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..
New Bounds For Distributed Mean Estimation and Variance Reduction
Authors: Peter Davies, Vijaykrishna Gurunanthan, Niusha Moshrefi, Saleh Ashkboos, Dan Alistarh
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally that our method yields practical improvements for common applications, relative to prior approaches. |
| Researcher Affiliation | Collaboration | Peter Davies IST Austria EMAIL Gurunathan IIT Bombay EMAIL Moshre๏ฌ IST Austria EMAIL Ashkboos IST Austria EMAIL Alistarh IST Austria & Neural Magic EMAIL |
| Pseudocode | No | The paper describes algorithms in prose, such as "The simplest version of our lattice quantization algorithm can be described as follows", but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper does not provide concrete access information (link, DOI, repository, or formal citation with authors/year) for a publicly available or open dataset. It mentions generating synthetic data and references other datasets in the full version without providing access details. |
| Dataset Splits | No | The paper does not specify exact dataset split percentages or absolute sample counts for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory amounts, or detailed computer specifications used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., library names like PyTorch 1.9, or specific solver versions). |
| Experiment Setup | Yes | Figure 1: Gradient quantization results for the regression example. S = 8192, n = 2 d = 100, batch_size = 4096... Figure 1 (right) Regression convergence: S = 8192, n = 2 d = 100, lr = 0.8, batch = 4096, qlevel = 8... Figure 2: Local SGD Convergence: S = 8192, n = 2 d = 100, lr = 0.1, batch = 4096, q = 8, rep = 10 |