Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Near-Optimality of Contrastive Divergence Algorithms
Authors: Pierre Glaser, Kevin Han Huang, Arthur Gretton
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We perform a non-asymptotic analysis of the contrastive divergence (CD) algorithm, a training method for unnormalized models. While prior work has established that (for exponential family distributions) the CD iterates asymptotically converge at an O(n 1/3) rate to the true parameter of the data distribution, we show, under some regularity assumptions, that CD can achieve the parametric rate O(n 1/2). |
| Researcher Affiliation | Academia | Pierre Glaser Kevin Han Huang Arthur Gretton Gatsby Computational Neuroscience Unit, University College London EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Online CD |
| Open Source Code | No | The paper does not include experiments, and thus no code or data. |
| Open Datasets | No | The paper is a theoretical analysis and does not involve empirical studies with specific datasets for training or evaluation. |
| Dataset Splits | No | The paper is a theoretical analysis and does not involve empirical studies with specific dataset splits for validation. |
| Hardware Specification | No | The paper is a theoretical analysis and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is a theoretical analysis and does not list specific software dependencies with version numbers for experimental replication. |
| Experiment Setup | No | The paper is a theoretical analysis and does not describe an experimental setup, hyperparameters, or system-level training settings. |