613 citations · 1k across the 21 of their papers we have counts for
6 papers · 1 filter
Improving Bayesian Network Structure Learning in the Presence of Measurement Error
Yang Liu, Anthony C. Constantinou, ZhiGao Guo
Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the var…
Chemical Property Prediction Under Experimental Biases
Yang Liu, Hisashi Kashima
Predicting the chemical properties of compounds is crucial in discovering novel materials and drugs with specific desired characteristics. Recent significant advances in machine le…
Physics informed deep learning for computational elastodynamics without labeled data
Chengping Rao, Hao Sun, Yang Liu
Numerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial differential equations (PDEs)…
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data
Anthony C. Constantinou, Yang Liu, Kiattikun Chobtham +2
Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case…
Physics-informed learning of governing equations from scarce data
Zhao Chen, Yang Liu, Hao Sun
Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and…
APTER: Aggregated Prognosis Through Exponential Reweighting
Kristiaan Pelckmans, Liu Yang
This paper considers the task of learning how to make a prognosis of a patient based on his/her micro-array expression levels. The method is an application of the aggregation metho…