Trading off Consistency and Dimensionality of Convex Surrogates for Multiclass Classification
Authors: Enrique Nueve, Dhamma Kimpara, Bo Waggoner, Jessica Finocchiaro
NeurIPS 2024 | Conference 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 enrique.nueveiv@colorado.edu Bo Waggoner Department of Computer Science University of Colorado Boulder bwag@colorado.edu Dhamma Kimpara Department of Computer Science University of Colorado Boulder dhamma.kimpara@colorado.edu Jessie Finocchiaro Department of Computer Science Boston College finocch@bc.edu |
| 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. |