2 citations · 4 across the 3 of their papers we have counts for
5 papers
Utility-Fairness Trade-Offs and How to Find Them
Sepehr Dehdashtian, Bashir Sadeghi, Vishnu Naresh Boddeti
When building classification systems with demographic fairness considerations, there are two objectives to satisfy: 1) maximizing utility for the specific task and 2) ensuring fair…
Adversarial Representation Learning With Closed-Form Solvers
Bashir Sadeghi, Lan Wang, Vishnu Naresh Boddeti
Adversarial representation learning aims to learn data representations for a target task while removing unwanted sensitive information at the same time. Existing methods learn mode…
On Characterizing the Trade-off in Invariant Representation Learning
Bashir Sadeghi, Sepehr Dehdashtian, Vishnu Boddeti
Many applications of representation learning, such as privacy preservation, algorithmic fairness, and domain adaptation, desire explicit control over semantic information being dis…
On the Global Optima of Kernelized Adversarial Representation Learning
Bashir Sadeghi, Runyi Yu, Vishnu Naresh Boddeti
Adversarial representation learning is a promising paradigm for obtaining data representations that are invariant to certain sensitive attributes while retaining the information ne…
Constrained Sampling: Optimum Reconstruction in Subspace with Minimax Regret Constraint
Bashir Sadeghi, Runyi Yu, Vishnu Naresh Boddeti
This paper considers the problem of optimum reconstruction in generalized sampling-reconstruction processes (GSRPs). We propose constrained GSRP, a novel framework that minimizes t…