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..
Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions
Authors: Yin Tat Lee, Ruoqi Shen, Kevin Tian
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give lower bounds on the performance of two of the most popular sampling methods in practice, the Metropolis-adjusted Langevin algorithm (MALA) and multi-step Hamiltonian Monte Carlo (HMC) with a leapfrog integrator, when applied to well-conditioned distributions. Our main result is a nearly-tight lower bound of eā¦(Īŗd) on the mixing time of MALA from an exponentially warm start, matching a line of algorithmic results [DCWY18, CDWY19, LST20a] up to logarithmic factors and answering an open question of [CLA+20]. |
| Researcher Affiliation | Collaboration | Yin Tat Lee University of Washington and Microsoft Research EMAIL Ruoqi Shen University of Washington EMAIL Kevin Tian Stanford University EMAIL |
| Pseudocode | No | The paper describes algorithms (MALA, HMC) using mathematical formulations and textual descriptions but does not provide structured pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link or explicit statement of code release) for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve empirical studies with datasets, thus no information on public dataset access is relevant or provided. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical studies with dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experimental setup involving specific hardware. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers used for its research. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training configurations. |