Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space

6 June 2017

José Miguel Hernández-Lobato

James Requeima

Edward O. Pyzer-Knapp

Alán Aspuru-Guzik

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Papers citing "Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space"

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Distributed Thompson sampling under constrained communication Saba Zerefa Zhaolin Ren Haitong Ma Na Li 28 1 0 03 Jan 2025
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Batched Bayesian optimization by maximizing the probability of including the optimum Jenna C. Fromer Runzhong Wang Mrunali Manjrekar Austin Tripp José Miguel Hernández-Lobato Connor W. Coley 42 0 0 08 Oct 2024
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Parallel Hyperparameter Optimization Of Spiking Neural Network Thomas Firmin Pierre Boulet El-Ghazali Talbi 30 3 0 01 Mar 2024
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces Leonard Papenmeier Luigi Nardi Matthias Poloczek 11 36 0 22 Apr 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 25 24 0 24 Jan 2023
Recent Advances in Bayesian Optimization Xilu Wang Yaochu Jin Sebastian Schmitt Markus Olhofer 38 197 0 07 Jun 2022
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery Chenru Duan F. Liu Aditya Nandy Heather J. Kulik AI4CE 16 34 0 06 May 2022
$Distributionally Robust Bayesian Optimization with $\varphi$-divergences$ Distributionally Robust Bayesian Optimization with $\varphi$ -divergences Hisham Husain Vu-Linh Nguyen A. Hengel 38 13 0 04 Mar 2022
Local Latent Space Bayesian Optimization over Structured Inputs N. Maus Haydn Jones Juston Moore Matt J. Kusner John Bradshaw J. Gardner BDL 49 69 0 28 Jan 2022
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems Jack Parker-Holder Raghunandan Rajan Xingyou Song André Biedenkapp Yingjie Miao ... Vu-Linh Nguyen Roberto Calandra Aleksandra Faust Frank Hutter Marius Lindauer AI4CE 30 100 0 11 Jan 2022
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery Daniel R Harper Aditya Nandy N. Arunachalam Chenru Duan J. Janet Heather J. Kulik 6 8 0 20 Jun 2021
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 Ryan Turner David Eriksson M. McCourt J. Kiili Eero Laaksonen Zhen Xu Isabelle M Guyon BDL 11 288 0 20 Apr 2021
Accelerating high-throughput virtual screening through molecular pool-based active learning David E. Graff E. Shakhnovich Connor W. Coley 76 142 0 13 Dec 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 13 57 0 08 Nov 2020
Asynchronous ε-Greedy Bayesian Optimisation George De Ath Richard Everson J. Fieldsend 18 5 0 15 Oct 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss M. Jankowiak David Eriksson Anil Damle J. Gardner 13 43 0 19 Jun 2020
Using Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19 Edward O. Pyzer-Knapp 4 7 0 11 May 2020
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge Florian Hase Matteo Aldeghi Riley J. Hickman L. Roch Alán Aspuru-Guzik 43 104 0 26 Mar 2020
Scalable Constrained Bayesian Optimization David Eriksson Matthias Poloczek 17 95 0 20 Feb 2020
$ε$ -shotgun: $ε$ -greedy Batch Bayesian Optimisation George De Ath Richard Everson J. Fieldsend Alma A. M. Rahat 8 15 0 05 Feb 2020
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation Ryan-Rhys Griffiths Alexander A. Aldrick Miguel García-Ortegón Vidhi R. Lalchand A. Lee 21 35 0 17 Oct 2019
Scalable Global Optimization via Local Bayesian Optimization Samyam Rajbhandari Michael Pearce J. Gardner Ryan D. Turner Matthias Poloczek 11 447 0 03 Oct 2019
Efficient and Scalable Batch Bayesian Optimization Using K-Means Matthew J. Groves Edward O. Pyzer-Knapp 6 15 0 04 Jun 2018
A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations Biswajit Paria Kirthevasan Kandasamy Barnabás Póczós 4 125 0 30 May 2018
Actively Learning what makes a Discrete Sequence Valid David Janz J. Westhuizen José Miguel Hernández-Lobato 13 22 0 15 Aug 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 261 9,134 0 06 Jun 2015