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..
Asymptotically Optimal Exact Minibatch Metropolis-Hastings
Authors: Ruqi Zhang, A. Feder Cooper, Christopher M. De Sa
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show Tuna MH outperforms other exact minibatch MH methods on robust linear regression, truncated Gaussian mixtures, and logistic regression. 5 Experiments We compare Tuna MH to MH, TFMH, SMH (i.e. TFMH with MAP control variates) and Fly MC. |
| Researcher Affiliation | Academia | Ruqi Zhang Cornell University EMAIL A. Feder Cooper Cornell University EMAIL Christopher De Sa Cornell University EMAIL |
| Pseudocode | Yes | Algorithm 1 Stateless, Energy-Difference-Based Minibatch Metropolis-Hastings, Algorithm 2 Tuna MH |
| Open Source Code | Yes | We released the code at https://github.com/ruqizhang/tunamh. |
| Open Datasets | Yes | Lastly we apply Tuna MH to logistic regression on the MNIST image dataset of handwritten number digits. |
| Dataset Splits | No | The paper mentions dataset sizes (e.g., N = 10^6 for Gaussian mixture, MNIST) but does not provide specific train/validation/test split percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models or types of computing resources used for the experiments. |
| Software Dependencies | No | We coded each method in Julia; our implementations are at least as fast as, if not faster than, prior implementations. |
| Experiment Setup | Yes | We tune the proposal stepsize separately for each method to reach a target acceptance rate, and report averaged results and standard error from the mean over three runs. We set χ to be roughly the largest value that keeps χC2M 2(θ, θ ) < 1 in most steps; we keep χ as high as possible while the average batch size is around its lower bound CM(θ, θ ). We found this strategy works well in practice. |