Austin Tripp

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 17

Top Topics

Gaussian Process

Top Venues

Publications

Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato
Retro-fallback: retrosynthetic planning in an uncertain world. ICLR (2024)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR (2024)
Austin Tripp, José Miguel Hernández-Lobato
Diagnosing and fixing common problems in Bayesian optimization for molecule design. CoRR (2024)
Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato
Retro-fallback: retrosynthetic planning in an uncertain world. CoRR (2023)
Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gainski, Philipp Seidl, Marwin H. S. Segler
Re-evaluating Retrosynthesis Algorithms with Syntheseus. CoRR (2023)
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
GAUCHE: A Library for Gaussian Processes in Chemistry. NeurIPS (2023)
Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR (2023)
Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato
Tanimoto Random Features for Scalable Molecular Machine Learning. CoRR (2023)
Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato
Tanimoto Random Features for Scalable Molecular Machine Learning. NeurIPS (2023)
Austin Tripp, José Miguel Hernández-Lobato
Genetic algorithms are strong baselines for molecule generation. CoRR (2023)
Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. ICLR (2023)
Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin H. S. Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu
Retrosynthetic Planning with Dual Value Networks. ICML (2023)
Guoqing Liu, Di Xue, Shufang Xie, Yingce Xia, Austin Tripp, Krzysztof Maziarz, Marwin H. S. Segler, Tao Qin, Zongzhang Zhang, Tie-Yan Liu
Retrosynthetic Planning with Dual Value Networks. CoRR (2023)
Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Bojana Rankovic, Yuanqi Du, Arian Jamasb, Julius Schwartz, Austin Tripp, Gregory Kell, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha A. Lee, Philippe Schwaller, Jian Tang
GAUCHE: A Library for Gaussian Processes in Chemistry. CoRR (2022)
Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. CoRR (2022)
Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. NeurIPS (2020)
Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. CoRR (2020)