Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. - Austin Tripp; Erik A. Daxberger; José Miguel Hernández-Lobato (NeurIPS, 2020)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Bojana Rankovic; Yuanqi Du; Arian R. Jamasb; Julius Schwartz; Austin Tripp; Gregory Kell; Anthony Bourached; et al. (CoRR, 2022)

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. - Miguel García-Ortegón; Gregor N. C. Simm; Austin J. Tripp; José Miguel Hernández-Lobato; Andreas Bender 002; Sergio Bacallado (J. Chem. Inf. Model., 2022)

Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. - Wenlin Chen; Austin Tripp; José Miguel Hernández-Lobato (ICLR, 2023)

Retrosynthetic Planning with Dual Value Networks. - Guoqing Liu; Di Xue; Shufang Xie 003; Yingce Xia; Austin Tripp; Krzysztof Maziarz; Marwin H. S. Segler; Tao Qin 001; Zongzhang Zhang; Tie-Yan Liu (ICML, 2023)

GAUCHE: A Library for Gaussian Processes in Chemistry. - Ryan-Rhys Griffiths; Leo Klarner; Henry B. Moss; Aditya Ravuri; Sang Truong; Yuanqi Du; Samuel Stanton; Gary Tom; Bojana Rankovic; Arian Rokkum Jamasb; Aryan Deshwal; Julius Schwartz; et al. (NeurIPS, 2023)

Tanimoto Random Features for Scalable Molecular Machine Learning. - Austin Tripp; Sergio Bacallado; Sukriti Singh; José Miguel Hernández-Lobato (NeurIPS, 2023)

Genetic algorithms are strong baselines for molecule generation. - Austin Tripp; José Miguel Hernández-Lobato (CoRR, 2023)

Re-evaluating Retrosynthesis Algorithms with Syntheseus. - Krzysztof Maziarz; Austin Tripp; Guoqing Liu; Megan Stanley; Shufang Xie 003; Piotr Gainski; Philipp Seidl; Marwin H. S. Segler (CoRR, 2023)

Stochastic Gradient Descent for Gaussian Processes Done Right. - Jihao Andreas Lin; Shreyas Padhy; Javier Antorán; Austin Tripp; Alexander Terenin; Csaba Szepesvári; José Miguel Hernández-Lobato; David Janz (ICLR, 2024)

Retro-fallback: retrosynthetic planning in an uncertain world. - Austin Tripp; Krzysztof Maziarz; Sarah Lewis; Marwin H. S. Segler; José Miguel Hernández-Lobato (ICLR, 2024)

Diagnosing and fixing common problems in Bayesian optimization for molecule design. - Austin Tripp; José Miguel Hernández-Lobato (CoRR, 2024)

Batched Bayesian optimization with correlated candidate uncertainties. - Jenna C. Fromer; Runzhong Wang; Mrunali Manjrekar; Austin Tripp; José Miguel Hernández-Lobato; Connor W. Coley (CoRR, 2024)

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach. - Jingyi Zhao 002; Yuxuan Ou; Austin Tripp; Morteza Rasoulianboroujeni; José Miguel Hernández-Lobato (CoRR, 2024)

A Deep Generative Model for the Design of Synthesizable Ionizable Lipids. - Yuxuan Ou; Jingyi Zhao 002; Austin Tripp; Morteza Rasoulianboroujeni; José Miguel Hernández-Lobato (CoRR, 2024)

Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum. - Jenna C. Fromer; Runzhong Wang; Mrunali Manjrekar; Austin Tripp; José Miguel Hernández-Lobato; Connor W. Coley (J. Chem. Inf. Model., 2025)

Bayesian Optimization for Molecules Should Be Pareto-Aware. - Anabel Yong; Austin Tripp; Layla Hosseini-Gerami; Brooks Paige (CoRR, 2025)