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Church: a language for generative models
v1v2 (latest)

Church: a language for generative models

Conference on Uncertainty in Artificial Intelligence (UAI), 2008
13 June 2012
Noah D. Goodman
Vikash K. Mansinghka
Daniel M. Roy
Keith Bonawitz
J. Tenenbaum
ArXiv (abs)PDFHTML

Papers citing "Church: a language for generative models"

50 / 179 papers shown
Lumos: Let there be Language Model System Certification
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Isha Chaudhary
Vedaant V. Jain
Avaljot Singh
Kavya Sachdeva
Sayan Ranu
Gagandeep Singh
Gagandeep Singh
ELMLRM
107
0
0
02 Dec 2025
SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
Zhenyu Yu
Mohd Yamani Idna Idris
Pei Wang
Yuelong Xia
Fei Ma
Rizwan Qureshi
263
6
0
06 Jun 2025
LLM-Guided Probabilistic Program Induction for POMDP Model Estimation
LLM-Guided Probabilistic Program Induction for POMDP Model Estimation
Aidan Curtis
Hao Tang
Thiago Veloso
Kevin Ellis
Joshua B. Tenenbaum
Tomás Lozano-Pérez
Leslie Pack Kaelbling
997
6
0
04 May 2025
A Distribution Semantics for Probabilistic Term Rewriting
A Distribution Semantics for Probabilistic Term Rewriting
Germán Vidal
359
0
0
19 Oct 2024
Building Machines that Learn and Think with People
Building Machines that Learn and Think with People
Katherine M. Collins
Ilia Sucholutsky
Umang Bhatt
Kartik Chandra
Lionel Wong
...
Mark K. Ho
Vikash K. Mansinghka
Adrian Weller
Joshua B. Tenenbaum
Thomas Griffiths
430
104
0
22 Jul 2024
Element-Free Probability Distributions and Random Partitions
Element-Free Probability Distributions and Random Partitions
Victor Blanchi
Hugo Paquet
175
1
0
27 May 2024
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable
  AI Systems
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
David Dalrymple
Joar Skalse
Yoshua Bengio
Stuart J. Russell
Max Tegmark
...
Clark Barrett
Ding Zhao
Zhi-Xuan Tan
Jeannette Wing
Joshua Tenenbaum
402
106
0
10 May 2024
Foundation Model Sherpas: Guiding Foundation Models through Knowledge
  and Reasoning
Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning
D. Bhattacharjya
Junkyu Lee
Don Joven Agravante
Balaji Ganesan
Radu Marinescu
LLMAG
280
4
0
02 Feb 2024
Recovering Mental Representations from Large Language Models with Markov
  Chain Monte Carlo
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo
Jian-Qiao Zhu
Haijiang Yan
Thomas Griffiths
255
3
0
30 Jan 2024
Exploring the hierarchical structure of human plans via program
  generation
Exploring the hierarchical structure of human plans via program generation
Carlos G. Correa
Sophia Sanborn
Mark K. Ho
Frederick Callaway
Nathaniel D. Daw
Thomas Griffiths
203
18
0
30 Nov 2023
Rethinking Variational Inference for Probabilistic Programs with
  Stochastic Support
Rethinking Variational Inference for Probabilistic Programs with Stochastic SupportNeural Information Processing Systems (NeurIPS), 2023
Tim Reichelt
C. Ong
Tom Rainforth
278
3
0
01 Nov 2023
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs
  with Stochastic Support
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic SupportInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Tim Reichelt
C.-H. Luke Ong
Tom Rainforth
274
0
0
23 Oct 2023
Worst-Case Analysis is Maximum-A-Posteriori Estimation
Worst-Case Analysis is Maximum-A-Posteriori Estimation
Hongjun Wu
Di Wang
167
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0
15 Oct 2023
A Heavy-Tailed Algebra for Probabilistic Programming
A Heavy-Tailed Algebra for Probabilistic ProgrammingNeural Information Processing Systems (NeurIPS), 2023
Feynman T. Liang
Liam Hodgkinson
Michael W. Mahoney
285
4
0
15 Jun 2023
Push: Concurrent Probabilistic Programming for Bayesian Deep Learning
Push: Concurrent Probabilistic Programming for Bayesian Deep Learning
Daniel Huang
Christian Camaño
Jonathan Tsegaye
Jonathan Austin Gale
AI4CE
302
0
0
10 Jun 2023
Human-like Few-Shot Learning via Bayesian Reasoning over Natural
  Language
Human-like Few-Shot Learning via Bayesian Reasoning over Natural LanguageNeural Information Processing Systems (NeurIPS), 2023
Kevin Ellis
BDLLRM
379
25
0
05 Jun 2023
Evaluating statistical language models as pragmatic reasoners
Evaluating statistical language models as pragmatic reasonersAnnual Meeting of the Cognitive Science Society (CogSci), 2023
Benjamin Lipkin
L. Wong
Gabriel Grand
J. Tenenbaum
384
19
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01 May 2023
