Fetching the paper…
Reading the bibliography…
The increasing availability of structured but high dimensional data has opened new opportunities for optimization.
On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
William R Thompson · 1933
Earlier work this paper cites.
SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
Earlier work this paper cites.
Gaussian processes in machine learning
Carl Edward Rasmussen · 2003
Earlier work this paper cites.
Gaussian process latent variable models for visualisation of high dimensional data
Neil D Lawrence · 2004
Earlier work this paper cites.
Minimum volume enclosing ellipsoid
Nima Moshtagh et al · 2005
Earlier work this paper cites.
Sparse gaussian processes using pseudo-inputs
Edward Snelson and Zoubin Ghahramani · 2005
Earlier work this paper cites.
Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery
Tobias Fink and Jean-Louis Reymond · 2007
Earlier work this paper cites.
Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
Earlier work this paper cites.
Variational learning of inducing variables in sparse gaussian processes
Michalis Titsias · 2009
Earlier work this paper cites.
Eric Brochu, Vlad M Cora, and Nando De Freitas · 2010
Earlier work this paper cites.
Time series analysis: forecasting and control , volume 734
George EP Box, Gwilym M Jenkins, and Gregory C Reinsel · 2011
Earlier work this paper cites.
An empirical evaluation of Thompson sampling
Olivier Chapelle and Lihong Li · 2011
Cited alongside, same era.
Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Cited alongside, same era.
Active learning of linear embeddings for gaussian processes
Roman Garnett, Michael A Osborne, and Philipp Hennig · 2013
Cited alongside, same era.
Bayesian Optimization in high dimensions via random embeddings
Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, Nando De Freitas, et al · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
dSprites: Disentanglement testing Sprites dataset
Loic Matthey, Irina Higgins, Demis Hassabis, and Alexander Lerchner · 2017
Later among the works it cites.
Hierarchical Variational Autoencoders for Music , 2017
Adam Roberts, Jesse Engel, and Douglas Eck, editors · 2017
Later among the works it cites.
Improved variational autoencoders for text modeling using dilated convolutions
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick · 2017
Later among the works it cites.
Bayesian optimization and attribute adjustment
Stephan Eissman, Daniel Levy, Rui Shu, Stefan Bartzsch, and Stefano Ermon · 2018
Later among the works it cites.
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, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
Cited alongside, same era.
High dimensional Bayesian optimisation and bandits via additive models
Kirthevasan Kandasamy, Jeff Schneider, and Barnabás Póczos · 2015
Cited alongside, same era.
Taking the human out of the loop: A review of Bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando De Freitas · 2015
Cited alongside, same era.
Constrained Bayesian optimization for automatic chemical design
Ryan-Rhys Griffiths and José Miguel Hernández-Lobato · 2017
Cited alongside, same era.
Deep feature consistent variational autoencoder
Xianxu Hou, Linlin Shen, Ke Sun, and Guoping Qiu · 2017
Cited alongside, same era.
Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
Cited alongside, same era.
Xiaoyu Lu, Javier Gonzalez, Zhenwen Dai, and Neil Lawrence · 2018
Later among the works it cites.
Efficient high dimensional Bayesian optimization with additivity and quadrature Fourier features
Mojmir Mutny and Andreas Krause · 2018
Later among the works it cites.
The continuous Bernoulli: fixing a pervasive error in variational autoencoders
Gabriel Loaiza-Ganem and John P Cunningham · 2019
Later among the works it cites.
Emulation of physical processes with Emukit
Andrei Paleyes, Mark Pullin, Maren Mahsereci, Neil Lawrence, and Javier Gonzalez · 2019
Later among the works it cites.
Variational sparse coding
Francesco Tonolini, Bjørn Sand Jensen, and Roderick Murray-Smith · 2020
Closest in time.
Sample-efficient optimization in the latent space of deep generative models via weighted retraining
Austin Tripp, Erik Daxberger, and José Miguel Hernández-Lobato · 2020
Closest in time.