Cuong Tran

According to our database, Cuong Tran authored at least 20 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

The Data Minimization Principle in Machine Learning.

Prakhar Ganesh, Cuong Tran, Reza Shokri, Ferdinando Fioretto

CoRR, 2024

Low-rank finetuning for LLMs: A fairness perspective.

Saswat Das, Marco Romanelli, Cuong Tran, Zarreen Reza, Bhavya Kailkhura, Ferdinando Fioretto

CoRR, 2024

On the Effects of Fairness to Adversarial Vulnerability.

Cuong Tran, Keyu Zhu, Pascal Van Hentenryck, Ferdinando Fioretto

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

On The Fairness Impacts of Hardware Selection in Machine Learning.

Sree Harsha Nelaturu, Nishaanth Kanna Ravichandran, Cuong Tran, Sara Hooker, Ferdinando Fioretto

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Personalized Privacy Auditing and Optimization at Test Time.

Cuong Tran, Ferdinando Fioretto

CoRR, 2023

Data Minimization at Inference Time.

Cuong Tran, Ferdinando Fioretto

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles.

Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

On the Fairness Impacts of Private Ensembles Models.

Cuong Tran, Ferdinando Fioretto

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

2022

Fairness Increases Adversarial Vulnerability.

Cuong Tran, Keyu Zhu, Ferdinando Fioretto, Pascal Van Hentenryck

CoRR, 2022

Pruning has a disparate impact on model accuracy.

Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, Rakshit Naidu

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

Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey.

Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, Keyu Zhu

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

2021

A Fairness Analysis on Private Aggregation of Teacher Ensembles.

Cuong Tran, My H. Dinh, Kyle Beiter, Ferdinando Fioretto

CoRR, 2021

Differentially Private Deep Learning under the Fairness Lens.

Cuong Tran, My H. Dinh, Ferdinando Fioretto

CoRR, 2021

A Privacy-Preserving and Trustable Multi-agent Learning Framework.

Anudit Nagar, Cuong Tran, Ferdinando Fioretto

CoRR, 2021

Decision Making with Differential Privacy under a Fairness Lens.

Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck

CoRR, 2021

Differentially Private Empirical Risk Minimization under the Fairness Lens.

Cuong Tran, My H. Dinh, Ferdinando Fioretto

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

Decision Making with Differential Privacy under a Fairness Lens.

Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck, Zhiyan Yao

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Privacy-Preserving and Accountable Multi-agent Learning.

Anudit Nagar, Cuong Tran, Ferdinando Fioretto

Proceedings of the AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems, 2021

Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach.

Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Lagrangian Duality for Constrained Deep Learning.

Ferdinando Fioretto, Pascal Van Hentenryck, Terrence W. K. Mak, Cuong Tran, Federico Baldo, Michele Lombardi

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, 2020