On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms. - Nicolás García Trillos; Zachary Kaplan; Thabo Samakhoana; Daniel Sanz-Alonso (CoRR, 2017)

Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning. - Nicolás García Trillos; Zachary Kaplan; Daniel Sanz-Alonso (Entropy, 2019)

On the consistency of graph-based Bayesian semi-supervised learning and the scalability of sampling algorithms. - Nicolás García Trillos; Zachary Kaplan; Thabo Samakhoana; Daniel Sanz-Alonso (J. Mach. Learn. Res., 2020)

A Computational Physics-based Approach to Predict Unbound Brain-to-Plasma Partition Coefficient, Kp,uu. - Morgan Lawrenz; Mats Svensson; Mitsunori Kato; Karen H. Dingley; Jackson Chief Elk; Zhe Nie; Yefen Zou; Zachary Kaplan; H. Rachel Lagiakos; Hideyuki Igawa; Eric Therrien (J. Chem. Inf. Model., 2023)

Advancing material property prediction: using physics-informed machine learning models for viscosity. - Alex K. Chew; Matthew Sender; Zachary Kaplan; Anand Chandrasekaran; Jackson Chief Elk; Andrea R. Browning; H. Shaun Kwak; Mathew D. Halls; Mohammad Atif Faiz Afzal (J. Cheminformatics, 2024)