Statistical Efficiency of Score Matching: The View from Isoperimetry
Authors: Frederic Koehler, Alexander Heckett, Andrej Risteski
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 7, we perform several simulations which illustrate the close connection between isoperimetry and the performance of score matching. We give examples both when fitting the parameters of an exponential family and when the score function is fit using a neural network. |
| Researcher Affiliation | Academia | Frederic Koehler Stanford University fkoehler@stanford.edu Alexander Heckett Carnegie Mellon University aheckett@andrew.cmu.edu Andrej Risteski Carnegie Mellon University aristesk@andrew.cmu.edu |
| Pseudocode | No | The paper does not contain any sections or blocks labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of its own source code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper discusses synthetic or generative distributions (e.g., 'a bimodal distribution', 'a mixture of Gaussians') for simulations, but does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper refers to 'training data' and 'finite number of samples n' but does not provide specific details on dataset splits (percentages, counts, or references to standard splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory, or cloud instances) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions) needed to replicate the experiments. |
| Experiment Setup | Yes | Model details: both models illustrated in the figure have 2048 tanh units and are trained via SGD on fresh samples for 300000 steps. |