Automatic Reparameterisation of Probabilistic Programs
Authors: Maria Gorinova, Dave Moore, Matthew Hoffman
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare these strategies to a fixed centred and non-centred parameterisation across a range of well-known hierarchical models. Our results suggest that both VIP and i HMC can enable for more automated robust inference, often performing at least as well as the best fixed parameterisation and sometimes better, without requiring a priori knowledge of the optimal parameterisation. |
| Researcher Affiliation | Collaboration | Maria I. Gorinova * 1 Dave Moore 2 Matthew D. Hoffman 2 *Work done while interning at Google. 1University of Edinburgh, Edinburgh, UK 2Google, San Francisco, CA, USA. |
| Pseudocode | Yes | Algorithm 1: Interleaved Hamiltonian Monte Carlo Algorithm 2: Variationally Inferred Parameterisation |
| Open Source Code | Yes | Code for these algorithms and experiments is available at https://github.com/mgorinova/autoreparam. |
| Open Datasets | Yes | Eight schools (Rubin, 1981): estimating the treatment effects θi of a course taught at each of i = 1 . . . 8 schools, given test scores yi and standard errors σi: Radon (Gelman & Hill, 2006): hierarchical linear regression, in which the radon level ri in a home i in county c is modelled as a function of the (unobserved) county-level effect mc, the county uranium reading uc, and xi, the number of floors in the home: German credit (Dua & Graff, 2017): logistic regression; hierarchical prior on coefficient scales: Election 88 (Gelman & Hill, 2006): logistic model of 1988 US presidential election outcomes by county, given demographic covariates xi and state-level effects αs: Electric Company (Gelman & Hill, 2006): paired causal analysis of the effect of viewing an educational TV show on each of 192 classforms over G = 4 grades. |
| Dataset Splits | No | The paper does not provide specific dataset splits (e.g., percentages or counts) for training, validation, or testing. It mentions using variational optimization, which serves a validation purpose, but not in terms of dataset partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | We implement reparameterisation handlers in Edward2, a deep PPL embedded in Python and Tensor Flow (Tran et al., 2018). No version numbers for Edward2, Python, or TensorFlow are specified. |
| Experiment Setup | Yes | The HMC step size and number of leapfrog steps were tuned following the procedures described in Appendix C, which also contains additional details of the experimental setup. |