mlsanityData to 7 Oct 2026

Simple, Distributed, and Accelerated Probabilistic Programming

arXiv 1811.02091 ↗First posted 5 Nov 2018stat.ML cs.LG cs.PLcited by 18

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Abstract (arXiv)

We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single abstraction---the random variable. Our lightweight implementation in TensorFlow enables numerous applications: a model-parallel variational auto-encoder (VAE) with 2nd-generation tensor processing units (TPUv2s); a data-parallel autoregressive model (Image Transformer) with TPUv2s; and multi-GPU No-U-Turn Sampler (NUTS). For both a state-of-the-art VAE on 64x64 ImageNet and Image Transformer on 256x256 CelebA-HQ, our approach achieves an optimal linear speedup from 1 to 256 TPUv2 chips. With NUTS, we see a 100x speedup on GPUs over Stan and 37x over PyMC3.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2018.

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Citing papers per year: 2020: 1; 2021: 6; 2022: 6; 2023: 2; 2024: 2; 2025: 1.

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  1. 2022Plex: Towards Reliability using Pretrained Large Model Extensionscited by 102
  2. 2022Tensor Program Optimization with Probabilistic Programscited by 30
  3. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21
  4. 2021Learning Proposals for Probabilistic Programs with Inference Combinatorscited by 6
  5. 2021TyXe: Pyro-based Bayesian neural nets for Pytorchcited by 6
  6. 2022Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inferencecited by 5
  7. 2022A Survey on Uncertainty Toolkits for Deep Learningcited by 4
  8. 2024Probabilistic Programming with Programmable Variational Inferencecited by 4
  9. 2021D3p -- A Python Package for Differentially-Private Probabilistic Programmingcited by 2
  10. 2023A Heavy-Tailed Algebra for Probabilistic Programmingcited by 2
  11. 2020Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learningcited by 1
  12. 2021Meta-Learning an Inference Algorithm for Probabilistic Programscited by 1
6 more
  1. 2022Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectivescited by 1
  2. 2021Variational Inference for Category Recommendation in E-Commerce platformscited by 0
  3. 2022BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Datacited by 0
  4. 2023Push: Concurrent Probabilistic Programming for Bayesian Deep Learningcited by 0
  5. 2024YAMLE: Yet Another Machine Learning Environmentcited by 0
  6. 2025Representing Molecules with Algebraic Data Types: Beyond SMILES and SELFIEScited by 0