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 [1].
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. |