Search Results for author: Ashkan Rezaei

Found 5 papers, 2 papers with code

Fairness for Robust Learning to Rank

no code implementations12 Dec 2021 Omid Memarrast, Ashkan Rezaei, Rizal Fathony, Brian Ziebart

While conventional ranking systems focus solely on maximizing the utility of the ranked items to users, fairness-aware ranking systems additionally try to balance the exposure for different protected attributes such as gender or race.

Fairness Learning-To-Rank

Robust Fairness under Covariate Shift

1 code implementation11 Oct 2020 Ashkan Rezaei, Anqi Liu, Omid Memarrast, Brian Ziebart

We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same.

Fairness

Fairness for Robust Log Loss Classification

1 code implementation10 Mar 2019 Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian Ziebart

Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications.

Classification Decision Making +3

Distributionally Robust Graphical Models

no code implementations NeurIPS 2018 Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri, Xinhua Zhang, Brian D. Ziebart

Our approach enjoys both the flexibility of incorporating customized loss metrics into its design as well as the statistical guarantee of Fisher consistency.

Structured Prediction

Adversarial Structured Prediction for Multivariate Measures

no code implementations20 Dec 2017 Hong Wang, Ashkan Rezaei, Brian D. Ziebart

Many predicted structured objects (e. g., sequences, matchings, trees) are evaluated using the F-score, alignment error rate (AER), or other multivariate performance measures.

named-entity-recognition Named Entity Recognition +3

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