6 citations · 8 across the 7 of their papers we have counts for
7 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…
Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity
Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1
Granger causality characterizes directed predictive dependencies in multivariate time series, but recovering such dependencies becomes challenging in the presence of latent confoun…
Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data
Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1
Granger causality, commonly used for inferring causal structures from time series data, has been adopted in widespread applications across various fields due to its intuitive expla…
Towards Invariant Time Series Forecasting in Smart Cities
Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1
In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban p…
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…
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…