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 [1].
Federated Sampling with Langevin Algorithm under Isoperimetry
Authors: Lukang Sun, Adil Salim, Peter Richtárik
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A Experiments We now experiment our algorithm and compare it to QLSD++ (Vono et al., 2022). Since this paper is theoretical, we chose a simple one dimensional example, in order to be able to visualize the performance of the algorithms on histograms. |
| Researcher Affiliation | Collaboration | Lukang Sun EMAIL King Abdullah University of Science and Technology Adil Salim EMAIL Microsoft Research Peter Richtárik EMAIL King Abdullah University of Science and Technology |
| Pseudocode | Yes | Algorithm 1 MARINA (Gorbunov et al., 2021) ... Algorithm 2 Langevin-MARINA (proposed algorithm) |
| Open Source Code | No | The paper does not provide any explicit statement about the availability of source code or a link to a code repository. |
| Open Datasets | No | The target distribution is π exp( F), where F is defined by a mathematical function... This is a synthetic setup, not using an external dataset. |
| Dataset Splits | No | The paper uses a synthetic target distribution for its experiments, therefore, traditional dataset splits are not applicable. |
| Hardware Specification | No | The paper describes experimental setup parameters like the number of clients and steps, but it does not specify any hardware details such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions several algorithms and components like MARINA, QLSD++, and a compression operator, but it does not specify any software versions for these or other dependencies. |
| Experiment Setup | Yes | Experimental setup. We use n = 5 clients, and client i has N(i) subfunctions Fij. The number N(i) is randomly chosen between 10 and 20. ... In Langevin-Marina we set p = 0.001, i.e., a full gradient is computing every 1000 iterations in expectation. In QLSD++ (Vono et al., 2022) we set α = 0.2, the initial memory term η(i) = 0, i [5] and l = 1000, i.e., a full gradient is computing every 1000 iterations. We use the compression operator from Vono et al. (2022) with quantization level s = 28. ... We run 400000 steps of both algorithms and collect all the points generated by each algorithm to plot their histogram. |