5 papers
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1
Causal discovery aims to uncover causal structures from data and is fundamental to scientific reasoning and intervention-based decision making. Its evaluation relies heavily on str…
HH-SAE: Discovering and Steering Hierarchical Knowledge of Complex Manifolds
Honghan Wu, Tianyan Wang, Jiacong Mi +2
Rare semantic innovations in high-dimensional, mission-critical domains are often obscured by dense background contexts, a challenge we define as \textit{feature density conflict}.…
Non-Parametric Rehearsal Learning via Conditional Mean Embeddings
Wen-Bo Du, Tian-Zuo Wang, Han-Jia Ye +1
In machine learning, a critical class of decision-related problems concerns preventing predicted undesirable outcomes, referred to as the \textit{avoiding undesired future} (AUF) p…
TabCausal: Pretraining Across Causal Environments for Tabular Causal Discovery
Zi-Rong Li, Si-Yang Liu, Tian-Zuo Wang +1
Causal discovery aims to recover directed causal relations from observational and interventional data, providing a basis for mechanistic understanding and reliable decision-making.…
New Rules for Causal Identification with Background Knowledge
Tian-Zuo Wang, Lue Tao, Zhi-Hua Zhou
Identifying causal relations is crucial for a variety of downstream tasks. In additional to observational data, background knowledge (BK), which could be attained from human expert…