Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Structured Prediction with Projection Oracles
Authors: Mathieu Blondel
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 19,198 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,519 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 | 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. |