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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
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. |