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..
Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization
Authors: Wenzhi Gao, Qi Deng
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our numerical experiments further confirm the empirical superiority of our proposed methods. |
| Researcher Affiliation | Academia | Wenzhi Gao Stanford University EMAIL Qi Deng Shanghai University of Finance and Economics EMAIL |
| Pseudocode | Yes | Algorithm 1: Delayed stochastic proximal subgradient method; Algorithm 2: Delayed stochastic prox-linear method; Algorithm 3: Safeguarded DSGD/DSPL |
| Open Source Code | No | The paper does not contain any explicit statement or link providing access to the source code for the methodology described. |
| Open Datasets | Yes | The real-life data is generated from zipcode dataset, where we vectorize a 16 × 16 hand-written digit from [16] |
| Dataset Splits | No | The paper specifies running for "400 epochs (K = 400m)" and a stopping criterion of "f < 1.5f(xˆ)", but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts) for reproducibility. |
| Hardware Specification | Yes | Our first experiment runs in an asynchronous environment implemented by MPI Python interface and is profiled on an Intel(R) Xeon(R) CPU E5-2640 v4 @ 2.40GHz machine with 10 cores and 20 threads. |
| Software Dependencies | No | The paper mentions using "MPI Python interface" but does not specify exact version numbers for either MPI or Python. It also states that "numerical linear algebra on the worker uses a raw implementation (not importing package)", indicating a lack of specific library dependencies with versions. |
| Experiment Setup | Yes | 2) Initial point and radius. Synthetic data: we generate x ∼ N(0, In) and start from x1 = x/x; zipcode data: we generate x ∼ N(ˆx, In) and take x1 = 10x. M = 1000x1. 3) Stopping criterion. We run algorithms for 400 epochs (K = 400m). ... 4) Stepsize. We set γ = K/α, where α ∈ {0.1, 0.5, 1.0} in the asynchronous environment, α ∈ [10−2, 101] for synthetic data and α ∈ [101, 102] for the zipcode dataset. 5) Simulated delay. In the simulated environment, we generate τk from two common distributions from literature, which are geometric G(p) and Poisson P(λ) [37]. After the delay is generated, it is truncated by twice the mean of the distribution. |