156 citations · 223 across the 11 of their papers we have counts for
8 papers
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows
Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as ins…
Achieving Counterfactual Fairness with Imperfect Structural Causal Model
Tri Dung Duong, Qian Li, Guandong Xu
Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i…
On the spectral radius of the -lazy Markov chain
Li Qian, Zhenyao Sun
We consider an -lazy operation on an irreducible Markov transition probability with state space where and . For each and ,…
FedMCSA: Personalized Federated Learning via Model Components Self-Attention
Qi Guo, Yong Qi, Saiyu Qi +2
Federated learning (FL) facilitates multiple clients to jointly train a machine learning model without sharing their private data. However, Non-IID data of clients presents a tough…
Reinforced Path Reasoning for Counterfactual Explainable Recommendation
Xiangmeng Wang, Qian Li, Dianer Yu +1
Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. Existing counterfact…
Deep Treatment-Adaptive Network for Causal Inference
Qian Li, Zhichao Wang, Shaowu Liu +2
Causal inference is capable of estimating the treatment effect (i.e., the causal effect of treatment on the outcome) to benefit the decision making in various domains. One fundamen…