3 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…