An Embedding Framework for Consistent Polyhedral Surrogates
Authors: Jessica Finocchiaro, Rafael Frongillo, Bo Waggoner
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formalize and study the natural approach of designing convex surrogate loss functions via embeddings for problems such as classification or ranking. We prove that this approach is equivalent, in a strong sense, to working with polyhedral (piecewise linear convex) losses. Moreover, given any polyhedral loss L, we give a construction of a link function through which L is a consistent surrogate for the loss it embeds. We go on to illustrate the power of this embedding framework with succinct proofs of consistency or inconsistency of various polyhedral surrogates in the literature. |
| Researcher Affiliation | Academia | Jessie Finocchiaro jefi8453@colorado.edu CU Boulder Rafael Frongillo raf@colorado.edu CU Boulder Bo Waggoner bwag@colorado.edu CU Boulder |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using datasets, therefore no training dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments using datasets, therefore no validation split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments that would require specific hardware, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore no experimental setup details like hyperparameters or training configurations are provided. |