| 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 | 120 | 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 | 52 | 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 | 24 | 2018 |
| Meta-learning adaptive deep kernel gaussian processes for molecular property prediction W Chen, A Tripp, JM Hernández-Lobato arXiv preprint arXiv:2205.02708, 2022 | 20 | 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 | 19 | 2024 |
| 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 | 9 | 2022 |
| A fresh look at de novo molecular design benchmarks A Tripp, GNC Simm, JM Hernández-Lobato NeurIPS 2021 AI for Science Workshop, 2021 | 9 | 2021 |
| 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 | 5 | 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 | 4 | 2022 |
| 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 | 4 | 2020 |
| Genetic algorithms are strong baselines for molecule generation A Tripp, JM Hernández-Lobato arXiv preprint arXiv:2310.09267, 2023 | 3 | 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 | 2 | 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 | 2 | 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 | 2 | 2022 |
| 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 | 1 | 2024 |
| Volatile liquid analysis I Pavlichenko, E Shirman, S Brandt, TSB Wong, A Tripp, J Aizenberg US Patent App. 17/054,868, 2021 | 1 | 2021 |
| 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 | | 2023 |
| 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 | | 2023 |
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeDirección de correo verificada de cam.ac.uk
