Constrained Sampling with Primal-Dual Langevin Monte Carlo
Authors: Luiz Chamon, Mohammad Reza Karimi Jaghargh, Anna Korba
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the relevance and effectiveness of PD-LMC in several applications. |
| Researcher Affiliation | Academia | Luiz F. O. Chamon University of Stuttgart luiz.chamon@simtech.uni-stuttgart.de Mohammad Reza Karimi ETH Zürich mkarimi@inf.ethz.ch Anna Korba CREST, ENSAE, IP Paris anna.korba@ensae.fr |
| Pseudocode | Yes | Algorithm 1 Primal-dual LMC |
| Open Source Code | Yes | Code for these examples is publicly available at https://www.github.com/lfochamon/pdlmc. |
| Open Datasets | Yes | The N = 32561 data points in the training set are composed of d = 62 socio-economical features (x Rd, including the intercept) and the goal is to predict whether the individual makes more than US$ 50000 per year (y {0, 1}). |
| Dataset Splits | No | The N = 32561 data points in the training set are composed of d = 62 socio-economical features... We find that, while the probability of positive outputs is 19.1% across the whole test set... |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., GPU/CPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper mentions 'Python code' will be made public (in the NeurIPS checklist), but it does not specify any software dependencies with version numbers within the main paper or its appendices. |
| Experiment Setup | Yes | In these experiments, we start all chains at zero (unless stated otherwise) and use different step-sizes for each of the updates in steps 3 5 from Algorithm 1. We refer to them as ηx, ηλ, and ην. In contrast, we do not use diminishing step-sizes. |