Sampling and Distortion Tradeoffs for Indirect Source Retrieval. - Elaheh Mohammadi; Alireza Fallah 001; Farokh Marvasti (IEEE Trans. Inf. Theory, 2017)

A Universally Optimal Multistage Accelerated Stochastic Gradient Method. - Necdet Serhat Aybat; Alireza Fallah 001; Mert Gürbüzbalaban; Asuman E. Ozdaglar (NeurIPS, 2019)

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

An Optimal Multistage Stochastic Gradient Method for Minimax Problems. - Alireza Fallah 001; Asuman E. Ozdaglar; Sarath Pattathil (CDC, 2020)

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

Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions. - Necdet Serhat Aybat; Alireza Fallah 001; Mert Gürbüzbalaban; Asuman E. Ozdaglar (SIAM J. Optim., 2020)

Private Adaptive Gradient Methods for Convex Optimization. - Hilal Asi; John C. Duchi; Alireza Fallah 001; Omid Javidbakht; Kunal Talwar (ICML, 2021)

A Wasserstein Minimax Framework for Mixed Linear Regression. - Theo Diamandis; Yonina C. Eldar; Alireza Fallah 001; Farzan Farnia; Asuman E. Ozdaglar (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)

Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms. - Alireza Fallah 001; Ali Makhdoumi; Azarakhsh Malekian; Asuman E. Ozdaglar (NeurIPS, 2022)

Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks. - Alireza Fallah 001; Mert Gürbüzbalaban; Asuman E. Ozdaglar; Umut Simsekli; Lingjiong Zhu (J. Mach. Learn. Res., 2022)

Entropic Compressibility of Lévy Processes. - Julien Fageot; Alireza Fallah 001; Thibaut Horel (IEEE Trans. Inf. Theory, 2022)

Algorithmic Interactions With Strategic Users: Incentives, Interplay, and Impact - Alireza Fallah 001 (2023)

Fair Allocation in Dynamic Mechanism Design. - Alireza Fallah 001; Michael I. Jordan; Annie Ulichney (NeurIPS, 2024)

Contract Design With Safety Inspections. - Alireza Fallah 001; Michael I. Jordan (EC, 2024)

The Limits of Price Discrimination Under Privacy Constraints. - Alireza Fallah 001; Michael I. Jordan; Ali Makhdoumi; Azarakhsh Malekian (CoRR, 2024)

On Three-Layer Data Markets. - Alireza Fallah 001; Michael I. Jordan; Ali Makhdoumi; Azarakhsh Malekian (CoRR, 2024)

Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms. - Alireza Fallah 001; Ali Makhdoumi; Azarakhsh Malekian; Asuman E. Ozdaglar (Oper. Res., 2024)

Optimal adaptive testing for epidemic control: Combining molecular and serology tests. - Daron Acemoglu; Alireza Fallah 001; Andrea Giometto; Daniel P. Huttenlocher; Asuman E. Ozdaglar; Francesca Parise; Sarath Pattathil (Autom., 2024)

Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. - Maryam Aliakbarpour; Syomantak Chaudhuri; Thomas A. Courtade; Alireza Fallah 001; Michael I. Jordan (AISTATS, 2025)

A General Framework for Estimating Preferences Using Response Time Data. - Federico Echenique; Alireza Fallah 001; Michael I. Jordan (CoRR, 2025)