8 citations · 16 across the 9 of their papers we have counts for
9 papers
From Fewer Samples to Fewer Bits: Reframing Dataset Distillation as Joint Optimization of Precision and Compactness
My H. Dinh, Aditya Sant, Akshay Malhotra +2
Dataset Distillation (DD) compresses large datasets into compact synthetic ones that maintain training performance. However, current methods mainly target sample reduction, with li…
End-to-End Optimization and Learning of Fair Court Schedules
My H Dinh, James Kotary, Lauryn P. Gouldin +2
Criminal courts across the United States handle millions of cases every year, and the scheduling of those cases must accommodate a diverse set of constraints, including the prefere…
End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
My H Dinh, James Kotary, Ferdinando Fioretto
Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferre…
Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
My H. Dinh, James Kotary, Ferdinando Fioretto
Learning to Rank (LTR) is one of the most widely used machine learning applications. It is a key component in platforms with profound societal impacts, including job search, health…
Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization
James Kotary, Jacob Christopher, My H Dinh +1
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this sett…
Context-Aware Differential Privacy for Language Modeling
My H. Dinh, Ferdinando Fioretto
The remarkable ability of language models (LMs) has also brought challenges at the interface of AI and security. A critical challenge pertains to how much information these models…
Backpropagation of Unrolled Solvers with Folded Optimization
James Kotary, My H. Dinh, Ferdinando Fioretto
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this sett…
Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions
My H. Dinh, Ferdinando Fioretto, Mostafa Mohammadian +1
Optimal Power Flow (OPF) is a fundamental problem in power systems. It is computationally challenging and a recent line of research has proposed the use of Deep Neural Networks (DN…
A Fairness Analysis on Private Aggregation of Teacher Ensembles
Cuong Tran, My H. Dinh, Kyle Beiter +1
The Private Aggregation of Teacher Ensembles (PATE) is an important private machine learning framework. It combines multiple learning models used as teachers for a student model th…