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1810.01539
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Automated learning with a probabilistic programming language: Birch
2 October 2018
Lawrence M. Murray
Thomas B. Schon
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Papers citing
"Automated learning with a probabilistic programming language: Birch"
25 / 25 papers shown
Automatic Rao-Blackwellization for Sequential Monte Carlo with Belief Propagation
Waïss Azizian
Guillaume Baudart
Marc Lelarge
91
3
0
15 Dec 2023
Rethinking Variational Inference for Probabilistic Programs with Stochastic Support
Neural Information Processing Systems (NeurIPS), 2023
Tim Reichelt
C. Ong
Tom Rainforth
280
3
0
01 Nov 2023
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Tim Reichelt
C.-H. Luke Ong
Tom Rainforth
278
0
0
23 Oct 2023
Automatically Marginalized MCMC in Probabilistic Programming
International Conference on Machine Learning (ICML), 2023
Jinlin Lai
Javier Burroni
Hui Guan
Daniel Sheldon
288
4
0
01 Feb 2023
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method
IEEE Control Systems (IEEE Control Syst. Mag.), 2022
A. Wigren
Johan Wågberg
Fredrik Lindsten
A. Wills
Thomas B. Schon
215
16
0
26 Oct 2022
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters
A. Wigren
Fredrik Lindsten
262
0
0
13 Oct 2022
Nested Variational Inference
Neural Information Processing Systems (NeurIPS), 2021
Heiko Zimmermann
Hao Wu
Babak Esmaeili
Jan-Willem van de Meent
BDL
230
24
0
21 Jun 2021
Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently
Conference on Uncertainty in Artificial Intelligence (UAI), 2021
Tim Reichelt
Adam Goliñski
C.-H. Luke Ong
Tom Rainforth
TPM
182
0
0
09 Jun 2021
Learning Proposals for Probabilistic Programs with Inference Combinators
Conference on Uncertainty in Artificial Intelligence (UAI), 2021
Sam Stites
Heiko Zimmermann
Hao Wu
Eli Sennesh
Jan-Willem van de Meent
NAI
357
18
0
01 Mar 2021
Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs
I. Hasuo
Yuichiro Oyabu
Clovis Eberhart
Kohei Suenaga
Kenta Cho
Shin-ya Katsumata
TPM
226
3
0
05 Jan 2021
Conditional independence by typing
Maria I. Gorinova
Andrew D. Gordon
Charles Sutton
Matthijs Vákár
253
21
0
22 Oct 2020
An invitation to sequential Monte Carlo samplers
Chenguang Dai
J. Heng
Pierre E. Jacob
N. Whiteley
473
83
0
23 Jul 2020
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Alexander K. Lew
Monica Agrawal
David Sontag
Vikash K. Mansinghka
597
36
0
23 Jul 2020
Stochastically Differentiable Probabilistic Programs
David Tolpin
Yuanshuo Zhou
Hongseok Yang
BDL
245
0
0
02 Mar 2020
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
Mohamed Tarek
Kai Xu
Martin Trapp
Hong Ge
Zoubin Ghahramani
173
8
0
07 Feb 2020
Lazy object copy as a platform for population-based probabilistic programming
Lawrence M. Murray
181
5
0
09 Jan 2020
Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continua
Journal of Statistical Software (JSS), 2019
Alexandre Bouchard-Côté
Kevin Chern
Davor Cubranic
Sahand Hosseini
Justin Hume
Matteo Lepur
Zihui Ouyang
G. Sgarbi
163
7
0
22 Dec 2019
Parameter elimination in particle Gibbs sampling
Neural Information Processing Systems (NeurIPS), 2019
A. Wigren
Riccardo Sven Risuleo
Lawrence M. Murray
Fredrik Lindsten
300
17
0
30 Oct 2019
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
International Conference on Machine Learning (ICML), 2019
Yuanshuo Zhou
Hongseok Yang
Yee Whye Teh
Tom Rainforth
TPM
366
20
0
29 Oct 2019
Functional Tensors for Probabilistic Programming
F. Obermeyer
Eli Bingham
M. Jankowiak
Du Phan
Jonathan P. Chen
215
20
0
23 Oct 2019
Particle filter with rejection control and unbiased estimator of the marginal likelihood
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019
J. Kudlicka
Lawrence M. Murray
Thomas B. Schon
Fredrik Lindsten
353
3
0
21 Oct 2019
Probabilistic Models with Deep Neural Networks
A. Masegosa
Rafael Cabañas
H. Langseth
Thomas D. Nielsen
Antonio Salmerón
BDL
314
16
0
09 Aug 2019
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
Conference on Uncertainty in Artificial Intelligence (UAI), 2019
J. Kudlicka
Lawrence M. Murray
F. Ronquist
Thomas B. Schon
281
10
0
10 Jul 2019
Deployable probabilistic programming
SIGPLAN symposium on New ideas, new paradigms, and reflections on programming and software (Onward!), 2019
David Tolpin
TPM
297
7
0
20 Jun 2019
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
Guillaume Baudart
Javier Burroni
Martin Hirzel
Louis Mandel
Avraham Shinnar
BDL
423
4
0
30 Sep 2018
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