10 papers
TimeLAVA: Learning-Agnostic Valuation for Time Series Data
Wenqin Liu, Weizhi Quan, Aoqi Zuo +5
Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time series in critical domains…
Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
Xinyu Li, Yuanyuan Wang, Haoxuan Li +7
Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making. However, different causal discovery algorithms can prod…
Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction
Wenkang Jiang, Yuhang Liu, Erdun Gao +3
Single-cell perturbation prediction aims to infer how cells respond to unseen interventions and to achieve out-of-distribution (OOD) generalization, providing a computational route…
What Makes a Representation Good for Single-Cell Perturbation Prediction?
Wenkang Jiang, Yuhang Liu, Yichao Cai +5
Single-cell perturbation modeling is fundamental for understanding and predicting cellular responses to genetic perturbations. However, existing approaches, from causal representat…
Uncertainty Propagation in LLM-Based Systems
Boming Xia, Liming Zhu, Erdun Gao +3
Uncertainty in large language model (LLM)-based systems is often studied at the level of a single model output, yet deployed LLM applications are compound systems in which uncertai…
Observationally Informed Adaptive Causal Experimental Design
Erdun Gao, Liang Zhang, Jake Fawkes +5
Randomized Controlled Trials (RCTs) represent the gold standard for causal inference yet remain a scarce resource. While large-scale observational data is often available, it is ut…