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[1] Dirk P. Kroese,et al. Cross‐Entropy Method , 2011 .
[2] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.
[3] Iain Murray,et al. Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation , 2016, 1605.06376.
[4] David J. Nott,et al. Variational Bayes With Intractable Likelihood , 2015, 1503.08621.
[5] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[6] Li Li,et al. Optimization of Molecules via Deep Reinforcement Learning , 2018, Scientific Reports.
[7] Iain Murray,et al. Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows , 2018, AISTATS.
[8] Jakob H. Macke,et al. Likelihood-free inference with emulator networks , 2018, AABI.
[9] Alán Aspuru-Guzik,et al. Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models , 2017, ArXiv.
[10] Jakob H. Macke,et al. Flexible statistical inference for mechanistic models of neural dynamics , 2017, NIPS.
[11] Aapo Hyvärinen,et al. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models , 2010, AISTATS.
[12] Alán Aspuru-Guzik,et al. Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space , 2020, ICLR.
[13] James Zou,et al. Feedback GAN for DNA optimizes protein functions , 2019, Nature Machine Intelligence.
[14] Jinwoo Shin,et al. Guiding Deep Molecular Optimization with Genetic Exploration , 2020, NeurIPS.
[15] Scott A. Sisson,et al. Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model , 2015, 1504.04093.
[16] Jennifer Listgarten,et al. Conditioning by adaptive sampling for robust design , 2019, ICML.
[17] Jean-Michel Marin,et al. Approximate Bayesian computational methods , 2011, Statistics and Computing.
[18] Yun S. Song,et al. A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks , 2018, bioRxiv.
[19] Rafael Izbicki,et al. High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation , 2014, AISTATS.
[20] Dmitry Chudakov,et al. Local fitness landscape of the green fluorescent protein , 2016, Nature.
[21] D. Balding,et al. Approximate Bayesian computation in population genetics. , 2002, Genetics.
[22] Gilles Louppe,et al. Mining gold from implicit models to improve likelihood-free inference , 2018, Proceedings of the National Academy of Sciences.
[23] Jacob Witten,et al. Deep learning regression model for antimicrobial peptide design , 2019, bioRxiv.
[24] David Dohan,et al. Model-based reinforcement learning for biological sequence design , 2020, ICLR.
[25] Kerrie Mengersen,et al. Approximating the likelihood in approximate Bayesian computation , 2018, 1803.06645.
[26] Anne Brindle,et al. Genetic algorithms for function optimization , 1980 .
[27] S. Rees,et al. Principles of early drug discovery , 2011, British journal of pharmacology.
[28] Mohamed Ahmed,et al. Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design , 2018, ICLR.
[29] G. Seelig,et al. Human 5′ UTR design and variant effect prediction from a massively parallel translation assay , 2018, bioRxiv.
[30] B. Rost,et al. ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing. , 2021, IEEE transactions on pattern analysis and machine intelligence.
[31] Xi Chen,et al. Evolution Strategies as a Scalable Alternative to Reinforcement Learning , 2017, ArXiv.
[32] Frances H. Arnold,et al. Enzyme Engineering for Nonaqueous Solvents: Random Mutagenesis to Enhance Activity of Subtilisin E in Polar Organic Media , 1991, Bio/Technology.
[33] Kevin K. Yang,et al. Machine-learning-guided directed evolution for protein engineering , 2018, Nature Methods.
[34] David S. Greenberg,et al. Automatic Posterior Transformation for Likelihood-Free Inference , 2019, ICML.
[35] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[36] M. Gutmann,et al. Fundamentals and Recent Developments in Approximate Bayesian Computation , 2016, Systematic biology.
[37] Iain Murray,et al. Masked Autoregressive Flow for Density Estimation , 2017, NIPS.
[38] Jennifer Listgarten,et al. Design by adaptive sampling , 2018, ArXiv.
[39] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[40] Zachary Wu,et al. Machine learning-assisted directed protein evolution with combinatorial libraries , 2019, Proceedings of the National Academy of Sciences.
[41] Jukka Corander,et al. Likelihood-Free Inference by Ratio Estimation , 2016, Bayesian Analysis.
[42] S. Wood. Statistical inference for noisy nonlinear ecological dynamic systems , 2010, Nature.
[43] John C. Duchi,et al. Derivative Free Optimization Via Repeated Classification , 2018, AISTATS.
[44] Gilles Louppe,et al. Likelihood-free MCMC with Amortized Approximate Likelihood Ratios , 2019 .
[45] Koji Tsuda,et al. Population-based de novo molecule generation, using grammatical evolution , 2018, 1804.02134.
[46] Jaie C. Woodard,et al. Survey of variation in human transcription factors reveals prevalent DNA binding changes , 2016, Science.
[47] Alok Aggarwal,et al. Regularized Evolution for Image Classifier Architecture Search , 2018, AAAI.
[48] Paul Fearnhead,et al. Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation , 2012 .
[49] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[50] Jan H. Jensen,et al. A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space , 2018, Chemical science.
[51] Ritabrata Dutta,et al. Likelihood-free inference via classification , 2014, Stat. Comput..
[52] Zhanxing Zhu,et al. Neural Approximate Sufficient Statistics for Implicit Models , 2021, ICLR.
[53] Yoshua Bengio,et al. Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning , 2020, ICML.
[54] Christian P. Robert,et al. Approximate Bayesian computation via empirical likelihood , 2012 .
[55] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[56] David Dohan,et al. Population-Based Black-Box Optimization for Biological Sequence Design , 2020, ICML.
[57] Ziheng Wang,et al. Antibody complementarity determining region design using high-capacity machine learning , 2019, bioRxiv.
[58] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[59] Tom Schaul,et al. Natural Evolution Strategies , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
[60] M. Blum. Approximate Bayesian Computation: A Nonparametric Perspective , 2009, 0904.0635.
[61] Weinan Zhang,et al. GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation , 2020, ICLR.
[62] John Canny,et al. Evaluating Protein Transfer Learning with TAPE , 2019, bioRxiv.