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..
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
Authors: Yutong He, Xinmeng Huang, Kun Yuan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our theoretical findings with experiments on both synthetic data and real datasets. |
| Researcher Affiliation | Academia | Yutong He Peking University EMAIL Xinmeng Huang University of Pennsylvania EMAIL Kun Yuan Peking University EMAIL |
| Pseudocode | Yes | Algorithm 1: ADIANA |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code for the methodology described. |
| Open Datasets | Yes | Logistic regression. Consider a distributed logistic regression problem (1) with fi(x) := 1 M PM m=1 ln(1 + exp( bi,ma i,mx), where {(ai,m, bi,m)}1 i n,1 m M are datapoints in a9a and w8a datasets from LIBSVM [11]. |
| Dataset Splits | No | The paper mentions distributing data to nodes for distributed optimization but does not explicitly describe train/validation/test splits for the datasets used in the experiments. |
| Hardware Specification | Yes | Computational resource. All experiments are run on an NVIDIA A100 server. |
| Software Dependencies | No | The paper mentions implementing algorithms but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | F.3 Parameter values. In this subsection, we list all the parameter values that are selected by applying Bayesian Optimization. Table 2, 3, 4, 5, 6 list the parameters chosen in the least squares problem, logistic regression using a9a dataset, logistic regression using w8a dataset, the constructed problem, and logistic regression using CIFAR-10 dataset, respectively. |