2 citations · 3 across the 3 of their papers we have counts for
6 papers
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms
Yunqi Wang, Furui Liu, Zhitang Chen +4
Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve t…
Learning to Select Cuts for Efficient Mixed-Integer Programming
Zeren Huang, Kerong Wang, Furui Liu +6
Cutting plane methods play a significant role in modern solvers for tackling mixed-integer programming (MIP) problems. Proper selection of cuts would remove infeasible solutions in…
Causal World Models by Unsupervised Deconfounding of Physical Dynamics
Minne Li, Mengyue Yang, Furui Liu +3
The capability of imagining internally with a mental model of the world is vitally important for human cognition. If a machine intelligent agent can learn a world model to create a…
Decoder-free Robustness Disentanglement without (Additional) Supervision
Yifei Wang, Dan Peng, Furui Liu +3
Adversarial Training (AT) is proposed to alleviate the adversarial vulnerability of machine learning models by extracting only robust features from the input, which, however, inevi…
Confounder Detection in High Dimensional Linear Models using First Moments of Spectral Measures
Furui Liu, Laiwan Chan
In this paper, we study the confounder detection problem in the linear model, where the target variable is predicted using its potential causes . Based…
Causal Inference on Discrete Data via Estimating Distance Correlations
Furui Liu, Laiwan Chan
In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain. By considering the distribution of the cause and the conditional…