Jesse H. Krijthe | DeepAI

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Featured Co-authors

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∙ 05/27/2022

Combining observational datasets from multiple environments to detect hidden confounding

A common assumption in causal inference from observational data is the a...

0 Rickard K. A. Karlsson, et al. ∙

∙ 04/07/2020

A Brief Prehistory of Double Descent

In their thought-provoking paper [1], Belkin et al. illustrate and discu...

0 Marco Loog, et al. ∙

∙ 07/13/2017

On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL

In various approaches to learning, notably in domain adaptation, active ...

0 Marco Loog, et al. ∙

∙ 06/08/2017

Nuclear Discrepancy for Active Learning

Active learning algorithms propose which unlabeled objects should be que...

0 Tom J. Viering, et al. ∙

∙ 12/28/2016

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

We show that for linear classifiers defined by convex margin-based surro...

0 Jesse H. Krijthe, et al. ∙

∙ 12/27/2016

Reproducible Pattern Recognition Research: The Case of Optimistic SSL

In this paper, we discuss the approaches we took and trade-offs involved...

0 Jesse H. Krijthe, et al. ∙

∙ 12/23/2016

RSSL: Semi-supervised Learning in R

In this paper, we introduce a package for semi-supervised learning resea...

0 Jesse H. Krijthe, et al. ∙

∙ 10/17/2016

The Peaking Phenomenon in Semi-supervised Learning

For the supervised least squares classifier, when the number of training...

0 Jesse H. Krijthe, et al. ∙

∙ 10/12/2016

Optimistic Semi-supervised Least Squares Classification

The goal of semi-supervised learning is to improve supervised classifier...

0 Jesse H. Krijthe, et al. ∙

∙ 02/25/2016

Projected Estimators for Robust Semi-supervised Classification

For semi-supervised techniques to be applied safely in practice we at le...

0 Jesse H. Krijthe, et al. ∙

∙ 12/27/2015

Robust Semi-supervised Least Squares Classification by Implicit Constraints

We introduce the implicitly constrained least squares (ICLS) classifier,...

0 Jesse H. Krijthe, et al. ∙

∙ 12/15/2015

Feature-Level Domain Adaptation

Domain adaptation is the supervised learning setting in which the traini...

0 Wouter M. Kouw, et al. ∙

∙ 11/17/2014

Implicitly Constrained Semi-Supervised Linear Discriminant Analysis

Semi-supervised learning is an important and active topic of research in...

0 Jesse H. Krijthe, et al. ∙

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