Meta-Learned Models of Cognition
Meta-Learned Models of CognitionBehavioral and Brain Sciences (BBS), 2023
Marcel Binz
Ishita Dasgupta
Akshay K. Jagadish
M. Botvinick
Jane X. Wang
Eric Schulz
370
38
0
12 Apr 2023
Declarative Probabilistic Logic Programming in Discrete-Continuous
  Domains
Declarative Probabilistic Logic Programming in Discrete-Continuous DomainsArtificial Intelligence (AIJ), 2023
Pedro Zuidberg Dos Martires
Luc de Raedt
Angelika Kimmig
302
6
0
21 Feb 2023
Thermodynamic AI and the fluctuation frontier
Thermodynamic AI and the fluctuation frontierInternational Conference on Rebooting Computing (ICRC), 2023
Patrick J. Coles
Collin Szczepanski
Denis Melanson
Kaelan Donatella
Antonio J. Martinez
Faris M. Sbahi
AI4CE
313
31
0
09 Feb 2023
Counterfactual Identifiability of Bijective Causal Models
Counterfactual Identifiability of Bijective Causal ModelsInternational Conference on Machine Learning (ICML), 2023
Arash Nasr-Esfahany
MohammadIman Alizadeh
Devavrat Shah
CMLBDL
531
41
0
04 Feb 2023
Automatically Marginalized MCMC in Probabilistic Programming
Automatically Marginalized MCMC in Probabilistic ProgrammingInternational Conference on Machine Learning (ICML), 2023
Jinlin Lai
Javier Burroni
Hui Guan
Daniel Sheldon
288
4
0
01 Feb 2023
Nonparametric Involutive Markov Chain Monte Carlo
Nonparametric Involutive Markov Chain Monte CarloInternational Conference on Machine Learning (ICML), 2022
Carol Mak
Fabian Zaiser
C. Ong
340
2
0
02 Nov 2022
Nonlinear System Identification: Learning while respecting physical
  models using a sequential Monte Carlo method
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo methodIEEE Control Systems (IEEE Control Syst. Mag.), 2022
A. Wigren
Johan Wågberg
Fredrik Lindsten
A. Wills
Thomas B. Schon
209
16
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26 Oct 2022
Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs
Swift Markov Logic for Probabilistic Reasoning on Knowledge GraphsTheory and Practice of Logic Programming (TPLP), 2022
Luigi Bellomarini
Eleonora Laurenza
Emanuel Sallinger
Evgeny Sherkhonov
198
0
0
01 Oct 2022
Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept
  Statistics
Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics
Masataro Asai
742
0
0
08 Sep 2022
Smoothness Analysis for Probabilistic Programs with Application to
  Optimised Variational Inference
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
Wonyeol Lee
Xavier Rival
Hongseok Yang
373
13
0
22 Aug 2022
Tensor Program Optimization with Probabilistic Programs
Tensor Program Optimization with Probabilistic ProgramsNeural Information Processing Systems (NeurIPS), 2022
Junru Shao
Xiyou Zhou
Siyuan Feng
Bohan Hou
Ruihang Lai
Hongyi Jin
Wuwei Lin
Masahiro Masuda
Cody Hao Yu
Tianqi Chen
362
57
0
26 May 2022
Foundation Posteriors for Approximate Probabilistic Inference
Foundation Posteriors for Approximate Probabilistic InferenceNeural Information Processing Systems (NeurIPS), 2022
Mike Wu
Noah D. Goodman
UQCV
264
7
0
19 May 2022
Guaranteed Bounds for Posterior Inference in Universal Probabilistic
  Programming
Guaranteed Bounds for Posterior Inference in Universal Probabilistic ProgrammingACM-SIGPLAN Symposium on Programming Language Design and Implementation (PLDI), 2022
Raven Beutner
Luke Ong
Fabian Zaiser
286
20
0
06 Apr 2022
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs
  via PAC-Bayes Objectives
Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives
J. Warrell
M. Gerstein
GP
281
1
0
30 Mar 2022
Mixed Nondeterministic-Probabilistic Automata: Blending graphical
  probabilistic models with nondeterminism
Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminismDiscrete event dynamic systems (DEDS), 2022
A. Benveniste
Jean-Baptiste Raclet
TPM
142
1
0
19 Jan 2022
Programming with Neural Surrogates of Programs
Programming with Neural Surrogates of Programs
Alex Renda
Yi Ding
Michael Carbin
154
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0
12 Dec 2021
Unifying AI Algorithms with Probabilistic Programming using Implicitly
  Defined Representations
Unifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations
Avi Pfeffer
M. Harradon
Joseph Campolongo
S. Cvijic
213
3
0
05 Oct 2021
Supervised Bayesian Specification Inference from Demonstrations
Supervised Bayesian Specification Inference from Demonstrations
Ankit J. Shah
Pritish Kamath
Shen Li
Patrick L. Craven
Kevin J. Landers
Kevin B. Oden
J. Shah
308
