Samira Samadi
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View article: Designing Ambiguity Sets for Distributionally Robust Optimization Using Structural Causal Optimal Transport
Designing Ambiguity Sets for Distributionally Robust Optimization Using Structural Causal Optimal Transport Open
Distributionally robust optimization tackles out-of-sample issues like overfitting and distribution shifts by adopting an adversarial approach over a range of possible data distributions, known as the ambiguity set. To balance conservatism…
View article: From Fragile to Certified: Wasserstein Audits of Group Fairness Under Distribution Shift
From Fragile to Certified: Wasserstein Audits of Group Fairness Under Distribution Shift Open
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 Wasserstein distributionally robust framework that certifies wor…
View article: Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairness
Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairness Open
In recent years, Wasserstein Distributionally Robust Optimization (DRO) has garnered substantial interest for its efficacy in data-driven decision-making under distributional uncertainty. However, limited research has explored the applicat…
View article: Fairness for the People, by the People: Minority Collective Action
Fairness for the People, by the People: Minority Collective Action Open
Machine learning models often preserve biases present in training data, leading to unfair treatment of certain minority groups. Despite an array of existing firm-side bias mitigation techniques, they typically incur utility costs and requi…
View article: Designing Ambiguity Sets for Distributionally Robust Optimization Using Structural Causal Optimal Transport
Designing Ambiguity Sets for Distributionally Robust Optimization Using Structural Causal Optimal Transport Open
Distributionally robust optimization tackles out-of-sample issues like overfitting and distribution shifts by adopting an adversarial approach over a range of possible data distributions, known as the ambiguity set. To balance conservatism…
View article: Online Decision Deferral under Budget Constraints
Online Decision Deferral under Budget Constraints Open
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 their burden and automate decisions when the performance of …
View article: A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems
A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems Open
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 its tasks to the expert. Although there are currently syst…
View article: The Role of Learning Algorithms in Collective Action
The Role of Learning Algorithms in Collective Action Open
Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms. While previous research has concentrated on assessing the impact of collectives against Bayes (sub-)optim…
View article: Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces Open
As responsible AI gains importance in machine learning algorithms, properties like fairness, adversarial robustness, and causality have received considerable attention in recent years. However, despite their individual significance, there …
View article: Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics
Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics Open
Counterfactual explanations provide individuals with cost-optimal recommendations to achieve their desired outcomes. However, when a significant number of individuals seek similar state modifications, this individual-centric approach can i…
View article: Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness Open
Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often studied in isolation. Adversarial perturbation, used to identify vulnerabilities in models, and indiv…
View article: Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces Open
As responsible AI gains importance in machine learning algorithms, properties such as fairness, adversarial robustness, and causality have received considerable attention in recent years. However, despite their individual significance, the…
View article: Sample Efficient Learning of Predictors that Complement Humans
Sample Efficient Learning of Predictors that Complement Humans Open
One of the goals of learning algorithms is to complement and reduce the burden on human decision makers. The expert deferral setting wherein an algorithm can either predict on its own or defer the decision to a downstream expert helps acco…
View article: FPT Approximation for Fair Minimum-Load Clustering
FPT Approximation for Fair Minimum-Load Clustering Open
In this paper, we consider the Minimum-Load k-Clustering/Facility Location (MLkC) problem where we are given a set P of n points in a metric space that we have to cluster and an integer k > 0 that denotes the number of clusters. Additional…
View article: Pairwise Fairness for Ordinal Regression
Pairwise Fairness for Ordinal Regression Open
We initiate the study of fairness for ordinal regression. We adapt two fairness notions previously considered in fair ranking and propose a strategy for training a predictor that is approximately fair according to either notion. Our predic…
View article: Socially Fair k-Means Clustering
Socially Fair k-Means Clustering Open
We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e.g., demographic groups). Such biased clusterings can have…
View article: Fair k-Means Clustering
Fair k-Means Clustering Open
We show that the popular $k$-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e.g., demographic groups). Such biased clusterings can ha…
View article: Multi-Criteria Dimensionality Reduction with Applications to Fairness
Multi-Criteria Dimensionality Reduction with Applications to Fairness Open
Dimensionality reduction is a classical technique widely used for data analysis. One foundational instantiation is Principal Component Analysis (PCA), which minimizes the average reconstruction error. In this paper, we introduce the "multi…
View article: Fair Dimensionality Reduction and Iterative Rounding for SDPs
Fair Dimensionality Reduction and Iterative Rounding for SDPs Open
We model dimensionality reduction as an optimization problem. A central example is the fair PCA problem: the input data is divided into $k$ groups, and the goal is to find a single $d$-dimensional representation for all groups for which t…
View article: Guarantees for Spectral Clustering with Fairness Constraints
Guarantees for Spectral Clustering with Fairness Constraints Open
Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion proposed by Chierichetti et al. (2017). According to this noti…
View article: The Price of Fair PCA: One Extra dimension
The Price of Fair PCA: One Extra dimension Open
We investigate whether the standard dimensionality reduction technique of PCA inadvertently produces data representations with different fidelity for two different populations. We show on several real-world data sets, PCA has higher recons…
View article: The Price of Fair PCA: One Extra Dimension
The Price of Fair PCA: One Extra Dimension Open
We investigate whether the standard dimensionality reduction technique of PCA inadvertently produces data representations with different fidelity for two different populations. We show on several real-world data sets, PCA has higher recons…
View article: Usability of Humanly Computable Passwords
Usability of Humanly Computable Passwords Open
Reusing passwords across multiple websites is a common practice that compromises security. Recently, Blum and Vempala have proposed password strategies to help people calculate, in their heads, passwords for different sites without depende…
View article: Usability of Humanly Computable Passwords
Usability of Humanly Computable Passwords Open
Reusing passwords across multiple websites is a common practice that compromises security. Recently, Blum and Vempala have proposed password strategies to help people calculate, in their heads, passwords for different sites without depende…