, Aryan Mokhtari, Sanjay Shakkottai |
| 2020 | AISTATS | On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms. | Alireza Fallah, Aryan Mokhtari, Asuman E. Ozdaglar |
| 2020 | AISTATS | 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 Takc |
| 2020 | AISTATS | A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach. | Aryan Mokhtari, Asuman E. Ozdaglar, Sarath Pattathil |
| 2020 | AISTATS | FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. | Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, Ramtin Pedarsani |
| 2020 | AISTATS | DAve-QN: A Distributed Averaged Quasi-Newton Method with Local Superlinear Convergence Rate. | Saeed Soori, Konstantin Mishchenko, Aryan Mokhtari, Maryam Mehri Dehnavi, Mert Grbzbalaban |
| 2020 | AISTATS | Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free. | Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi |
| 2020 | AISTATS | One Sample Stochastic Frank-Wolfe. | Mingrui Zhang, Zebang Shen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi |
| 2020 | ICML | Quantized Decentralized Stochastic Learning over Directed Graphs. | Hossein Taheri, Aryan Mokhtari, Hamed Hassani, Ramtin Pedarsani |
| 2019 | AISTATS | Efficient Nonconvex Empirical Risk Minimization via Adaptive Sample Size Methods. | Aryan Mokhtari, Asuman E. Ozdaglar, Ali Jadbabaie |
| 2018 | AISTATS | Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method. | Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro |
| 2018 | AISTATS | Conditional Gradient Method for Stochastic Submodular Maximization: Closing the Gap. | Aryan Mokhtari, Hamed Hassani, Amin Karbasi |
| 2018 | ICASSP | Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning. | Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro |
| 2018 | ICML | Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings. | Aryan Mokhtari, Hamed Hassani, Amin Karbasi |
| 2018 | ICML | Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication. | Zebang Shen, Aryan Mokhtari, Tengfei Zhou, Peilin Zhao, Hui Qian |
| 2017 | ACSSC | A primal-dual Quasi-Newton method for consensus optimization. | Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro |
| 2017 | ICASSP | An incremental quasi-Newton method with a local superlinear convergence rate. | Aryan Mokhtari, Mark Eisen, Alejandro Ribeiro |
| 2017 | ICASSP | A double incremental aggregated gradient method with linear convergence rate for large-scale optimization. | Aryan Mokhtari, Mert Grbzbalaban, Alejandro Ribeiro |
| 2017 | ICASSP | A Diagonal-Augmented quasi-Newton method with application to factorization machines. | Aryan Mokhtari, Amir Ingber |
| 2017 | ICASSP | Large-scale nonconvex stochastic optimization by Doubly Stochastic Successive Convex approximation. | Aryan Mokhtari, Alec Koppel, Gesualdo Scutari, Alejandro Ribeiro |
| 2016 | ACSSC | Doubly stochastic algorithms for large-scale optimization. | Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro |
| 2016 | ACSSC | ESOM: Exact second-order method for consensus optimization. | Aryan Mokhtari, Wei Shi, Qing Ling |
| 2015 | ACSSC | Decentralized double stochastic averaging gradient. | Aryan Mokhtari, Alejandro Ribeiro |
| 2015 | ACSSC | Prediction-correction methods for time-varying convex optimization. | Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro |
| 2015 | ICASSP | An approximate Newton method for distributed optimization. | Aryan Mokhtari, Qing Ling, Alejandro Ribeiro |
| 2014 | ACSSC | Network Newton. | Aryan Mokhtari, Qing Ling, Alejandro Ribeiro |
| 2014 | ICASSP | A quasi-Newton method for large scale support vector machines. | Aryan Mokhtari, Alejandro Ribeiro |