Automatic Reparameterisation Of Probabilistic Programs

Media Summary: Abstract from Maria: Markov chain Monte Carlo (MCMC) algorithms can be used to approximate a Joost-Pieter Katoen (RWTH Aachen University) Synthesis of Models and Systems. Recorded at the ML in PL 2019 Conference, the University of Warsaw, 22-24 November 2019. Martin Jankowiak (Uber AI Labs) ...

Automatic Reparameterisation Of Probabilistic Programs - Detailed Analysis & Overview

Abstract from Maria: Markov chain Monte Carlo (MCMC) algorithms can be used to approximate a Joost-Pieter Katoen (RWTH Aachen University) Synthesis of Models and Systems. Recorded at the ML in PL 2019 Conference, the University of Warsaw, 22-24 November 2019. Martin Jankowiak (Uber AI Labs) ...

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Automatic Reparameterisation of Probabilistic Programs

Towards the Automated Synthesis of Probabilistic Programs

Maria I. Gorinova: Automatic Reparameterisation in Probabilistic Programming

#17 Reparametrize Your Models Automatically, with Maria Gorinova

Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling

Tutorial: Probabilistic Programming

Fritz Obermeyer - Probabilistic Programming and Readable Models | PyData Yerevan 2022

Automatic Transformation of Bayesian Probabilistic Models Into Interactive Visualizations

Paradigms of Probabilistic Programming

Martin Jankowiak - Brief Introduction to Probabilistic Programming

(MLTrain@UAI2018 Pyro) Deep Probabilistic Programming 101: The Variational Autoencoder

[LAFI'23] On the Reparameterisation Gradient for Non-Differentiable but Continuous Models

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Automatic Reparameterisation of Probabilistic Programs

Automatic Reparameterisation of Probabilistic Programs

Abstract from Maria: Markov chain Monte Carlo (MCMC) algorithms can be used to approximate a

Towards the Automated Synthesis of Probabilistic Programs

Towards the Automated Synthesis of Probabilistic Programs

Joost-Pieter Katoen (RWTH Aachen University) https://simons.berkeley.edu/talks/tbd-313 Synthesis of Models and Systems.

Maria I. Gorinova: Automatic Reparameterisation in Probabilistic Programming

Maria I. Gorinova: Automatic Reparameterisation in Probabilistic Programming

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#17 Reparametrize Your Models Automatically, with Maria Gorinova

#17 Reparametrize Your Models Automatically, with Maria Gorinova

... GitHub: https://github.com/mgorinova

Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling

Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling

Paper and supplementary material: ...

Tutorial: Probabilistic Programming

Tutorial: Probabilistic Programming

Probabilistic programming

Fritz Obermeyer - Probabilistic Programming and Readable Models | PyData Yerevan 2022

Fritz Obermeyer - Probabilistic Programming and Readable Models | PyData Yerevan 2022

Fritz Obermeyer Presents:

Automatic Transformation of Bayesian Probabilistic Models Into Interactive Visualizations

Automatic Transformation of Bayesian Probabilistic Models Into Interactive Visualizations

Speaker: Evdoxia Taka Title:

Paradigms of Probabilistic Programming

Paradigms of Probabilistic Programming

Presented at SPLASH-I 2018

Martin Jankowiak - Brief Introduction to Probabilistic Programming

Martin Jankowiak - Brief Introduction to Probabilistic Programming

Recorded at the ML in PL 2019 Conference, the University of Warsaw, 22-24 November 2019. Martin Jankowiak (Uber AI Labs) ...

(MLTrain@UAI2018 Pyro) Deep Probabilistic Programming 101: The Variational Autoencoder

(MLTrain@UAI2018 Pyro) Deep Probabilistic Programming 101: The Variational Autoencoder

This is the video from the

[LAFI'23] On the Reparameterisation Gradient for Non-Differentiable but Continuous Models

[LAFI'23] On the Reparameterisation Gradient for Non-Differentiable but Continuous Models

[LAFI'23] On the

Maria Gorinova Program Analysis of Probabilistic Programs

Maria Gorinova Program Analysis of Probabilistic Programs

... what we um what's the goal of