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 [1].
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. |