Privacy and Fairness in Recommender Systems via Adversarial Training of User Representations

no code implementations • 10 Jul 2018 • Yehezkel S. Resheff, Yanai Elazar, Moni Shahar, Oren Sar Shalom

Latent factor models for recommender systems represent users and items as low dimensional vectors.

Decision Making Fairness +1

Fusing Multifaceted Transaction Data for User Modeling and Demographic Prediction

no code implementations • 19 Dec 2017 • Yehezkel S. Resheff, Moni Shahar

Inferring user characteristics such as demographic attributes is of the utmost importance in many user-centric applications.

A Two-Step Disentanglement Method

1 code implementation • CVPR 2018 • Naama Hadad, Lior Wolf, Moni Shahar

First, the part of the data that is correlated with the labels is extracted by training a classifier.

Disentanglement Vocal Bursts Valence Prediction

Decomposing Isotonic Regression for Efficiently Solving Large Problems

no code implementations • NeurIPS 2010 • Ronny Luss, Saharon Rosset, Moni Shahar

A new algorithm for isotonic regression is presented based on recursively partitioning the solution space.

regression