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..
Structured Prediction with Projection Oracles
Authors: Mathieu Blondel
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our losses on label ranking, ordinal regression and multilabel classification, confirming the improved accuracy enabled by projections. |
| Researcher Affiliation | Industry | Mathieu Blondel NTT Communication Science Laboratories Kyoto, Japan |
| Pseudocode | No | The paper describes methods textually but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | A Python implementation is available at https://github.com/mblondel/projection-losses. |
| Open Datasets | Yes | We use the same six public datasets as in [26]. |
| Dataset Splits | Yes | choosing λ against the validation set. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were found in the paper. |
| Software Dependencies | No | A Python implementation is available at https://github.com/mblondel/projection-losses. |
| Experiment Setup | No | In all cases, we use a linear model θ = g(x) := Wx and solve 1 n Pn i=1 SΨ C (Wxi, yi) + λ 2 W 2 F by L-BFGS, choosing λ against the validation set. |