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..
Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation
Authors: David Holzmüller, Francis Bach
JMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental study nonetheless confirms practical differences between the convergence rates of some of the investigated efficient algorithms, although it is limited to a toy problem and simple algorithms. |
| Researcher Affiliation | Academia | David Holzmüller david dot holzmuller at inria.fr Francis Bach EMAIL INRIA Ecole Normale Supérieure PSL Research University |
| Pseudocode | Yes | Algorithm 1 Rejection sampling with proposal distribution Pg limited to n function evaluations. ... Algorithm 2 Bisection sampling algorithm using a log-partition algorithm L. |
| Open Source Code | Yes | Our plots can be reproduced using the code at github.com/dholzmueller/sampling_experiments |
| Open Datasets | No | To further investigate the convergence behavior of some simple algorithms, we study them numerically on functions of the form f : [0, 1]3 R, x 7 β(x1 + x2 + x3). While these functions are simple (and concave), they pose a challenge to some general algorithms as they have a large range in relation to their Lipschitz constant. |
| Dataset Splits | No | The paper uses a synthetic function for its experiments (functions of the form f : [0, 1]3 R, x 7 β(x1 + x2 + x3)) and does not mention any dataset splits for this synthetic data, nor for any external dataset. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments are mentioned in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) are mentioned in the paper. |
| Experiment Setup | Yes | Figure 2: Convergence of the (median) error |Lf Lf| for different values of β {0.1, 40, 10000}. For the stochastic methods MC and PC+MC, the median is taken over 10001 independent runs. ... Figure 3: Convergence of different sampling methods in terms of the empirical energy distance, computed using N = 106 samples for each distribution, to the true distribution Pf for β = 15. |