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A Brief Prehistory of Double Descent
In their thought-provoking paper [1], Belkin et al. illustrate and discu...
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Making Learners (More) Monotone
Learning performance can show non-monotonic behavior. That is, more data...
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Consistency and Finite Sample Behavior of Binary Class Probability Estimation
In this work we investigate to which extent one can recover class probab...
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Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results
Semi-supervised learning is a setting in which one has labeled and unlab...
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How to Manipulate CNNs to Make Them Lie: the GradCAM Case
Recently many methods have been introduced to explain CNN decisions. How...
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Minimizers of the Empirical Risk and Risk Monotonicity
Plotting a learner's average performance against the number of training ...
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A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
Manifold regularization is a commonly used technique in semi-supervised ...
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Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
While the success of semi-supervised learning (SSL) is still not fully u...
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A review of single-source unsupervised domain adaptation
Domain adaptation has become a prominent problem setting in machine lear...
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Learning an MR acquisition-invariant representation using Siamese neural networks
Generalization of voxelwise classifiers is hampered by differences betwe...
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Distance Based Source Domain Selection for Sentiment Classification
Automated sentiment classification (SC) on short text fragments has rece...
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Semi-Generative Modelling: Domain Adaptation with Cause and Effect Features
This paper presents a novel, causally-inspired approach to domain adapta...
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Target Contrastive Pessimistic Discriminant Analysis
Domain-adaptive classifiers learn from a source domain and aim to genera...
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Single Shot Active Learning using Pseudo Annotators
Standard myopic active learning assumes that human annotations are alway...
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Effects of sampling skewness of the importance-weighted risk estimator on model selection
Importance-weighting is a popular and well-researched technique for deal...
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Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of ...
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On reducing sampling variance in covariate shift using control variates
Covariate shift classification problems can in principle be tackled by i...
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MR Acquisition-Invariant Representation Learning
Voxelwise classification is a popular and effective method for tissue qu...
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Object-Extent Pooling for Weakly Supervised Single-Shot Localization
In the face of scarcity in detailed training annotations, the ability to...
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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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Scale-Regularized Filter Learning
We start out by demonstrating that an elementary learning task, correspo...
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Target contrastive pessimistic risk for robust domain adaptation
In domain adaptation, classifiers with information from a source domain ...
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A Variance Maximization Criterion for Active Learning
Active learning aims to train a classifier as fast as possible with as f...
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Nuclear Discrepancy for Active Learning
Active learning algorithms propose which unlabeled objects should be que...
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Label Stability in Multiple Instance Learning
We address the problem of instance label stability in multiple instance ...
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Classification of COPD with Multiple Instance Learning
Chronic obstructive pulmonary disease (COPD) is a lung disease where ear...
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Active Learning Using Uncertainty Information
Many active learning methods belong to the retraining-based approaches, ...
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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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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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The Peaking Phenomenon in Semi-supervised Learning
For the supervised least squares classifier, when the number of training...
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Optimistic Semi-supervised Least Squares Classification
The goal of semi-supervised learning is to improve supervised classifier...
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On Regularization Parameter Estimation under Covariate Shift
This paper identifies a problem with the usual procedure for L2-regulari...
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Template Matching via Densities on the Roto-Translation Group
We propose a template matching method for the detection of 2D image obje...
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Projected Estimators for Robust Semi-supervised Classification
For semi-supervised techniques to be applied safely in practice we at le...
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Robust Semi-supervised Least Squares Classification by Implicit Constraints
We introduce the implicitly constrained least squares (ICLS) classifier,...
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Feature-Level Domain Adaptation
Domain adaptation is the supervised learning setting in which the traini...
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Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification
Improvement guarantees for semi-supervised classifiers can currently onl...
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Implicitly Constrained Semi-Supervised Linear Discriminant Analysis
Semi-supervised learning is an important and active topic of research in...
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On Classification with Bags, Groups and Sets
Many classification problems can be difficult to formulate directly in t...
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Quantile Representation for Indirect Immunofluorescence Image Classification
In the diagnosis of autoimmune diseases, an important task is to classif...
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Dissimilarity-based Ensembles for Multiple Instance Learning
In multiple instance learning, objects are sets (bags) of feature vector...
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Multiple Instance Learning with Bag Dissimilarities
Multiple instance learning (MIL) is concerned with learning from sets (b...
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