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].
Personalized Dictionary Learning for Heterogeneous Datasets
Authors: Geyu Liang, Naichen Shi, Raed AL Kontar, Salar Fattahi
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we showcase the effectiveness of Algorithm 1 using synthetic and real data. All experiments are performed on a Mac Book Pro 2021 with the Apple M1 Pro chip and 16GB unified memory for a serial implementation in MATLAB 2022a. |
| Researcher Affiliation | Academia | Geyu Liang University of Michigan Ann Arbor, MI 48109 EMAIL Naichen Shi University of Michigan Ann Arbor, MI 48109 EMAIL Raed Al Kontar University of Michigan Ann Arbor, MI 48109 EMAIL Salar Fattahi University of Michigan Ann Arbor, MI 48109 EMAIL |
| Pseudocode | Yes | Algorithm 1 Per MA: Federated Matching and Averaging |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the described methodology or state that the code is available in supplementary materials. |
| Open Datasets | Yes | In section 5.2, we aim to learn a dictionary with imbalanced data collected from MNIST dataset (Le Cun et al., 2010). |
| Dataset Splits | No | The paper describes the construction of synthetic and MNIST datasets but does not explicitly provide training/test/validation dataset splits or percentages for reproducibility. |
| Hardware Specification | Yes | All experiments are performed on a Mac Book Pro 2021 with the Apple M1 Pro chip and 16GB unified memory for a serial implementation in MATLAB 2022a. |
| Software Dependencies | Yes | All experiments are performed on a Mac Book Pro 2021 with the Apple M1 Pro chip and 16GB unified memory for a serial implementation in MATLAB 2022a. |
| Experiment Setup | Yes | In this section, we validate our theoretical results on a synthetic dataset. We consider ten clients, each with a dataset generated according to the model 1. The details of our construction are presented in the appendix. Specifically, we use ΞΆ = 0.15 for the experiments in Section 5.1. |