13 citations · 24 across the 5 of their papers we have counts for
5 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…
Bayesian Inference of Contextual Bandit Policies via Empirical Likelihood
Jiangrong Ouyang, Mingming Gong, Howard Bondell
Policy inference plays an essential role in the contextual bandit problem. In this paper, we use empirical likelihood to develop a Bayesian inference method for the joint analysis…
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models
Erdun Gao, Ignavier Ng, Mingming Gong +5
State-of-the-art causal discovery methods usually assume that the observational data is complete. However, the missing data problem is pervasive in many practical scenarios such as…
FedDAG: Federated DAG Structure Learning
Erdun Gao, Junjia Chen, Li Shen +3
To date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server. However, due to the consideration of privacy protection, d…