Marco Loog

The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning.

2016 · arXiv (Cornell University)

Consider a classification problem where we have both labeled and unlabeled data available. We show that for linear classifiers defined by convex margin-based surrogate losses that are decreasing, it is impossible to construct any semi-supervised …

Active learning using uncertainty information

2016

Many active learning methods belong to the retraining-based approaches, which select one unlabeled instance, add it to the training set with its possible labels, retrain the classification model, and evaluate the criteria that we base …

An empirical investigation into the inconsistency of sequential active learning

2016

In active learning, one aims to acquire labeled samples that are particularly useful for training a classifier. In sequential active learning, this sample selection is done in a one-at-a-time manner where the choice of sample …

Enhancing Classifier Conservativeness and Robustness by Polynomiality

2022 · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression. We then show how polynomiality can remedy the situation. This, among others, leads …