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].
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. |