Yura Perov

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All published works

Action	Title	Year	Authors
+	Learning medical triage from clinicians using Deep Q-Learning.	2020	Albert Buchard Baptiste Bouvier Giulia Prando Rory Beard Michail Livieratos Dan Busbridge Daniel H. Thompson Jonathan G. Richens Yuanzhao Zhang Adam Baker
+	Learning medical triage from clinicians using Deep Q-Learning	2020	Albert Buchard Baptiste Bouvier Giulia Prando Rory Beard Michail Livieratos Dan Busbridge Daniel B. Thompson Jonathan G. Richens Yuanzhao Zhang Adam Baker
+	Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs	2019	Robert Walecki Kostis Gourgoulias Adam Baker Chris Hart Chris Lucas Max Zwiessele Albert Buchard María Lomelí Yura Perov Saurabh Johri
+	MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming	2019	Yura Perov Logan Graham Kostis Gourgoulias Jonathan G. Richens Ciarán M. Lee Adam Baker Saurabh Johri
+	A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis	2018	Salman Razzaki Adam Baker Yura Perov Katherine Middleton Janie Baxter Daniel Mullarkey Davinder Sangar Michael Taliercio Mobasher Butt Azeem Majeed
+	Inference Over Programs That Make Predictions	2018	Yura Perov
+	Universal Marginalizer for Amortised Inference and Embedding of Generative Models	2018	Robert Walecki Albert Buchard Kostis Gourgoulias Chris Hart María Lomelí Alexandre K. W. Navarro Max Zwiessele Yura Perov Saurabh Johri
+	A comparative study of artificial intelligence and human doctors for the purpose of triage and diagnosis	2018	Salman Razzaki Adam Baker Yura Perov Katherine Middleton Janie Baxter Daniel J. Mullarkey Davinder Sangar Michael Taliercio Mobasher Butt Azeem Majeed
+	A Universal Marginalizer for Amortized Inference in Generative Models.	2017	Laura Douglas Iliyan Zarov Konstantinos Gourgoulias Chris Lucas Chris Hart Adam Baker Maneesh Sahani Yura Perov Saurabh Johri
+	A Universal Marginalizer for Amortized Inference in Generative Models	2017	Laura Douglas Iliyan Zarov Konstantinos Gourgoulias Chris Lucas Chris Hart Adam Baker Maneesh Sahani Yura Perov Saurabh Johri
+	Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)	2016	Yura Perov
+	Applications of Probabilistic Programming (Master's thesis, 2015)	2016	Yura Perov
+	Spreadsheet Probabilistic Programming	2016	Mike Wu Yura Perov Frank Wood Hongseok Yang
+	Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)	2016	Yura Perov
+	Data-driven Sequential Monte Carlo in Probabilistic Programming	2015	Yura Perov Tuan Anh Le Frank Wood
+	Data-driven Sequential Monte Carlo in Probabilistic Programming	2015	Yura Perov Tuan Anh Le Frank Wood
+	Learning Probabilistic Programs.	2014	Yura Perov Frank Wood
+	Venture: a higher-order probabilistic programming platform with programmable inference	2014	Vikash K. Mansinghka Daniel Selsam Yura Perov
+	Learning Probabilistic Programs	2014	Yura Perov Frank Wood
+	Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs	2013	Vikash K. Mansinghka Tejas D. Kulkarni Yura Perov Joshua B. Tenenbaum

Common Coauthors

Coauthor	Papers Together
Saurabh Johri	9
Adam Baker	5
Kostis Gourgoulias	5
Albert Buchard	4
Frank Wood	4
Adam Baker	3
Jonathan G. Richens	3
Chris Hart	3
Iliyan Zarov	2
Maneesh Sahani	2
Michail Livieratos	2
Azeem Majeed	2
María Lomelí	2
Michael Taliercio	2
Megan Mahoney	2
Baptiste Bouvier	2
Yuanzhao Zhang	2
Katherine Middleton	2
Max Zwiessele	2
Laura Douglas	2
Davinder Sangar	2
Dan Busbridge	2
Janie Baxter	2
