Austin Tripp
Austin Tripp
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Cited by
Cited by
Sample-efficient optimization in the latent space of deep generative models via weighted retraining
A Tripp, E Daxberger, JM Hernández-Lobato
Advances in Neural Information Processing Systems 33, 11259-11272, 2020
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
M García-Ortegón, GNC Simm, AJ Tripp, JM Hernández-Lobato, A Bender, ...
Journal of chemical information and modeling 62 (15), 3486-3502, 2022
GAUCHE: a library for Gaussian processes in chemistry
RR Griffiths, L Klarner, H Moss, A Ravuri, S Truong, Y Du, S Stanton, ...
Advances in Neural Information Processing Systems 36, 2024
Meta-learning adaptive deep kernel gaussian processes for molecular property prediction
W Chen, A Tripp, JM Hernández-Lobato
arXiv preprint arXiv:2205.02708, 2022
Petroleomic analysis of the treatment of naphthenic organics in oil sands process-affected water with buoyant photocatalysts
T Leshuk, KM Peru, D de Oliveira Livera, A Tripp, P Bardo, JV Headley, ...
Water Research 141, 297-306, 2018
A fresh look at de novo molecular design benchmarks
A Tripp, GNC Simm, JM Hernández-Lobato
NeurIPS 2021 AI for Science Workshop, 2021
Re-evaluating chemical synthesis planning algorithms
A Tripp, K Maziarz, S Lewis, G Liu, M Segler
NeurIPS 2022 AI for Science: Progress and Promises, 2022
Retrosynthetic Planning with Dual Value Networks
G Liu, D Xue, S Xie, Y Xia, A Tripp, K Maziarz, M Segler, T Qin, Z Zhang, ...
40th International Conference on Machine Learning 202, 22266-22276, 2023
Re-evaluating Retrosynthesis algorithms with syntheseus
K Maziarz, A Tripp, G Liu, M Stanley, S Xie, P Gaiński, P Seidl, M Segler
arXiv preprint arXiv:2310.19796, 2023
An evaluation framework for the objective functions of de novo drug design benchmarks
A Tripp, W Chen, JM Hernández-Lobato
ICLR2022 Machine Learning for Drug Discovery, 2022
Genetic algorithms are strong baselines for molecule generation
A Tripp, JM Hernández-Lobato
arXiv preprint arXiv:2310.09267, 2023
Retro-fallback: retrosynthetic planning in an uncertain world
A Tripp, K Maziarz, S Lewis, M Segler, JM Hernández-Lobato
arXiv preprint arXiv:2310.09270, 2023
Tanimoto random features for scalable molecular machine learning
A Tripp, S Bacallado, S Singh, JM Hernández-Lobato
Advances in Neural Information Processing Systems 36, 2024
Vibrational Raman shifts of spin isomer combinations of hydrogen dimers and isotopologues
A Marr, T Halverson, A Tripp, PN Roy
The Journal of Physical Chemistry A 124 (34), 6877-6888, 2020
Stochastic Gradient Descent for Gaussian Processes Done Right
JA Lin, S Padhy, J Antorán, A Tripp, A Terenin, C Szepesvári, ...
arXiv preprint arXiv:2310.20581, 2023
GAUCHE: A library for Gaussian processes and Bayesian optimisation in chemistry
RR Griffiths, L Klarner, A Ravuri, S Truong, B Rankovic, Y Du, A Jamasb, ...
ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in …, 2022
Nonequilibrium sensing of volatile compounds using active and passive analyte delivery
S Brandt, I Pavlichenko, AV Shneidman, H Patel, A Tripp, TSB Wong, ...
Proceedings of the National Academy of Sciences 120 (31), e2303928120, 2023
Volatile liquid analysis
I Pavlichenko, E Shirman, S Brandt, TSB Wong, A Tripp, J Aizenberg
US Patent App. 17/054,868, 2021
Diagnosing and fixing common problems in Bayesian optimization for molecule design
A Tripp, JM Hernández-Lobato
arXiv preprint arXiv:2406.07709, 2024
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