Giuseppe Vietri

According to our database, Giuseppe Vietri authored at least 12 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2023

Generating Private Synthetic Data with Genetic Algorithms.

Terrance Liu, Jingwu Tang, Giuseppe Vietri, Steven Wu

Proceedings of the International Conference on Machine Learning, 2023

2022

Confidence-Ranked Reconstruction of Census Microdata from Published Statistics.

Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

CoRR, 2022

Private Synthetic Data for Multitask Learning and Marginal Queries.

Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu

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

Improved Regret for Differentially Private Exploration in Linear MDP.

Dung Daniel T. Ngo, Giuseppe Vietri, Steven Wu

Proceedings of the International Conference on Machine Learning, 2022

2021

Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods.

Terrance Liu, Giuseppe Vietri, Steven Wu

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

Leveraging Public Data for Practical Private Query Release.

Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu

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

2020

EXP4-DFDC: A Non-Stochastic Multi-Armed Bandit for Cache Replacement.

Farzana Beente Yusuf, Camilo Valdes, Vitalii Stebliankin, Giuseppe Vietri, Giri Narasimhan

CoRR, 2020

New Oracle-Efficient Algorithms for Private Synthetic Data Release.

Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

Private Reinforcement Learning with PAC and Regret Guarantees.

Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

Oracle Efficient Private Non-Convex Optimization.

Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Differentially Private Objective Perturbation: Beyond Smoothness and Convexity.

Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

CoRR, 2019

2018

Driving Cache Replacement with ML-based LeCaR.

Giuseppe Vietri, Liana V. Rodriguez, Wendy A. Martinez, Steven Lyons, Jason Liu, Raju Rangaswami, Ming Zhao, Giri Narasimhan

Proceedings of the 10th USENIX Workshop on Hot Topics in Storage and File Systems, 2018