3 citations · 7 across the 9 of their papers we have counts for
9 papers
LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation
Zijian Shen, Taijie Chen, Bin Zhou +2
Tabular data generation supports analysis and decision-making when target-domain data are scarce, yet collecting complete target samples is often costly. A practical but underexplo…
Synergizing Deconfounding and Temporal Generalization For Time-series Counterfactual Outcome Estimation
Yiling Liu, Juncheng Dong, Chen Fu +4
Estimating counterfactual outcomes from time-series observations is crucial for effective decision-making, e.g. when to administer a life-saving treatment, yet remains significantl…
Deep Causal Inference for Point-referenced Spatial Data with Continuous Treatments
Ziyang Jiang, Zach Calhoun, Yiling Liu +2
Causal reasoning is often challenging with spatial data, particularly when handling high-dimensional inputs. To address this, we propose a neural network (NN) based framework integ…
Assessing the Potential of PlanetScope Satellite Imagery to Estimate Particulate Matter Oxidative Potential
Ian Hough, Loïc Argentier, Ziyang Jiang +6
Oxidative potential (OP), which measures particulate matter's (PM) capacity to induce oxidative stress in the lungs, is increasingly recognized as an indicator of PM toxicity. Sinc…
Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation
Lei Duan, Ziyang Jiang, David Carlson
Fusing abundant satellite data with sparse ground measurements constitutes a major challenge in climate modeling. To address this, we propose a strategy to augment the training dat…
Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators
Ziyang Jiang, Yiling Liu, Michael H. Klein +5
Causal mediation analysis (CMA) is a powerful method to dissect the total effect of a treatment into direct and mediated effects within the potential outcome framework. This is imp…
Domain Adaptation via Rebalanced Sub-domain Alignment
Yiling Liu, Juncheng Dong, Ziyang Jiang +5
Unsupervised domain adaptation (UDA) is a technique used to transfer knowledge from a labeled source domain to a different but related unlabeled target domain. While many UDA metho…
Estimating Causal Effects using a Multi-task Deep Ensemble
Ziyang Jiang, Zhuoran Hou, Yiling Liu +3
A number of methods have been proposed for causal effect estimation, yet few have demonstrated efficacy in handling data with complex structures, such as images. To fill this gap,…
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel
Ziyang Jiang, Tongshu Zheng, Yiling Liu +1
It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to…