5 citations · 6 across the 10 of their papers we have counts for
4 papers · 1 filter
Designing Ambiguity Sets for Distributionally Robust Optimization Using Structural Causal Optimal Transport
Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi
Distributionally robust optimization tackles out-of-sample issues like overfitting and distribution shifts by adopting an adversarial approach over a range of possible data distrib…
Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairness
Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi
In recent years, Wasserstein Distributionally Robust Optimization (DRO) has garnered substantial interest for its efficacy in data-driven decision-making under distributional uncer…
From Fragile to Certified: Wasserstein Audits of Group Fairness Under Distribution Shift
Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi
Group-fairness metrics (e.g., equalized odds) can vary sharply across resamples and are especially brittle under distribution shift, undermining reliable audits. We propose a Wasse…
Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead
Tom Sühr, Florian E. Dorner, Olawale Salaudeen +2
Large Language Models (LLMs) have achieved remarkable results on a range of standardized tests originally designed to assess human cognitive and psychological traits, such as intel…