Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion
Authors: Jialun Zhang, Hong-Ming Chiu, Richard Y Zhang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we observe a similar acceleration for item-item collaborative filtering on the Movie Lens25M dataset via a pair-wise ranking loss, with 100 million training pairs and 10 million testing pairs. |
| Researcher Affiliation | Academia | Gavin Zhang University of Illinois at Urbana Champaign jialun2@illinois.edu Hong-Ming Chiu University of Illinois at Urbana Champaign hmchiu2@illinois.edu Richard Y. Zhang University of Illinois at Urbana Champaign ryz@illinois.edu |
| Pseudocode | No | The paper describes the update equations for SGD and Scaled SGD (equations 1 and 2) but does not provide formal pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | See supporting code at https://github.com/Hong-Ming/Scaled SGD. The code for all experiments are available at https://github.com/Hong-Ming/Scaled SGD. |
| Open Datasets | Yes | In our experiments, we observe a similar acceleration for item-item collaborative filtering on the Movie Lens25M dataset via a pair-wise ranking loss, with 100 million training pairs and 10 million testing pairs. |
| Dataset Splits | No | The paper specifies training and testing sets ('100 million training pairs' and '10 million testing pairs' for Movie Lens25M dataset) but does not explicitly mention a separate validation set or its size/split. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU/CPU models, memory) used for running the experiments. It only discusses the computational complexity of the algorithms. |
| Software Dependencies | No | The paper does not specify any software names with version numbers for reproducibility (e.g., Python, PyTorch, TensorFlow, specific libraries, or solvers). |
| Experiment Setup | Yes | All of our experiments use random Gaussian initializations and an initial P = σ2I. Matrix Completion with RMSE loss... Step-size = 0.3. The CF model is trained using Bayesian Personalized Ranking (BRP) loss [1] on a training set, which consists of 100 million pairwise samples in M. |