Minimum Stein Discrepancy Estimators
Authors: Alessandro Barp, Francois-Xavier Briol, Andrew Duncan, Mark Girolami, Lester Mackey
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate this advantage for several challenging problems for score matching, such as non-smooth, heavy-tailed or light-tailed densities. |
| Researcher Affiliation | Collaboration | Alessandro Barp Department of Mathematics Imperial College London a.barp16@imperial.ac.uk; François-Xavier Briol Department of Statistical Science University College London f.briol@ucl.ac.uk; Andrew B. Duncan Department of Mathematics Imperial College London a.duncan@imperial.ac.uk; Mark Girolami Department of Engineering University of Cambridge mag92@eng.cam.ac.uk; Lester Mackey Microsoft Research Cambridge, MA, USA lmackey@microsoft.com |
| Pseudocode | No | The paper describes algorithms and derivations in prose and mathematical notation but does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or provide a link to a code repository. |
| Open Datasets | No | The numerical experiments are conducted on data generated from specified distributions (e.g., symmetric Bessel distributions, non-standardised student-t distributions, generalised Gamma distributions). The paper does not provide access information (link, citation with author/year, or mention of standard public datasets) for any pre-existing, publicly available datasets. |
| Dataset Splits | No | The paper does not specify any train/validation/test dataset splits, sample counts for splits, or reference predefined splits for reproducibility. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies or their version numbers (e.g., 'Python 3.8', 'PyTorch 1.9') required to reproduce the experiments. |
| Experiment Setup | Yes | In the numerical experiments, specific details are provided such as 'Both algorithms use constant stepsizes and minibatches of size 50.' (Section 4.2) and 'We set n = 300 and corrupt 80 points by setting them to the value x = 8.' (Section 4.3). |