Alireza Fallah

Andrea Giometto

Daniel P. Huttenlocher

Francesca Parise

Sarath Pattathil

Autom., February, 2024

On Three-Layer Data Markets.

[BibT_eX]

[DOI]

CoRR, 2024

The Limits of Price Discrimination Under Privacy Constraints.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Contract Design With Safety Inspections.

[BibT_eX]

[DOI]

Michael I. Jordan

CoRR, 2023

2022

Entropic Compressibility of Lévy Processes.

[BibT_eX]

[DOI]

Julien Fageot

Thibaut Horel

IEEE Trans. Inf. Theory, 2022

Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms.

[BibT_eX]

[DOI]

Proceedings of the EC '22: The 23rd ACM Conference on Economics and Computation, Boulder, CO, USA, July 11, 2022

Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms.

[BibT_eX]

[DOI]

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

2021

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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

A Wasserstein Minimax Framework for Mixed Linear Regression.

[BibT_eX]

[DOI]

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

Private Adaptive Gradient Methods for Convex Optimization.

[BibT_eX]

[DOI]

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

2020

Robust Accelerated Gradient Methods for Smooth Strongly Convex Functions.

[BibT_eX]

[DOI]

SIAM J. Optim., 2020

Personalized Federated Learning: A Meta-Learning Approach.

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

CoRR, 2020

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

[BibT_eX]

[DOI]

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

An Optimal Multistage Stochastic Gradient Method for Minimax Problems.

[BibT_eX]

[DOI]

Sarath Pattathil

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

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

[BibT_eX]

[DOI]

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

2019

A Universally Optimal Multistage Accelerated Stochastic Gradient Method.

[BibT_eX]

[DOI]

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

2017

Sampling and Distortion Tradeoffs for Indirect Source Retrieval.

[BibT_eX]

[DOI]

Elaheh Mohammadi