Trading off Consistency and Dimensionality of Convex Surrogates for Multiclass Classification
Authors: Enrique Nueve, Dhamma Kimpara, Bo Waggoner, Jessica Finocchiaro
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate two approaches for trading off consistency and dimensionality in multiclass classification while using a convex surrogate loss. We first formalize partial consistency when the optimized surrogate has dimension d n. We then check if partial consistency holds under a given embedding and low-noise assumption, providing insight into when to use a particular embedding into Rd. Finally, we present a new method to construct (fully) consistent losses with d n out of multiple problem instances. This paper does not include experiments requiring code. |
| Researcher Affiliation | Academia | Enrique Nueve Department of Computer Science University of Colorado Boulder EMAIL Bo Waggoner Department of Computer Science University of Colorado Boulder EMAIL Dhamma Kimpara Department of Computer Science University of Colorado Boulder EMAIL Jessie Finocchiaro Department of Computer Science Boston College EMAIL |
| Pseudocode | Yes | Algorithm 1 Elicit mode via comparisons and the d-Cross Polytopes |
| Open Source Code | No | This paper does not include experiments requiring code. |
| Open Datasets | No | The paper is primarily theoretical and does not involve empirical experiments that would require training on datasets. |
| Dataset Splits | No | The paper is primarily theoretical and does not involve empirical experiments, therefore no dataset splits for validation are described. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |