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ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

31 January 2019

Willie Neiswanger

Kirthevasan Kandasamy

Papers citing "ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language"

20 / 20 papers shown

Title
Accelerating Experimental Design by Incorporating Experimenter Hunches Cheng Li Santu Rana Sunil R. Gupta Vu Nguyen Svetha Venkatesh ... David Rubín de Celis Leal Teo Slezak Murray Height M. Mohammed I. Gibson 71 33 0 22 Jul 2019
Pyro: Deep Universal Probabilistic Programming Eli Bingham Jonathan P. Chen M. Jankowiak F. Obermeyer Neeraj Pradhan Theofanis Karaletsos Rohit Singh Paul A. Szerlip Paul Horsfall Noah D. Goodman BDL GP 158 1,057 0 18 Oct 2018
Maximizing acquisition functions for Bayesian optimization James T. Wilson Frank Hutter M. Deisenroth 129 247 0 25 May 2018
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming Kirthevasan Kandasamy Willie Neiswanger Reed Zhang A. Krishnamurthy J. Schneider Barnabás Póczós 42 5 0 25 May 2018
Neural Architecture Search with Bayesian Optimisation and Optimal Transport Kirthevasan Kandasamy Willie Neiswanger J. Schneider Barnabás Póczós Eric Xing 82 609 0 11 Feb 2018
Learning Disentangled Representations with Semi-Supervised Deep Generative Models Siddharth Narayanaswamy Brooks Paige Jan-Willem van de Meent Alban Desmaison Noah D. Goodman Pushmeet Kohli Frank Wood Philip Torr DRL CoGe 133 363 0 01 Jun 2017
Bayesian Optimization with Gradients Jian Wu Matthias Poloczek A. Wilson P. Frazier 58 210 0 13 Mar 2017
Deep Probabilistic Programming Dustin Tran Matthew D. Hoffman Rif A. Saurous E. Brevdo Kevin Patrick Murphy David M. Blei BDL 110 193 0 13 Jan 2017
Neural Architecture Search with Reinforcement Learning Barret Zoph Quoc V. Le 478 5,381 0 05 Nov 2016
Inference Compilation and Universal Probabilistic Programming T. Le A. G. Baydin Frank Wood UQCV 194 143 0 31 Oct 2016
Edward: A library for probabilistic modeling, inference, and criticism Dustin Tran A. Kucukelbir Adji Bousso Dieng Maja R. Rudolph Dawen Liang David M. Blei 80 300 0 31 Oct 2016
Deep Amortized Inference for Probabilistic Programs Daniel E. Ritchie Paul Horsfall Noah D. Goodman TPM 103 82 0 18 Oct 2016
A New Approach to Probabilistic Programming Inference Frank Wood Jan-Willem van de Meent Vikash K. Mansinghka 61 347 0 03 Jul 2015
Parallelizing MCMC with Random Partition Trees Xiangyu Wang Fangjian Guo Katherine A. Heller David B. Dunson 92 76 0 10 Jun 2015
Scalable Bayesian Optimization Using Deep Neural Networks Jasper Snoek Oren Rippel Kevin Swersky Ryan Kiros N. Satish N. Sundaram Md. Mostofa Ali Patwary P. Prabhat Ryan P. Adams BDL UQCV 95 1,045 0 19 Feb 2015
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints José Miguel Hernández-Lobato M. Gelbart Matthew W. Hoffman Ryan P. Adams Zoubin Ghahramani 66 153 0 18 Feb 2015
Venture: a higher-order probabilistic programming platform with programmable inference Vikash K. Mansinghka Daniel Selsam Yura N. Perov 71 256 0 01 Apr 2014
Asymptotically Exact, Embarrassingly Parallel MCMC Willie Neiswanger Chong-Jun Wang Eric Xing FedML 89 330 0 19 Nov 2013
Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek Hugo Larochelle Ryan P. Adams 373 7,957 0 13 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 171 4,309 0 18 Nov 2011