Publications

(*) denotes equal

  1. My H. Dinh, James Kotary, Ferdinando Fioretto. End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty, UAI, 2024.
  2. My H. Dinh, James Kotary, Ferdinando Fioretto. Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages, In ACM FaccT, 2024.
  3. James Kotary, Jacob Christopher, My H. Dinh, and Ferdinando Fioretto. Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization, CoRR abs/2301.12047, 2023.
  4. My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian, Kyri Baker. An Analysis of the Reliability of AC Optimal Power Flow Deep Learning Proxies, In IEEE PES Innovative Smart Grid Technologies 2023.
  5. James Kotary, My H. Dinh, Ferdinando Fioretto. Backpropagation of Unrolled Solvers with Folded Optimization, In International Joint Conference on Artificial Intelligence (IJCAI) 2023.
  6. My H. Dinh, Ferdinando Fioretto. Context-Aware Differential Privacy for Language Modeling, CoRR abss/2301.12288, 2023
  7. Cuong Tran, My H. Dinh, Kyle Beiter, Ferdinando Fioretto. A fairness analysis on private aggregation of teacher ensembles, AAAI Workshop on Privacy Preserving Artificial Intelligence (PPAI)–at AAAI, 2022
  8. 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.
  9. Mostafa Mohammadian, Kyri Baker, My H. Dinh, and Ferdinando Fioretto. Learning Solutions for Intertemporal Power Systems Optimization with Recurrent Neural Networks, In 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2022.