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..
Variance Reduction in Stochastic Particle-Optimization Sampling
Authors: Jianyi Zhang, Yang Zhao, Changyou Chen
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are veri๏ฌed by a number of experiments on both synthetic and real datasets. |
| Researcher Affiliation | Academia | 1Duke University 2University at Buffalo, SUNY. |
| Pseudocode | Yes | Algorithm 1 SAGA-POS; Algorithm 2 SVRG-POS; Algorithm 3 SVRG-POS+ |
| Open Source Code | No | The paper does not provide a statement about releasing open-source code or a link to a code repository. |
| Open Datasets | Yes | We test the proposed algorithms for Bayesian-logistic-regression (BLR) on four publicly available datasets from the UCI machine learning repository: Australian (690-14), Pima (768-8), Diabetic (1151-20) and Susy (100000-18), where (N d) means a dataset of N data points with dimensionality d. |
| Dataset Splits | No | The datasets are split into 80% training data and 20% testing data. There is no explicit mention of a separate validation set split. |
| Hardware Specification | No | No specific hardware details such as GPU models, CPU types, or memory specifications were mentioned for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers (e.g., Python, PyTorch, TensorFlow versions, or library versions). |
| Experiment Setup | Yes | Optimized constant stepsizes are applied for each algorithm via grid search. ... The minibatch size is set to 15 for all experiments. ... averaging over 10 runs with 50 particles. |