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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression
Authors: Sheng Chen, Arindam Banerjee
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results support our theoretical developments. |
| Researcher Affiliation | Collaboration | Sheng Chen The Voleon Group EMAIL Arindam Banerjee Dept. of Computer Science & Engineering University of Minnesota, Twin Cities EMAIL |
| Pseudocode | Yes | Algorithm 1 Alternating minimization for multi-response regression |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | No | The paper uses synthetically generated data: 'Entries of X of η are generated by i.i.d. standard Gaussian, and θ = [1, . . . , 1 | {z } 10 , 1, . . . , 1 | {z } 10 , 0, . . . , 0 | {z } 980 ]T . Σ is given as a block diagonal matrix with Σ = h 1 a a 1 i replicated along the diagonal.' No access information is provided. |
| Dataset Splits | No | The paper mentions 'sample size n' but does not specify training, validation, and test dataset splits with percentages or counts for their experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | Specifically we focus on the sparsity structure of θ , and consider L0-cardinality as complexity function f. Throughout the experiment, we fix problem dimension p = 1000, sparsity level of θ s = 20, and number of iterations T = 10. Entries of X of η are generated by i.i.d. standard Gaussian, and θ = [1, . . . , 1 | {z } 10 , 1, . . . , 1 | {z } 10 , 0, . . . , 0 | {z } 980 ]T . Σ is given as a block diagonal matrix with Σ = h 1 a a 1 i replicated along the diagonal. All the plots are obtained based on the average over 100 random trials. |