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..
Stochastic Gradient Descent under Markovian Sampling Schemes
Authors: Mathieu Even
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical illustration of our theory We present in Appendix G two experiments on synthetic problems, comparing MC-SAG and MC-SGD to gossip-based and token baselines. |
| Researcher Affiliation | Academia | 1Inria ENS Paris. |
| Pseudocode | Yes | Algorithm 1 Markov Chain SAG (MC-SAG) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that its source code is publicly available. |
| Open Datasets | No | The paper uses synthetic data generated internally ("For v V, we take fv(x) = ℓ(x, av, bv) for av and bv random variables.") and does not refer to a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes generating synthetic data but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We compare our algorithms MC-SGD and MC-SAGA with Walkman (Mao et al., 2020) and decentralized SGD (D-SGD in Figure 1) (Koloskova et al., 2020; Yu et al., 2019) with both randomized gossip communications and fixed gossip matrix. We consider the non-convex loss function ℓ(x, a, b) = (σ(x a) b)2/2 where σ(t) = 1/(1 + exp( t)) as in Mei et al. (2018). For v V, we take fv(x) = ℓ(x, av, bv) for av and bv random variables. |