Search Results for author: Tomo Lazovich

Found 5 papers, 1 papers with code

Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems

no code implementations12 Sep 2022 Amanda Bower, Kristian Lum, Tomo Lazovich, Kyra Yee, Luca Belli

Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user.

Fairness Recommendation Systems

Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics

no code implementations3 Feb 2022 Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszar, Rumman Chowdhury

We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.

Learning to Repair Software Vulnerabilities with Generative Adversarial Networks

no code implementations NeurIPS 2018 Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin

Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections.

Code Repair Translation

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