The Boomerang Sampler
Authors: Joris Bierkens, Sebastiano Grazzi, Kengo Kamatani, Gareth Roberts
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirically that it can out-perform existing benchmark piecewise deterministic Markov processes such as the bouncy particle sampler and the Zig-Zag. In the Bayesian statistics context, these competitor algorithms are of substantial interest in the large data context due to the fact that they can adopt data subsampling techniques which are exact (ie induce no error in the stationary distribution). We demonstrate theoretically and empirically that we can also construct a control-variate subsampling boomerang sampler which is also exact, and which possesses remarkable scaling properties in the large data limit. |
| Researcher Affiliation | Academia | 1Technische Universiteit Delft, Netherlands 2Graduate School of Engineering Science, Osaka University, Japan 3Department of Statistics, University of Warwick, United Kingdom. |
| Pseudocode | No | The paper describes the dynamics and theoretical aspects of the Boomerang Sampler but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code used to carry out all of the experiments of this paper may be found online at https://github.com/ jbierkens/ICML-boomerang. |
| Open Datasets | No | The boxplots are taken over 20 randomly generated experiments, where each experiment corresponds to a logistic regression problem with a random (standard normal) parameter, based on randomly generated data from the model. The paper does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | No | The paper only vaguely mentions 'standard desktop computer' without providing specific hardware details such as CPU/GPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | The refreshment rates for BPS and the Boomerang Samplers are taken to be 0.1. [...] The time horizon is throughout fixed at 10, 000 (with 10,000 iterations for MALA). [...] The refreshment rate relative to each coefficient xi,j is fixed to λrefr,i,j = 0.01 and the truncation of the expansion is N = 6. |