Optimal Binary Classifier Aggregation for General Losses
Authors: Akshay Balsubramani, Yoav S. Freund
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-convex losses, extending a recent analysis of the problem for misclassification error. The result is a family of semi-supervised ensemble aggregation algorithms which are as efficient as linear learning by convex optimization, but are minimax optimal without any relaxations. Their decision rules take a form familiar in decision theory applying sigmoid functions to a notion of ensemble margin without the assumptions typically made in margin-based learning. |
| Researcher Affiliation | Academia | Akshay Balsubramani University of California, San Diego abalsubr@ucsd.edu Yoav Freund University of California, San Diego yfreund@ucsd.edu |
| Pseudocode | No | The paper describes an 'Ensemble Aggregation Algorithm' in Section 2.2, but it is presented as descriptive text rather than a formal pseudocode block or algorithm box. |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability, nor does it include links to code repositories. |
| Open Datasets | No | The paper is theoretical and does not present experiments using specific datasets. It discusses 'training set' in a general problem context, but not as a dataset used for its own evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments; therefore, it does not discuss validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |