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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
A Consistent Regularization Approach for Structured Prediction
Authors: Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,927 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,376 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 | Experimental results are provided to demonstrate the practical usefulness of the proposed approach. |
| Researcher Affiliation | Academia | 1 Laboratory for Computational and Statistical Learning Istituto Italiano di Tecnologia, Genova, Italy & Massachusetts Institute of Technology, Cambridge, MA 02139, USA. 2 Università degli Studi di Genova, Genova, Italy. |
| Pseudocode | No | The paper refers to 'Alg. 1' and provides the mathematical formulation for it, but it does not present it in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code for the methodology described, nor does it include links to a code repository. |
| Open Datasets | Yes | We considered the problem of ranking movies in the Movie Lens dataset [29] (ratings (from 1 to 5) of 1682 movies by 943 users). We considered the USPS digits reconstruction experiment originally proposed in [18]. |
| Dataset Splits | Yes | We randomly sampled n = 643 users for training and tested on the remaining 300. We performed 5-fold cross-validation for model selection. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory specifications, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Matlab FMINUNC function', but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | No | The paper describes general experimental approaches such as kernel choices and cross-validation, but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training settings. |