(*) denotes equal contribution
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, Rakshit Naidu. Pruning has a disparate impact on model accuracy. In NeurIPS 2022.
Ferdinando Fioretto, Cuong Tran *, Keyu Zhu, and Pascal Van Hentenryck. Differential privacy and fairness in decisions and learning tasks: A survey. In IJCAI Survey Track, 2022
Cuong Tran, Ferdinando Fioretto, and Pascal Van Hentenryck. Sf-pate: Scalable, fair, and private aggregation of teacher ensembles. Submitted to ECML PKDD, 2022.
Cuong Tran, My H. Dinh, Kyle Beiter, and Ferdinando Fioretto. A fairness analysis on private aggregation of teacher ensembles. Submitted to AAAI, 2022.
Cuong Tran, My H. Dinh, and Ferdinando Fioretto. Differentially private deep learning under the fairness lens. In Advances in Neural Information Processing Systems (NeurIPS), 2021.
Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck, and Zhiyan Yao. Decision making with differential privacy under the fairness lens. In Proceedings of the International Joint Conference on Artificial Intelligence(IJCAI), 2021.
Anudit Nagar, Cuong Tran, and Ferdinando Fioretto. A privacy-preserving and accountable multiagent learning framework. In Proceedings of International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2021
Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck “Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach”. In AAAI 2021
Ferdinando Fioretto, Pascal Van Hentenryck, Terrence W.K. Mak, Cuong Tran, Federico Baldo, Michele Lombardi. “Lagrangian Duality for Constrained Deep Learning” . In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2020.
Cuong Tran, Vladimir Pavlovic, Robert Kopp. Gaussian Process for Noisy Inputs with Ordering Constraints, available on ArXiv 2015.