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2020 – today
- 2024
[c7]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- [c6]Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Retro-fallback: retrosynthetic planning in an uncertain world. ICLR 2024 - [i11]Austin Tripp, José Miguel Hernández-Lobato:
Diagnosing and fixing common problems in Bayesian optimization for molecule design. CoRR abs/2406.07709 (2024) - 2023
- [c5]Wenlin Chen
, Austin Tripp, José Miguel Hernández-Lobato:
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. ICLR 2023 - [c4]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: 22266-22276 - [c3]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 - [c2]Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato:
Tanimoto Random Features for Scalable Molecular Machine Learning. NeurIPS 2023 - [i10]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 abs/2301.13755 (2023) - [i9]Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato:
Tanimoto Random Features for Scalable Molecular Machine Learning. CoRR abs/2306.14809 (2023) - [i8]Austin Tripp, José Miguel Hernández-Lobato:
Genetic algorithms are strong baselines for molecule generation. CoRR abs/2310.09267 (2023) - [i7]Austin Tripp, Krzysztof Maziarz, Sarah Lewis, Marwin H. S. Segler, José Miguel Hernández-Lobato:
Retro-fallback: retrosynthetic planning in an uncertain world. CoRR abs/2310.09270 (2023) - [i6]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 abs/2310.19796 (2023) - [i5]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 abs/2310.20581 (2023) - 2022
- [j1]Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, Sergio Bacallado:
DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design. J. Chem. Inf. Model. 62(15): 3486-3502 (2022) - [i4]Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato:
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction. CoRR abs/2205.02708 (2022) - [i3]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:
GAUCHE: A Library for Gaussian Processes in Chemistry. CoRR abs/2212.04450 (2022) - 2021
- [i2]Miguel García-Ortegón, Gregor N. C. Simm, Austin J. Tripp, José Miguel Hernández-Lobato, Andreas Bender, Sergio Bacallado:
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design. CoRR abs/2110.15486 (2021) - 2020
- [c1]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 - [i1]Austin Tripp, Erik A. Daxberger, José Miguel Hernández-Lobato:
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining. CoRR abs/2006.09191 (2020)
Coauthor Index
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