Local Latent Space Bayesian Optimization over Structured Inputs

28 January 2022

Papers citing "Local Latent Space Bayesian Optimization over Structured Inputs"

44 / 44 papers shown

Title
Safe Bayesian Optimization for the Control of High-Dimensional Embodied Systems Yunyue Wei Zeji Yi Hongda Li Saraswati Soedarmadji Yanan Sui 61 0 0 31 Dec 2024
Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design Melis Ilayda Bal Pier Giuseppe Sessa Mojmír Mutný Andreas Krause 58 1 0 27 Sep 2024
CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization Jacob O. Tørring Carl Hvarfner Luigi Nardi Magnus Sjalander 78 1 0 24 Jun 2024
Expected Coordinate Improvement for High-Dimensional Bayesian Optimization Dawei Zhan 64 5 0 18 Apr 2024
Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces Aryan Deshwal J. Doppa BDL 61 42 0 01 Nov 2021
Improving black-box optimization in VAE latent space using decoder uncertainty Pascal Notin José Miguel Hernández-Lobato Y. Gal 112 61 0 30 Jun 2021
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning Antoine Grosnit Rasul Tutunov A. Maraval Ryan-Rhys Griffiths Alexander I. Cowen-Rivers ... Wenlong Lyu Zhitang Chen Jun Wang Jan Peters Haitham Bou-Ammar BDL DRL 58 60 0 07 Jun 2021
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery Yutong Xie Chence Shi Hao Zhou Yuwei Yang Weinan Zhang Yong Yu Lei Li 74 143 0 18 Mar 2021
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces David Eriksson M. Jankowiak 57 139 0 27 Feb 2021
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development Kexin Huang Tianfan Fu Wenhao Gao Yue Zhao Yusuf Roohani J. Leskovec Connor W. Coley Cao Xiao Jimeng Sun Marinka Zitnik OOD LM&MA 52 272 0 18 Feb 2021
Good practices for Bayesian Optimization of high dimensional structured spaces E. Siivola Javier I. González Andrei Paleyes Aki Vehtari 57 37 0 31 Dec 2020
Accelerating high-throughput virtual screening through molecular pool-based active learning David E. Graff E. Shakhnovich Connor W. Coley 100 145 0 13 Dec 2020
Guiding Deep Molecular Optimization with Genetic Exploration SungSoo Ahn Junsup Kim Hankook Lee Jinwoo Shin 62 73 0 04 Jul 2020
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining Austin Tripp Erik A. Daxberger José Miguel Hernández-Lobato MedIm 56 139 0 16 Jun 2020
Autonomous discovery in the chemical sciences part II: Outlook Connor W. Coley Natalie S. Eyke K. Jensen 45 171 0 30 Mar 2020
Autonomous discovery in the chemical sciences part I: Progress Connor W. Coley Natalie S. Eyke K. Jensen 46 215 0 30 Mar 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 244 10,591 0 17 Feb 2020
Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery Hakime Öztürk Arzucan Özgür P. Schwaller Teodoro Laino Elif Özkirimli 63 118 0 10 Feb 2020
Scalable Global Optimization via Local Bayesian Optimization Samyam Rajbhandari Michael Pearce Jacob R. Gardner Ryan D. Turner Matthias Poloczek 79 458 0 03 Oct 2019
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space AkshatKumar Nigam Pascal Friederich Mario Krenn Alán Aspuru-Guzik AI4CE 41 131 0 25 Sep 2019
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation Mario Krenn Florian Hase AkshatKumar Nigam Pascal Friederich Alán Aspuru-Guzik 74 71 0 31 May 2019
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs Muhan Zhang Shali Jiang Zhicheng Cui Roman Garnett Yixin Chen GNN BDL CML 62 199 0 24 Apr 2019
GuacaMol: Benchmarking Models for De Novo Molecular Design Nathan Brown Marco Fiscato Marwin H. S. Segler Alain C. Vaucher ELM 84 703 0 22 Nov 2018
Optimization of Molecules via Deep Reinforcement Learning Zhenpeng Zhou S. Kearnes Li Li R. Zare Patrick F. Riley AI4CE 74 537 0 19 Oct 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob R. Gardner Geoff Pleiss D. Bindel Kilian Q. Weinberger A. Wilson GP 77 1,088 0 28 Sep 2018
Molecular Hypergraph Grammar with its Application to Molecular Optimization Hiroshi Kajino 38 102 0 08 Sep 2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation Jiaxuan You Bowen Liu Rex Ying Vijay S. Pande J. Leskovec GNN 267 895 0 07 Jun 2018
Syntax-Directed Variational Autoencoder for Structured Data H. Dai Yingtao Tian Bo Dai Steven Skiena Le Song 77 325 0 24 Feb 2018
NeVAE: A Deep Generative Model for Molecular Graphs Bidisha Samanta A. De G. Jana P. Chattaraj Niloy Ganguly Manuel Gomez Rodriguez GNN DRL BDL DiffM 56 214 0 14 Feb 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation Wengong Jin Regina Barzilay Tommi Jaakkola 303 1,358 0 12 Feb 2018
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 501 129,831 0 12 Jun 2017
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space José Miguel Hernández-Lobato James Requeima Edward O. Pyzer-Knapp Alán Aspuru-Guzik 56 181 0 06 Jun 2017
Batched Large-scale Bayesian Optimization in High-dimensional Spaces Zi Wang Clement Gehring Pushmeet Kohli Stefanie Jegelka UQCV 37 211 0 05 Jun 2017
Molecular De Novo Design through Deep Reinforcement Learning Marcus Olivecrona T. Blaschke Ola Engkvist Hongming Chen BDL 99 1,003 0 25 Apr 2017
Grammar Variational Autoencoder Matt J. Kusner Brooks Paige José Miguel Hernández-Lobato BDL DRL 71 838 0 06 Mar 2017
Stochastic Variational Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 69 267 0 01 Nov 2016
Automatic chemical design using a data-driven continuous representation of molecules Rafael Gómez-Bombarelli Jennifer N. Wei David Duvenaud José Miguel Hernández-Lobato Benjamín Sánchez-Lengeling Dennis Sheberla J. Aguilera-Iparraguirre Timothy D. Hirzel Ryan P. Adams Alán Aspuru-Guzik 3DV 132 2,911 0 07 Oct 2016
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 196 882 0 06 Nov 2015
High Dimensional Bayesian Optimisation and Bandits via Additive Models Kirthevasan Kandasamy J. Schneider Barnabás Póczós 51 354 0 05 Mar 2015
Semi-Supervised Learning with Deep Generative Models Diederik P. Kingma Danilo Jimenez Rezende S. Mohamed Max Welling GAN SSL BDL 73 2,731 0 20 Jun 2014
Active Learning of Linear Embeddings for Gaussian Processes Roman Garnett Michael A. Osborne Philipp Hennig GP 82 91 0 24 Oct 2013
Gaussian Processes for Big Data J. Hensman Nicolò Fusi Neil D. Lawrence GP 94 1,226 0 26 Sep 2013
Bayesian Optimization in a Billion Dimensions via Random Embeddings Ziyun Wang Frank Hutter M. Zoghi David Matheson Nando de Freitas 152 439 0 09 Jan 2013
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 298 7,883 0 13 Jun 2012