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0
06 Jul 2021
Nonparametric Hamiltonian Monte Carlo
Nonparametric Hamiltonian Monte CarloInternational Conference on Machine Learning (ICML), 2021
Carol Mak
Fabian Zaiser
C.-H. Luke Ong
220
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Expectation Programming: Adapting Probabilistic Programming Systems to
  Estimate Expectations Efficiently
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations EfficientlyConference on Uncertainty in Artificial Intelligence (UAI), 2021
Tim Reichelt
Adam Goliñski
C.-H. Luke Ong
Tom Rainforth
TPM
181
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0
09 Jun 2021
A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive
  Architectures for Developmental Robots
A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental RobotsNeural Networks (NN), 2021
T. Taniguchi
Hiroshi Yamakawa
Takayuki Nagai
Kenji Doya
M. Sakagami
Masahiro Suzuki
Tomoaki Nakamura
Akira Taniguchi
322
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15 Mar 2021
Meta-Learning an Inference Algorithm for Probabilistic Programs
Meta-Learning an Inference Algorithm for Probabilistic Programs
Gwonsoo Che
Hongseok Yang
TPM
344
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01 Mar 2021
Learning Proposals for Probabilistic Programs with Inference Combinators
Learning Proposals for Probabilistic Programs with Inference CombinatorsConference on Uncertainty in Artificial Intelligence (UAI), 2021
Sam Stites
Heiko Zimmermann
Hao Wu
Eli Sennesh
Jan-Willem van de Meent
NAI
351
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01 Mar 2021
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal
  Inference
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference
Sam Witty
David D. Jensen
Vikash K. Mansinghka
CML
193
4
0
23 Feb 2021
Automatic variational inference with cascading flows
Automatic variational inference with cascading flowsInternational Conference on Machine Learning (ICML), 2021
Luca Ambrogioni
Gianluigi Silvestri
Marcel van Gerven
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210
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Universal Policies for Software-Defined MDPs
Universal Policies for Software-Defined MDPs
Daniel Selsam
Jesse Michael Han
L. D. Moura
Patrice Godefroid
216
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21 Dec 2020
Modeling Content and Context with Deep Relational Learning
Modeling Content and Context with Deep Relational LearningTransactions of the Association for Computational Linguistics (TACL), 2020
Maria Leonor Pacheco
Dan Goldwasser
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288
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0
20 Oct 2020
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic
  Programmed Deep Kernels
Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels
Alexander Lavin
BDLMedIm
372
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0
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PClean: Bayesian Data Cleaning at Scale with Domain-Specific
  Probabilistic Programming
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic ProgrammingInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Alexander K. Lew
Monica Agrawal
David Sontag
Vikash K. Mansinghka
596
36
0
23 Jul 2020
Automating Involutive MCMC using Probabilistic and Differentiable
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Automating Involutive MCMC using Probabilistic and Differentiable Programming
Marco F. Cusumano-Towner
Alexander K. Lew
Vikash K. Mansinghka
356
21
0
20 Jul 2020
Smart Choices and the Selection Monad
Smart Choices and the Selection MonadLogic in Computer Science (LICS), 2020
M. Abadi
G. Plotkin
466
4
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Symbolic Logic meets Machine Learning: A Brief Survey in Infinite
  Domains
Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domains
Vaishak Belle
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304
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Simulation-Based Inference for Global Health Decisions
Simulation-Based Inference for Global Health Decisions
Christian Schroeder de Witt
Bradley Gram-Hansen
Nantas Nardelli
Andrew Gambardella
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A. B. Espinosa-González
A. Darzi
Juil Sock
A. G. Baydin
AI4CE
231
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0
14 May 2020
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