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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...
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04/07/2020
A Brief Prehistory of Double Descent
In their thought-provoking paper [1], Belkin et al. illustrate and discu...
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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 ...
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06/08/2017
Nuclear Discrepancy for Active Learning
Active learning algorithms propose which unlabeled objects should be que...
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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...
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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...
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12/23/2016
RSSL: Semi-supervised Learning in R
In this paper, we introduce a package for semi-supervised learning resea...
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10/17/2016
The Peaking Phenomenon in Semi-supervised Learning
For the supervised least squares classifier, when the number of training...
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10/12/2016
Optimistic Semi-supervised Least Squares Classification
The goal of semi-supervised learning is to improve supervised classifier...
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02/25/2016
Projected Estimators for Robust Semi-supervised Classification
For semi-supervised techniques to be applied safely in practice we at le...
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12/27/2015
Robust Semi-supervised Least Squares Classification by Implicit Constraints
We introduce the implicitly constrained least squares (ICLS) classifier,...
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12/15/2015
Feature-Level Domain Adaptation
Domain adaptation is the supervised learning setting in which the traini...
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11/17/2014