Exponentially Convergent Algorithms for Supervised Matrix Factorization
Authors: Joowon Lee, Hanbaek Lyu, Weixin Yao
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We numerically verify Theorem 3.5 on a semi-synthetic dataset generated by using MNIST image dataset [24] (p = 282 = 784, q = 0, n = 500, = 1) and a text dataset named Real / Fake Job Posting Prediction [1] (p = 2840, q = 72, n = 17880, = 1). |
| Researcher Affiliation | Academia | Joowon Lee Department of Statistics University of Wisconsin Madison, WI, USA jlee2256@wisc.edu Hanbaek Lyu Department of Mathematics University of Wisconsin Madison, WI, USA hlyu@math.wisc.edu Weixin Yao Department of Statistics University of California, Riverside, CA, USA weixiny@ucr.edu |
| Pseudocode | Yes | Algorithm 1 Lifted PGD for SMF |
| Open Source Code | Yes | We provide our implementation of Algorithm 1 in our code repository https://github.com/ljw9510/SMF/tree/main. |
| Open Datasets | Yes | We numerically verify Theorem 3.5 on a semi-synthetic dataset generated by using MNIST image dataset [24] (p = 282 = 784, q = 0, n = 500, = 1) and a text dataset named Real / Fake Job Posting Prediction [1] (p = 2840, q = 72, n = 17880, = 1). We apply the proposed methods to two datasets from the Curated Microarray Database (Cu Mi Da) [14]. |
| Dataset Splits | Yes | Other parameters are chosen through 5-fold cross-validation ( 2 {0.1, 1, 10} and λ 2 {0.1, 1, 10}), and the algorithms are repeated in 1,000 iterations or until convergence. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For all experiments, λ = 2 and stepsize = 0.01 were used. Other parameters are chosen through 5-fold cross-validation ( 2 {0.1, 1, 10} and λ 2 {0.1, 1, 10}), and the algorithms are repeated in 1,000 iterations or until convergence. |