Austin Tripp

According to our database, Austin Tripp authored at least 11 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

CoRR, 2023

Re-evaluating Retrosynthesis Algorithms with Syntheseus.

Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gainski, Philipp Seidl, Marwin H. S. Segler

CoRR, 2023

Retro-fallback: retrosynthetic planning in an uncertain world.

Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato

CoRR, 2023

Genetic algorithms are strong baselines for molecule generation.

Austin Tripp, José Miguel Hernández-Lobato

CoRR, 2023

Tanimoto Random Features for Scalable Molecular Machine Learning.

Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Retrosynthetic Planning with Dual Value Networks.

Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin H. S. Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu

Proceedings of the International Conference on Machine Learning, 2023

Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction.

Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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, Sergio Bacallado

J. Chem. Inf. Model., 2022

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, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha A. Lee, Philippe Schwaller, Jian Tang

CoRR, 2022

2020

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining.

Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020