Surrogate Regret Bounds for Polyhedral Losses
Authors: Rafael Frongillo, Bo Waggoner
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide two general results. The first gives a linear surrogate regret bound for any polyhedral (piecewise-linear and convex) surrogate, meaning that surrogate generalization rates translate directly to target rates. The second shows that for sufficiently non-polyhedral surrogates, the regret bound is a square root, meaning fast surrogate generalization rates translate to slow rates for the target. ... The proof of our upper bound, Theorem 1, is nonconstructive. After proving the main results (Sections 3 and 4), we make it constructive in Section 5. |
| Researcher Affiliation | Academia | Rafael Frongillo University of Colorado Boulder raf@colorado.edu Bo Waggoner University of Colorado Boulder bwag@colorado.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | This paper is theoretical and does not present methodology that would typically involve source code for release, nor does it make any statement about providing code. |
| Open Datasets | No | The paper is a theoretical work focusing on mathematical proofs and analyses of loss functions; it does not involve the use of datasets for empirical evaluation. |
| Dataset Splits | No | The paper is a theoretical work and does not describe experiments that would require training/validation/test dataset splits. |
| Hardware Specification | No | The paper is a theoretical study and does not describe experiments that would involve specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is a theoretical work and does not describe experiments that would involve specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is a theoretical work and does not describe experiments. Therefore, no experimental setup details such as hyperparameters or training configurations are provided. |