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..
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
Authors: Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J. Smola
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present our empirical results in this section. For our experiments, we study the problem of binary classification via l2-regularized logistic regression. |
| Researcher Affiliation | Academia | Sashank J. Reddi Carnegie Mellon University EMAIL Ahmed Hefny Carnegie Mellon University EMAIL Suvrit Sra Massachusetts Institute of Technology EMAIL Barnab as P oczos Carnegie Mellon University EMAIL Alex Smola Carnegie Mellon University EMAIL |
| Pseudocode | Yes | ALGORITHM 1: GENERIC STOCHASTIC VARIANCE REDUCTION ALGORITHM Data: x0 2 Rd, 0 i = x0 8i 2 [n] , {1, . . . , n}, step size > 0 Randomly pick a IT = {i0, . . . , i T } where it 2 {1, . . . , n} 8 t 2 {0, . . . , T} ; for t = 0 to T do Update iterate as xt+1 xt rfit(xt) rfit( t ; At+1 = SCHEDULEUPDATE({xi}t+1 i=0, At, t, IT ) ; end return x T |
| Open Source Code | No | The paper states 'All the algorithms were implemented in C++ 2' but does not provide any link or explicit statement about making the source code available for the methodology described. |
| Open Datasets | Yes | We run our experiments on datasets from LIBSVM website3. ... 3http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/binary.html |
| Dataset Splits | No | The paper mentions using specific datasets but does not provide details on training, validation, or test splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | Yes | All experiments were conducted on a Google Compute Engine n1-highcpu-32 machine with 32 processors and 28.8 GB RAM. |
| Software Dependencies | No | The paper states that 'All the algorithms were implemented in C++ 2' but does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | Yes | In all our experiments, we set λ = 1/n. ... The epoch size m is chosen as 2n (as recommended in [10]) in all our experiments. ... A constant step size that gives the best convergence is chosen for the dataset. |