29 citations · 101 across the 38 of their papers we have counts for
5 papers · 1 filter
Efficient Differentiable Causal Discovery via Reliable Super-Structure Learning
Pingchuan Ma, Qixin Zhang, Shuai Wang +1
Recently, differentiable causal discovery has emerged as a promising approach to improve the accuracy and efficiency of existing methods. However, when applied to high-dimensional…
Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)
Pingchuan Ma, Rui Ding, Qiang Fu +4
Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due…
Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning
Ao Sun, Yuanyuan Yuan, Pingchuan Ma +1
Concept Bottleneck Models (CBMs) aim to deliver interpretable and interventionable predictions by bridging features and labels with human-understandable concepts. While recent CBMs…
Towards Practical Federated Causal Structure Learning
Zhaoyu Wang, Pingchuan Ma, Shuai Wang
Understanding causal relations is vital in scientific discovery. The process of causal structure learning involves identifying causal graphs from observational data to understand s…
Causality-Aided Trade-off Analysis for Machine Learning Fairness
Zhenlan Ji, Pingchuan Ma, Shuai Wang +1
There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understandi…