Unifying Likelihood-free Inference with Black-box Optimization and Beyond

6 October 2021

Aaron Courville

Papers citing "Unifying Likelihood-free Inference with Black-box Optimization and Beyond"

34 / 34 papers shown

Title
Neural Approximate Sufficient Statistics for Implicit Models Yanzhi Chen Dinghuai Zhang Michael U. Gutmann Aaron Courville Zhanxing Zhu 56 83 0 20 Oct 2020
Error-guided likelihood-free MCMC Volodimir Begy Erich Schikuta 25 3 0 13 Oct 2020
ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing Ahmed Elnaggar M. Heinzinger Christian Dallago Ghalia Rehawi Yu Wang ... Tamas B. Fehér Christoph Angerer Martin Steinegger D. Bhowmik B. Rost DRL 25 927 0 13 Jul 2020
Guiding Deep Molecular Optimization with Genetic Exploration SungSoo Ahn Junsup Kim Hankook Lee Jinwoo Shin 48 73 0 04 Jul 2020
Population-Based Black-Box Optimization for Biological Sequence Design Christof Angermüller David Belanger Andreea Gane Zelda E. Mariet David Dohan Kevin Patrick Murphy Lucy J. Colwell D. Sculley 48 133 0 05 Jun 2020
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning S. Gottipati B. Sattarov Sufeng Niu Yashaswi Pathak Haoran Wei ... Simon R. Blackburn Connor W. Coley Jian Tang Sarath Chandar Yoshua Bengio 25 109 0 26 Apr 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 79 10,591 0 17 Feb 2020
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation Chence Shi Minkai Xu Zhaocheng Zhu Weinan Zhang Ming Zhang Jian Tang 109 434 0 26 Jan 2020
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space AkshatKumar Nigam Pascal Friederich Mario Krenn Alán Aspuru-Guzik AI4CE 29 131 0 25 Sep 2019
Evaluating Protein Transfer Learning with TAPE Roshan Rao Nicholas Bhattacharya Neil Thomas Yan Duan Xi Chen John F. Canny Pieter Abbeel Yun S. Song SSL 63 786 0 19 Jun 2019
Automatic Posterior Transformation for Likelihood-Free Inference David S. Greenberg M. Nonnenmacher Jakob H. Macke 44 319 0 17 May 2019
Adaptive Gaussian Copula ABC Yanzhi Chen Michael U. Gutmann TPM 40 27 0 27 Feb 2019
Conditioning by adaptive sampling for robust design David H. Brookes Hahnbeom Park Jennifer Listgarten 31 197 0 29 Jan 2019
Optimization of Molecules via Deep Reinforcement Learning Zhenpeng Zhou S. Kearnes Li Li R. Zare Patrick F. Riley AI4CE 58 537 0 19 Oct 2018
Design by adaptive sampling David H. Brookes Jennifer Listgarten TPM 64 66 0 08 Oct 2018
Mining gold from implicit models to improve likelihood-free inference Johann Brehmer Gilles Louppe J. Pavez Kyle Cranmer AI4CE TPM 50 181 0 30 May 2018
Likelihood-free inference with emulator networks Jan-Matthis Lueckmann Giacomo Bassetto Theofanis Karaletsos Jakob H. Macke 43 125 0 23 May 2018
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows George Papamakarios D. Sterratt Iain Murray BDL 87 360 0 18 May 2018
Derivative free optimization via repeated classification Tatsunori B. Hashimoto Steve Yadlowsky John C. Duchi 13 18 0 11 Apr 2018
Approximating the Likelihood in Approximate Bayesian Computation Christopher C. Drovandi Clara Grazian Kerrie Mengersen Christian P. Robert 36 12 0 18 Mar 2018
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks Jeffrey Chan Valerio Perrone J. Spence Paul A. Jenkins Sara Mathieson Yun S. Song 36 106 0 16 Feb 2018
Regularized Evolution for Image Classifier Architecture Search Esteban Real A. Aggarwal Yanping Huang Quoc V. Le 113 3,009 0 05 Feb 2018
Flexible statistical inference for mechanistic models of neural dynamics Jan-Matthis Lueckmann P. J. Gonçalves Giacomo Bassetto Kaan Öcal M. Nonnenmacher Jakob H. Macke 53 241 0 06 Nov 2017
Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models G. L. Guimaraes Benjamín Sánchez-Lengeling Carlos Outeiral Pedro Luis Cunha Farias Alán Aspuru-Guzik GAN 46 523 0 30 May 2017
Masked Autoregressive Flow for Density Estimation George Papamakarios Theo Pavlakou Iain Murray 52 1,340 0 19 May 2017
Evolution Strategies as a Scalable Alternative to Reinforcement Learning Tim Salimans Jonathan Ho Xi Chen Szymon Sidor Ilya Sutskever 52 1,523 0 10 Mar 2017
Likelihood-free inference by ratio estimation Owen Thomas Ritabrata Dutta J. Corander Samuel Kaski Michael U. Gutmann 71 147 0 30 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 97 2,911 0 07 Oct 2016
Fast $ε$ -free Inference of Simulation Models with Bayesian Conditional Density Estimation George Papamakarios Iain Murray TPM 54 158 0 20 May 2016
Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model Jingjing Li David J. Nott Yanan Fan Scott A. Sisson 37 42 0 16 Apr 2015
Likelihood-free inference via classification Michael U. Gutmann Ritabrata Dutta Samuel Kaski J. Corander 38 63 0 18 Jul 2014
Approximate Bayesian Computational methods Jean-Michel Marin Pierre Pudlo Christian P. Robert Robin J. Ryder 114 861 0 05 Jan 2011
Approximate Bayesian Computation: a nonparametric perspective M. Blum 66 240 0 03 Apr 2009
Adaptive approximate Bayesian computation Mark Beaumont J. Cornuet Jean-Michel Marin Christian P. Robert 69 641 0 15 May 2008