3 accepted papers
Recommender systems often rely on observational user--item interaction data, which is prone to selection bias due to users' selective interactions with items. Inverse propensity weighting and doubly robust estimators effectively mitigate selection bias under observed confounding, but are unreliable…
Recommendation systems commonly face selection bias from missing-not-at-random (MNAR) collected data. To address this bias, propensity-based methods such as inverse propensity scoring (IPS) and doubly robust (DR) estimators are widely used. In addition, many methods extend the vanilla IPS and DR to
Privacy in AI remains a topic that draws attention from researchers and the general public in recent years. As one way to implement privacy-preserving AI, differentially private learning is a framework that enables AI models to use differential privacy (DP). To achieve DP in the learning process, ex…