Rory Beard	2
Robert Walecki	2
Konstantinos Gourgoulias	2
Vikash K. Mansinghka	2
Arnold DoRosario	2
Tuan Anh Le	2
Chris Lucas	2
Giulia Prando	2
Mike Wu	1
Tejas D. Kulkarni	1
Frank Wood	1
Chris Hart	1
Joshua B. Tenenbaum	1
Mobasher Butt	1
Salman Razzaki	1
Logan Graham	1
Daniel Mullarkey	1
Ciarán M. Lee	1
Daniel B. Thompson	1
Salman Razzaki	1
Hongseok Yang	1
Alexandre K. W. Navarro	1
Daniel J. Mullarkey	1
Daniel H. Thompson	1
Chris Lucas	1
Daniel Selsam	1
Mobasher Butt	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	A Compilation Target for Probabilistic Programming Languages	2014	Brooks Paige Frank Wood	4
+	Venture: a higher-order probabilistic programming platform with programmable inference	2014	Vikash K. Mansinghka Daniel Selsam Yura Perov	4
+	Data-driven Sequential Monte Carlo in Probabilistic Programming	2015	Yura Perov Tuan Anh Le Frank Wood	3
+	Probabilistic Programming in Anglican	2015	David Tolpin Jan-Willem van de Meent Frank Wood	3
+ PDF Chat	Particle Markov Chain Monte Carlo Methods	2010	Christophe Andrieu Arnaud Doucet Roman Holenstein	3
+ PDF Chat	Using synthetic data to train neural networks is model-based reasoning	2017	Tuan Anh Le Atılım Güneş Baydin Robert Zinkov Frank Wood	3
+	Automated Variational Inference in Probabilistic Programming	2013	David Wingate Théophane Weber	3
+	Deep Amortized Inference for Probabilistic Programs	2016	Daniel Ritchie Paul Horsfall Noah D. Goodman	3
+	Exploiting compositionality to explore a large space of model structures	2012	Roger Grosse Ruslan Salakhutdinov William T. Freeman Joshua B. Tenenbaum	3
+	Inference Compilation and Universal Probabilistic Programming	2016	Tuan Anh Le Atılım Güneş Baydin Frank Wood	3
+	A New Approach to Probabilistic Programming Inference	2014	Frank Wood Jan-Willem van de Meent Vikash K. Mansinghka	3
+	Church: a language for generative models	2012	Noah D. Goodman Vikash K. Mansinghka Daniel M. Roy Keith Bonawitz Joshua B. Tenenbaum	2
+	Inducing Probabilistic Programs by Bayesian Program Merging	2011	Irvin Hwang Andreas Stuhlmüller Noah D. Goodman	2
+	Structured Priors for Structure Learning	2012	Vikash K. Mansinghka Charles Kemp Joshua B. Tenenbaum Thomas L. Griffiths	2
+	MADE: Masked Autoencoder for Distribution Estimation	2015	Mathieu Germain Karol Gregor Iain Murray Hugo Larochelle	2
+	Universal Marginalizer for Amortised Inference and Embedding of Generative Models	2018	Robert Walecki Albert Buchard Kostis Gourgoulias Chris Hart María Lomelí Alexandre K. W. Navarro Max Zwiessele Yura Perov Saurabh Johri	2
+	Particle Gibbs with Ancestor Sampling for Probabilistic Programs	2015	Jan-Willem van de Meent Hongseok Yang Vikash K. Mansinghka Frank Wood	2
+	Pyro: Deep Universal Probabilistic Programming	2018	Eli Bingham Jonathan P. Chen Martin Jankowiak Fritz Obermeyer Neeraj Pradhan Theofanis Karaletsos Rohit Singh Paul Szerlip Paul Horsfall Noah D. Goodman	2
+	Applications of Probabilistic Programming (Master's thesis, 2015)	2016	Yura Perov	2
+	Structure Discovery in Nonparametric Regression through Compositional Kernel Search	2013	David Duvenaud James Robert Lloyd Roger Grosse Joshua B. Tenenbaum Zoubin Ghahramani	2
+ PDF Chat	AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential Reasoning in Large Bayesian Networks	2000	J.-J. Cheng Marek J. Drużdżel	2
+	Novel approach to nonlinear/non-Gaussian Bayesian state estimation	1993	Neil Gordon David Salmond A. F. M. Smith	1
