5 papers
Causal Discovery Should Embrace the Wisdom of the Crowd
Ryan Feng Lin, Yuantao Wei, Huiling Liao +2
This paper argues for recognizing an emerging paradigm of causal learning by wisdom of the crowd. Recent developments in government, industry, and research point to the rise of dec…
Evaluating GFlowNet from partial episodes for stable and flexible policy-based training
Puhua Niu, Shili Wu, Xiaoning Qian
Generative Flow Networks (GFlowNets) were developed to learn policies for efficiently sampling combinatorial candidates by interpreting their generative processes as trajectories i…
Causal Bayesian Optimization via Exogenous Distribution Learning
Shaogang Ren, Zihao Wang, Yuzhou Chen +1
Maximizing a target variable as an operational objective within a structural causal model is a fundamental problem. Causal Bayesian Optimization (CBO) approaches typically achieve…
InvarGC: Invariant Granger Causality for Heterogeneous Interventional Time Series under Latent Confounding
Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1
Granger causality is widely used for causal structure discovery in complex systems from multivariate time series data. Traditional Granger causality tests based on linear models of…
Dynamic Incremental Optimization for Best Subset Selection
Shaogang Ren, Xiaoning Qian
Best subset selection is considered the `gold standard' for many sparse learning problems. A variety of optimization techniques have been proposed to attack this non-smooth non-con…