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11/25/2022
A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance
Plotting a learner's generalization performance against the training set...
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10/30/2022
A view on model misspecification in uncertainty quantification
Estimating uncertainty of machine learning models is essential to assess...
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08/30/2022
An Analysis of Abstracted Model-Based Reinforcement Learning
Many methods for Model-based Reinforcement learning (MBRL) provide guara...
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06/13/2022
On the reusability of samples in active learning
An interesting but not extensively studied question in active learning i...
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06/01/2022
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts
Forecasting tasks surrounding the dynamics of low-level human behavior a...
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03/23/2022
Enhancing Classifier Conservativeness and Robustness by Polynomiality
We illustrate the detrimental effect, such as overconfident decisions, t...
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07/28/2021
Social Processes: Self-Supervised Forecasting of Nonverbal Cues in Social Conversations
The default paradigm for the forecasting of human behavior in social con...
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06/07/2021
Resolution learning in deep convolutional networks using scale-space theory
Resolution in deep convolutional neural networks (CNNs) is typically bou...
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03/19/2021
The Shape of Learning Curves: a Review
Learning curves provide insight into the dependence of a learner's gener...
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02/03/2021
Nearest Neighbor-based Importance Weighting
Importance weighting is widely applicable in machine learning in general...
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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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11/25/2019
Making Learners (More) Monotone
Learning performance can show non-monotonic behavior. That is, more data...
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08/30/2019
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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08/26/2019
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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07/25/2019
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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07/11/2019
Minimizers of the Empirical Risk and Risk Monotonicity
Plotting a learner's average performance against the number of training ...
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06/14/2019
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
Manifold regularization is a commonly used technique in semi-supervised ...
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05/28/2019
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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01/16/2019
A review of single-source unsupervised domain adaptation
Domain adaptation has become a prominent problem setting in machine lear...
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10/17/2018
Learning an MR acquisition-invariant representation using Siamese neural networks
Generalization of voxelwise classifiers is hampered by differences betwe...
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08/28/2018
Distance Based Source Domain Selection for Sentiment Classification
Automated sentiment classification (SC) on short text fragments has rece...
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07/20/2018
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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06/21/2018
Target Contrastive Pessimistic Discriminant Analysis
Domain-adaptive classifiers learn from a source domain and aim to genera...
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05/17/2018
Single Shot Active Learning using Pseudo Annotators
Standard myopic active learning assumes that human annotations are alway...
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04/19/2018
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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10/25/2017
Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of ...
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10/17/2017
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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09/22/2017
MR Acquisition-Invariant Representation Learning
Voxelwise classification is a popular and effective method for tissue qu...
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07/19/2017
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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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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07/10/2017
Scale-Regularized Filter Learning
We start out by demonstrating that an elementary learning task, correspo...
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06/25/2017
Target contrastive pessimistic risk for robust domain adaptation
In domain adaptation, classifiers with information from a source domain ...
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06/23/2017
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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06/08/2017
Nuclear Discrepancy for Active Learning
Active learning algorithms propose which unlabeled objects should be que...
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03/15/2017
Label Stability in Multiple Instance Learning
We address the problem of instance label stability in multiple instance ...
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03/15/2017
Classification of COPD with Multiple Instance Learning
Chronic obstructive pulmonary disease (COPD) is a lung disease where ear...
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02/27/2017
Active Learning Using Uncertainty Information
Many active learning methods belong to the retraining-based approaches, ...
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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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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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07/31/2016
On Regularization Parameter Estimation under Covariate Shift
This paper identifies a problem with the usual procedure for L2-regulari...
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03/10/2016
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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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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03/01/2015
Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification
Improvement guarantees for semi-supervised classifiers can currently onl...
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11/17/2014
Implicitly Constrained Semi-Supervised Linear Discriminant Analysis
Semi-supervised learning is an important and active topic of research in...
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06/02/2014
On Classification with Bags, Groups and Sets
Many classification problems can be difficult to formulate directly in t...
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02/06/2014