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].
A Stochastic Newton Algorithm for Distributed Convex Optimization
Authors: Brian Bullins, Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5, we compare a more practical version of our method, FEDSN-LITE (Algorithm 6) against the other methods, showing we can significantly reduce communication compared to other first-order methods. ... In our experiments in Figure 6, we notice that FEDSN-LITE is either competitive with or outperforms the other baselines. This is especially true for the sparse communication settings, which are of most practical interest. |
| Researcher Affiliation | Academia | Brian Bullins Toyota Technological Institute at Chicago EMAIL Kumar Kshitij Patel Toyota Technological Institute at Chicago EMAIL Ohad Shamir Weizmann Institute of Science EMAIL Nathan Srebro Toyota Technological Institute at Chicago EMAIL Blake Woodworth Toyota Technological Institute at Chicago EMAIL |
| Pseudocode | Yes | Algorithm 1 FEDERATED-STOCHASTIC-NEWTON, a.k.a., FEDSN(x0) |
| Open Source Code | Yes | Code is availabe at https://github.com/kishinmh/Inexact-Newton. |
| Open Datasets | Yes | Empirical comparison of FEDSN-LITE (Algorithm 6) to other methods (see Appendix G.1) on the LIBSVM a9a (Chang and Lin, 2011; Dua and Graff, 2017) dataset |
| Dataset Splits | Yes | For all algorithms, we use the default 80/20 train/test split of the LIBSVM a9a dataset as provided by LIBSVMtools. We use a 10% held-out set from the training set as the validation set for tuning hyperparameters. |
| Hardware Specification | No | The paper mentions running experiments on 'M parallel machines' but does not provide specific hardware details such as CPU or GPU models, or memory specifications used for the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or TensorFlow, or specific LIBSVM version used). |
| Experiment Setup | Yes | For all algorithms, we tune the learning rate from the set {0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5, 10} and batch size from the set {1, 2, 4, 8, 16, 32, 64, 128, 256}. For algorithms with momentum (Minibatch SGD, Local SGD, FEDAC, and FEDSN-LITE), we tune the momentum parameter from the set {0.1, 0.3, 0.5, 0.7, 0.9}. |