25 citations · 43 across the 5 of their papers we have counts for
5 papers
Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well
Xin Du, Sikun Yang, Wouter Duivesteijn +1
Understanding the nuanced performance of machine learning models is essential for responsible deployment, especially in high-stakes domains like healthcare and finance. This paper…
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles
Xin Du, Subramanian Ramamoorthy, Wouter Duivesteijn +2
Local decision rules are commonly understood to be more explainable, due to the local nature of the patterns involved. With numerical optimization methods such as gradient boosting…
Causal Discovery from Incomplete Data: A Deep Learning Approach
Yuhao Wang, Vlado Menkovski, Hao Wang +2
As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs. By encod…
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
Xin Du, Lei Sun, Wouter Duivesteijn +2
Learning causal effects from observational data greatly benefits a variety of domains such as health care, education and sociology. For instance, one could estimate the impact of a…
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding
Yulong Pei, Xin Du, Jianpeng Zhang +2
Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitatio…