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Tianci Li

2 accepted papers

2025

Gradient-based Causal Feature Selection

IJCAI 2025

Causal feature selection leverages causal discovery techniques to identify critical features associated with a target variable using observational data. Traditional methodologies primarily rely on constraint-based or score-based techniques, which are fraught with limitations. For example, conditiona

2022

Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning

NeurIPS 2022accept

Reinforcement learning (RL) is a general framework for modeling sequential decision making problems, at the core of which lies the dilemma of exploitation and exploration. An agent failing to explore systematically will inevitably fail to learn efficiently. Optimism in the face of uncertainty (OFU)…

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