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[1] Minyi Guo,et al. GraphGAN: Graph Representation Learning with Generative Adversarial Nets , 2017, AAAI.
[2] Kirthevasan Kandasamy,et al. High Dimensional Bayesian Optimisation and Bandits via Additive Models , 2015, ICML.
[3] Eduardo F. Morales,et al. An Introduction to Reinforcement Learning , 2011 .
[4] Nicola De Cao,et al. MolGAN: An implicit generative model for small molecular graphs , 2018, ArXiv.
[5] Jin Woo Kim,et al. Molecular generative model based on conditional variational autoencoder for de novo molecular design , 2018, Journal of Cheminformatics.
[6] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[7] Jennifer Listgarten,et al. Conditioning by adaptive sampling for robust design , 2019, ICML.
[8] Regina Barzilay,et al. Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.
[9] Frank Hutter,et al. Neural Architecture Search: A Survey , 2018, J. Mach. Learn. Res..
[10] José Miguel Hernández-Lobato,et al. Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection , 2019, arXiv.org.
[11] Roman Garnett,et al. D-VAE: A Variational Autoencoder for Directed Acyclic Graphs , 2019, NeurIPS.
[12] Samy Bengio,et al. Generating Sentences from a Continuous Space , 2015, CoNLL.
[13] Kian Hsiang Low,et al. Decentralized High-Dimensional Bayesian Optimization with Factor Graphs , 2017, AAAI.
[14] Antoine Blanchard,et al. Output-Weighted Importance Sampling for Bayesian Experimental Design and Uncertainty Quantification , 2020, ArXiv.
[15] Jugal K. Kalita,et al. A Survey of the Usages of Deep Learning for Natural Language Processing , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[16] Jennifer Listgarten,et al. Design by adaptive sampling , 2018, ArXiv.
[17] Kyunghyun Cho,et al. Conditional molecular design with deep generative models , 2018, J. Chem. Inf. Model..
[18] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[19] Thomas Blaschke,et al. Molecular de-novo design through deep reinforcement learning , 2017, Journal of Cheminformatics.
[20] Nando de Freitas,et al. Bayesian Optimization in High Dimensions via Random Embeddings , 2013, IJCAI.
[21] Donald R. Jones,et al. Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..
[22] Olexandr Isayev,et al. Deep reinforcement learning for de novo drug design , 2017, Science Advances.
[23] Roman Garnett,et al. Active Learning of Linear Embeddings for Gaussian Processes , 2013, UAI.
[24] Seungjin Choi,et al. Bayesian Optimization over Sets , 2019, ArXiv.
[25] R. Rubinstein. The Cross-Entropy Method for Combinatorial and Continuous Optimization , 1999 .
[26] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[27] Kirthevasan Kandasamy,et al. ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations , 2019, AISTATS.
[28] Tie-Yan Liu,et al. Neural Architecture Optimization , 2018, NeurIPS.
[29] Jos'e Miguel Hern'andez-Lobato,et al. Reinforcement Learning for Molecular Design Guided by Quantum Mechanics , 2020, ICML.
[30] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[31] Neil D. Lawrence,et al. Structured Variationally Auto-encoded Optimization , 2018, ICML.
[32] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[33] José Miguel Hernández-Lobato,et al. A COLD Approach to Generating Optimal Samples , 2019, ArXiv.
[34] Thomas Brox,et al. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks , 2016, NIPS.
[35] Jakub M. Tomczak,et al. Combinatorial Bayesian Optimization using the Graph Cartesian Product , 2019, NeurIPS.
[36] Jos'e Miguel Hern'andez-Lobato,et al. Getting a CLUE: A Method for Explaining Uncertainty Estimates , 2020, ICLR.
[37] Andrew Gordon Wilson,et al. Deep Kernel Learning , 2015, AISTATS.
[38] Matt J. Kusner,et al. Grammar Variational Autoencoder , 2017, ICML.
[39] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[40] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[41] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[42] Ryan G. Coleman,et al. ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..
[43] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[44] Michalis K. Titsias,et al. Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.
[45] Stefan Schaal,et al. Reinforcement learning by reward-weighted regression for operational space control , 2007, ICML '07.
[46] Andreas Krause,et al. Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features , 2018, NeurIPS.
[47] Nando de Freitas,et al. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning , 2010, ArXiv.
[48] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[49] Li Li,et al. Optimization of Molecules via Deep Reinforcement Learning , 2018, Scientific Reports.
[50] Alán Aspuru-Guzik,et al. Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models , 2017, ArXiv.
[51] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[52] José Miguel Hernández-Lobato,et al. Constrained Bayesian optimization for automatic chemical design using variational autoencoders. , 2019 .
[53] James Zou,et al. Feedback GAN for DNA optimizes protein functions , 2019, Nature Machine Intelligence.
[54] Thierry Kogej,et al. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ACS central science.
[55] Michael McCloskey,et al. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .
[56] Deli Zhao,et al. Scalable Gaussian Process Regression Using Deep Neural Networks , 2015, IJCAI.
[57] Alexis Boukouvalas,et al. GPflow: A Gaussian Process Library using TensorFlow , 2016, J. Mach. Learn. Res..
[58] Nikos Komodakis,et al. GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders , 2018, ICANN.
[59] Daniel C. Elton,et al. Deep learning for molecular generation and optimization - a review of the state of the art , 2019, Molecular Systems Design & Engineering.
[60] Shie Mannor,et al. A Tutorial on the Cross-Entropy Method , 2005, Ann. Oper. Res..
[61] Honglak Lee,et al. Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.
[62] Regina Barzilay,et al. Learning Multimodal Graph-to-Graph Translation for Molecular Optimization , 2018, ICLR.
[63] Yoshua Bengio,et al. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[64] Andreas Krause,et al. Mixed-Variable Bayesian Optimization , 2020, IJCAI.
[65] Tom White,et al. Sampling Generative Networks: Notes on a Few Effective Techniques , 2016, ArXiv.
[66] Stefano Ermon,et al. Bayesian optimization and attribute adjustment , 2018, UAI.
[67] Silvio Savarese,et al. Active Learning for Convolutional Neural Networks: A Core-Set Approach , 2017, ICLR.
[68] Peter Rossmanith,et al. Simulated Annealing , 2008, Taschenbuch der Algorithmen.
[69] Matthias Poloczek,et al. Bayesian Optimization of Combinatorial Structures , 2018, ICML.
[70] Kathryn A. Dowsland,et al. Simulated Annealing , 1989, Encyclopedia of GIS.
[71] Yuxi Li,et al. Deep Reinforcement Learning , 2018, Reinforcement Learning for Cyber-Physical Systems.
[72] Hiroshi Kajino,et al. Molecular Hypergraph Grammar with its Application to Molecular Optimization , 2018, ICML.
[73] Jure Leskovec,et al. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation , 2018, NeurIPS.
[74] Reuven Y. Rubinstein,et al. Optimization of computer simulation models with rare events , 1997 .
[75] Yibo Li,et al. Multi-objective de novo drug design with conditional graph generative model , 2018, Journal of Cheminformatics.
[76] Max Welling,et al. Combinatorial Bayesian Optimization using Graph Representations , 2019, ArXiv.
[77] Steven Skiena,et al. Syntax-Directed Variational Autoencoder for Structured Data , 2018, ICLR.
[78] Alán Aspuru-Guzik,et al. Inverse molecular design using machine learning: Generative models for matter engineering , 2018, Science.