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..
Efficient constrained sampling via the mirror-Langevin algorithm
Authors: Kwangjun Ahn, Sinho Chewi
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also corroborate our theoretical findings with numerical experiments. We also perform a numerical experiment to compare the practical performance of MLA with PLA. ... we plot the error θk θ 2 in Figure 2, averaged over 10 trials. |
| Researcher Affiliation | Academia | Kwangjun Ahn Department of EECS Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL Sinho Chewi Department of Mathematics Massachusetts Institute of Technology Cambridge, MA 02139 EMAIL |
| Pseudocode | Yes | The mirror-Langevin algorithm (MLA): Xk+1/2 := arg min x Q [ η V (Xk), x + Dφ(x, Xk)] , (MLA:1) Xk+1 := φ (Wη) , where d Wt = 2 [ 2φ (Wt)] 1/2 d Bt , W0 = φ(Xk+1/2) . (MLA:2) |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper uses a synthetically generated dataset: 'we generate 1000 i.i.d. pairs (Xi, Yi) where Xi is sampled uniformly from the ℓ1 ball and Yi is generated from Xi according to (5.1) with θ = θ .' |
| Dataset Splits | No | The paper describes generating synthetic data for numerical experiments but does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We generate 30 samples using both MLA and PLA (both with step size η = 0.005). At each iteration, we average the samples to obtain an estimate θk for the posterior mean, and we plot the error θk θ 2 in Figure 2, averaged over 10 trials. We implement MLA:2 by performing 10 inner iterations of an Euler-Maruyama discretization. |