+	Image segmentation by data-driven markov chain monte carlo	2002	Zhuowen Tu Song-Chun Zhu	1
+	Using the Propensity Score Method to Estimate Causal Effects	2012	Mingxiang Li	1
+	The BUGS project: Evolution, critique and future directions	2009	David J. Lunn David J. Spiegelhalter Andrew C. Thomas Nicky Best	1
+ PDF Chat	Approximate Bayesian computational methods	2011	Jean‐Michel Marin Pierre Pudlo Christian P. Robert Robin Ryder	1
+	Particle gibbs with ancestor sampling	2014	Fredrik Lindsten Michael I. Jordan Thomas B. Schön	1
+	Structure Discovery in Nonparametric Regression through Compositional Kernel Search	2013	David Duvenaud James Robert Lloyd Roger Grosse Joshua B. Tenenbaum Zoubin Ghahramani	1
+	Any reasonable cost function can be used for a posteriori probability approximation	2002	Marco Saerens Patrice Latinne Christine Decaestecker	1
+ PDF Chat	Sequential Monte Carlo Samplers	2006	Pierre Del Moral Arnaud Doucet Ajay Jasra	1
+ PDF Chat	Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems	2008	Tina Toni David Welch Natalja Strelkowa Andreas Ipsen Michael P. H. Stumpf	1
+	Black Box Variational Inference	2013	Rajesh Ranganath Sean Gerrish David M. Blei	1
+ PDF Chat	The informed sampler: A discriminative approach to Bayesian inference in generative computer vision models	2015	Varun Jampani Sebastian Nowozin Matthew Loper Peter Gehler	1
+	Probability: Theory and Examples.	1992	Kathryn Prewitt Richard Durrett	1
+ PDF Chat	Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs	2015	David Tolpin Jan-Willem van de Meent Brooks Paige Frank Wood	1
+	Generalized Polya Urn for Time-varying Dirichlet Process Mixtures	2012	François Caron Manuel Davy Arnaud Doucet	1
+	Gibbs Sampling in Open-Universe Stochastic Languages	2012	Nimar S. Arora Rodrigo de Salvo Braz Erik B. Sudderth Stuart Russell	1
+	Counterfactual Fairness	2017	Matt J. Kusner Joshua R. Loftus Chris Russell Ricardo Silva	1
+	A Universal Marginalizer for Amortized Inference in Generative Models.	2017	Laura Douglas Iliyan Zarov Konstantinos Gourgoulias Chris Lucas Chris Hart Adam Baker Maneesh Sahani Yura Perov Saurabh Johri	1
+	Reliable Decision Support using Counterfactual Models	2017	Peter Schulam Suchi Saria	1
+	Nesting Probabilistic Programs	2018	Tom Rainforth	1
+	Local Rule-Based Explanations of Black Box Decision Systems.	2018	Riccardo Guidotti Anna Monreale Salvatore Ruggieri Dino Pedreschi Franco Turini Fosca Giannotti	1
+	An Introduction to Probabilistic Programming	2018	Jan-Willem van de Meent Brooks Paige Hongseok Yang Frank Wood	1
+	Simple, Distributed, and Accelerated Probabilistic Programming	2018	Dustin Tran Matthew D. Hoffman Dave Moore Christopher Suter Vasudevan Srinivas Alexey Radul Matthew Johnson Rif A. Saurous	1
+	Learning about an exponential amount of conditional distributions	2019	Mohamed Ishmael Belghazi Maxime Oquab Yann LeCun David López-Paz	1
+	Counterfactual Risk Minimization: Learning from Logged Bandit Feedback	2015	Adith Swaminathan Thorsten Joachims	1
+	Efficient Estimation of Word Representations in Vector Space	2013	Tomáš Mikolov Kai Chen Greg S. Corrado Jay B. Dean	1
+	Design and Implementation of Probabilistic Programming Language Anglican	2016	David Tolpin Jan Willem van de Meent Hongseok Yang Frank Wood	1
+	Neural Adaptive Sequential Monte Carlo	2015	Shixiang Gu Zoubin Ghahramani Richard E. Turner	1
+	MADE: Masked Autoencoder for Distribution Estimation	2015	Mathieu Germain Karol Gregor Iain Murray Hugo Larochelle	1