Kan Ren

Is Risk-Sensitive Reinforcement Learning Properly Resolved?

2023 · arXiv (Cornell University)

Due to the nature of risk management in learning applicable policies, risk-sensitive reinforcement learning (RSRL) has been realized as an important direction. RSRL is usually achieved by learning risk-sensitive objectives characterized by various risk measures, …

Dissecting and Mitigating Diffusion Bias via Mechanistic Interpretability

2025

Diffusion models have demonstrated impressive capabilities in synthesizing diverse content. However, despite their high-quality outputs, these models often perpetuate social biases, including those related to gender and race. These biases can potentially contribute to harmful …

Product-Based Neural Networks for User Response Prediction

2016

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage inmany Web applications including recommender systems, websearch and online advertising. The data in those applicationsis mostly categorical and …

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

2019

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have …