Differentially Private Empirical Risk Minimization under the Fairness Lens. - Cuong Tran 007; My H. Dinh; Ferdinando Fioretto (NeurIPS, 2021)

Differentially Private Deep Learning under the Fairness Lens. - Cuong Tran 007; My H. Dinh; Ferdinando Fioretto (CoRR, 2021)

A Fairness Analysis on Private Aggregation of Teacher Ensembles. - Cuong Tran 007; My H. Dinh; Kyle Beiter; Ferdinando Fioretto (CoRR, 2021)

Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions. - My H. Dinh; Ferdinando Fioretto; Mostafa Mohammadian; Kyri Baker (CoRR, 2021)

Backpropagation of Unrolled Solvers with Folded Optimization. - James Kotary; My H. Dinh; Ferdinando Fioretto (IJCAI, 2023)

Folded Optimization for End-to-End Model-Based Learning. - James Kotary; My H. Dinh; Ferdinando Fioretto (CoRR, 2023)

Context-Aware Differential Privacy for Language Modeling. - My H. Dinh; Ferdinando Fioretto (CoRR, 2023)

Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization. - James Kotary; Jacob Christopher; My H. Dinh; Ferdinando Fioretto (CoRR, 2023)

Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages. - My H. Dinh; James Kotary; Ferdinando Fioretto (FAccT, 2024)

End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty. - My H. Dinh; James Kotary; Ferdinando Fioretto (UAI, 2024)

End-to-End Optimization and Learning of Fair Court Schedules. - My H. Dinh; James Kotary; Lauryn P. Gouldin; William Yeoh 001; Ferdinando Fioretto (CoRR, 2024)