5 citations · 6 across the 10 of their papers we have counts for
10 papers
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…
Online Decision Deferral under Budget Constraints
Mirabel Reid, Tom Sühr, Claire Vernade +1
Machine Learning (ML) models are increasingly used to support or substitute decision making. In applications where skilled experts are a limited resource, it is crucial to reduce t…
A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
Mohammad-Amin Charusaie, Samira Samadi
Learn-to-Defer is a paradigm that enables learning algorithms to work not in isolation but as a team with human experts. In this paradigm, we permit the system to defer a subset of…
A Dynamic Model of Performative Human-ML Collaboration: Theory and Empirical Evidence
Tom Sühr, Samira Samadi, Chiara Farronato
Machine learning (ML) models are increasingly used in various applications, from recommendation systems in e-commerce to diagnosis prediction in healthcare. In this paper, we prese…
Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics
Ahmad-Reza Ehyaei, Ali Shirali, Samira Samadi
Counterfactual explanations provide individuals with cost-optimal recommendations to achieve their desired outcomes. However, when a significant number of individuals seek similar…
Challenging the Validity of Personality Tests for Large Language Models
Tom Sühr, Florian E. Dorner, Samira Samadi +1
With large language models (LLMs) like GPT-4 appearing to behave increasingly human-like in text-based interactions, it has become popular to attempt to evaluate personality traits…
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi
Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often studied in isolation. Adversarial perturba…