Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Convex Matrix Factorization with Representative Regions
Authors: Jianhao Peng, Olgica Milenkovic, Abhishek Agarwal
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,606 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,871 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real world datasets show significant computational savings of the proposed online convex MF method compared to classical convex MF. |
| Researcher Affiliation | Academia | Abhishek Agarwal Electrical and Computer Engineering University of Illinois Urbana-Champaign EMAIL Jianhao Peng Electrical and Computer Engineering University of Illinois Urbana-Champaign EMAIL Olgica Milenkovic Electrical and Computer Engineering University of Illinois Urbana-Champaign EMAIL |
| Pseudocode | Yes | Algorithm 1 Initialization; Algorithm 2 Online cvx MF |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of its source code. |
| Open Datasets | Yes | The real world datasets include are taken from the UCI Machine Learning [30] and the 10X Genomics repository [31]. |
| Dataset Splits | Yes | After performing cross validation on an evaluation set of size 1000, we selected c= 0.2. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. It only discusses the datasets and algorithms. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | After performing cross validation on an evaluation set of size 1000, we selected c= 0.2. ... All algorithms ran 3,000 iterations with N=150 and λ=0.1 to generate eigenimages, capturing the characteristic features used as bases [35]. |