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Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages
Learning to Rank (LTR) is one of the most widely used machine learning applications.
Context-Aware Differential Privacy for Language Modeling
The remarkable ability of language models (LMs) has also brought challenges at the interface of AI and security.
Backpropagation of Unrolled Solvers with Folded Optimization
A central challenge in this setting is backpropagation through the solution of an optimization problem, which typically lacks a closed form.
Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions
Optimal Power Flow (OPF) is a fundamental problem in power systems.
A Fairness Analysis on Private Aggregation of Teacher Ensembles
The Private Aggregation of Teacher Ensembles (PATE) is an important private machine learning framework.
Differentially Empirical Risk Minimization under the Fairness Lens
However, it was recently observed that DP learning systems may exacerbate bias and unfairness for different groups of individuals.
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