5 papers
Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
Wenhao Mu, Facundo Yan, Anik Mumssen +2
Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual…
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
Lingkai Kong, Yuanqi Du, Wenhao Mu +8
Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed…
Counterfactual Probabilistic Diffusion with Expert Models
Wenhao Mu, Zhi Cao, Mehmed Uludag +1
Predicting counterfactual distributions in complex dynamical systems is essential for scientific modeling and decision-making in domains such as public health and medicine. However…
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
Lingkai Kong, Haotian Sun, Yuchen Zhuang +3
Graph neural networks (GNNs) are powerful tools on graph data. However, their predictions are mis-calibrated and lack interpretability, limiting their adoption in critical applicat…
DF2: Distribution-Free Decision-Focused Learning
Lingkai Kong, Wenhao Mu, Jiaming Cui +4
Decision-focused learning (DFL), which differentiates through the KKT conditions, has recently emerged as a powerful approach for predict-then-optimize problems. However, under pro…