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..
Spectral k-Support Norm Regularization
Authors: Andrew M McDonald, Massimiliano Pontil, Dimitris Stamos
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the norms to real and synthetic matrix completion datasets. Our findings indicate that spectral k-support norm regularization gives state of the art performance, consistently improving over trace norm regularization and the matrix elastic net. |
| Researcher Affiliation | Academia | Andrew M. Mc Donald, Massimiliano Pontil, Dimitris Stamos Department of Computer Science University College London EMAIL |
| Pseudocode | Yes | Algorithm 1 Computation of x = prox λ |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the methodology described. |
| Open Datasets | Yes | The datasets we considered were Movie Lens 100k and Movie Lens 1M (http://grouplens.org/datasets/movielens/), which consist of user ratings of movies, and Jester 1 and Jester 3 (http://goldberg.berkeley.edu/jesterdata/), which consist of users and ratings of jokes (Jester 2 showed essentially identical performance to Jester 1). |
| Dataset Splits | Yes | we sampled uniformly a percentage ∈ {10%, 20%, 30%} of the entries for training, and used a fixed 10% for validation. |
| Hardware Specification | No | The paper does not provide any specific hardware details for the experimental setup. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For the optimization we used an accelerated proximal gradient method (FISTA), see e.g. [12, 21, 22], with the percentage change in objective as convergence criterion, with a tolerance of 10^-5 for the simulated datasets and 10^-3 for the real datasets. |