Personalized Dictionary Learning for Heterogeneous Datasets
Authors: Geyu Liang, Naichen Shi, Raed AL Kontar, Salar Fattahi
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 lianggy@umich.edu Naichen Shi University of Michigan Ann Arbor, MI 48109 naichens@umich.edu Raed Al Kontar University of Michigan Ann Arbor, MI 48109 alkontar@umich.edu Salar Fattahi University of Michigan Ann Arbor, MI 48109 fattahi@umich.edu |
| 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. |