Search Results for author:
Found 59 papers, 21 papers with code
On the reusability of samples in active learning
This paper explains why sample reusability is of practical interest, why reusability can be a problem, how reusability could be improved by importance-weighted active learning, and which obstacles to universal reusability remain.
Target contrastive pessimistic risk for robust domain adaptation
In domain adaptation, classifiers with information from a source domain adapt to generalize to a target domain.
A review of domain adaptation without target labels
Feature-based methods revolve around on mapping, projecting and representing features such that a source classifier performs well on the target domain and inference-based methods incorporate adaptation into the parameter estimation procedure, for instance through constraints on the optimization procedure.
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
We present ReproducedPapers. org: an open online repository for teaching and structuring machine learning reproducibility.
Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
While the success of semi-supervised learning (SSL) is still not fully understood, Sch\"olkopf et al. (2012) have established a link to the principle of independent causal mechanisms.
A Variance Maximization Criterion for Active Learning
We propose a novel approach which we refer to as maximizing variance for active learning or MVAL for short.
MR Acquisition-Invariant Representation Learning
Due to this acquisition related variation, classifiers trained on data from a specific scanner fail or under-perform when applied to data that was acquired differently.
Robust importance-weighted cross-validation under sample selection bias
Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk.
Learning an MR acquisition-invariant representation using Siamese neural networks
Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e. g. different acquisition protocols and field strengths.
Social Processes: Self-Supervised Meta-Learning over Conversational Groups for Forecasting Nonverbal Social Cues
In this way, an SP model learns to adapt its forecasts to the unique dynamics of the interacting partners, generalizing to unseen groups in a data-efficient manner.
Resolution learning in deep convolutional networks using scale-space theory
We use scale-space theory to obtain a self-similar parametrization of filters and make use of the N-Jet: a truncated Taylor series to approximate a filter by a learned combination of Gaussian derivative filters.
Single Shot Active Learning using Pseudo Annotators
These pseudo annotators always provide uniform and random labels whenever new unlabeled samples are queried.
On Regularization Parameter Estimation under Covariate Shift
This paper identifies a problem with the usual procedure for L2-regularization parameter estimation in a domain adaptation setting.
Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification
The latter refers to the fact that our estimates are conservative and therefore resilient to whatever form the true labeling of the unlabeled data takes on.
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
Current methods for covariate-shift adaptation use unlabelled data to compute importance weights or domain-invariant features, while the final model is trained on labelled data only.
Minimizers of the Empirical Risk and Risk Monotonicity
Plotting a learner's average performance against the number of training samples results in a learning curve.
Black Magic in Deep Learning: How Human Skill Impacts Network Training
Our study investigates the subjective human factor in comparisons of state of the art results and scientific reproducibility in deep learning.
The Shape of Learning Curves: a Review
This important tool can be used for model selection, to predict the effect of more training data, and to reduce the computational complexity of model training and hyperparameter tuning.
Effects of sampling skewness of the importance-weighted risk estimator on model selection
For sample selection bias settings, and for small sample sizes, the importance-weighted risk estimator produces overestimates for datasets in the body of the sampling distribution, i. e. the majority of cases, and large underestimates for data sets in the tail of the sampling distribution.
Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of automatically assigning objects to their respective classes on the basis of numerical measurements derived from these objects.
Object-Extent Pooling for Weakly Supervised Single-Shot Localization
To facilitate this, we propose a novel global pooling technique called Spatial Pyramid Averaged Max (SPAM) pooling for training this CAM-based network for object extent localisation with only weak image-level supervision.
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL
In various approaches to learning, notably in domain adaptation, active learning, learning under covariate shift, semi-supervised learning, learning with concept drift, and the like, one often wants to compare a baseline classifier to one or more advanced (or at least different) strategies.
Scale-Regularized Filter Learning
We start out by demonstrating that an elementary learning task, corresponding to the training of a single linear neuron in a convolutional neural network, can be solved for feature spaces of very high dimensionality.
Nuclear Discrepancy for Active Learning
In particular we show the relation between the bound of the state-of-the-art Maximum Mean Discrepancy (MMD) active learner, the bound of the Discrepancy, and a new and looser bound that we refer to as the Nuclear Discrepancy bound.
Label Stability in Multiple Instance Learning
We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers.
Classification of COPD with Multiple Instance Learning
Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate.
Template Matching via Densities on the Roto-Translation Group
We propose a template matching method for the detection of 2D image objects that are characterized by orientation patterns.
Active Learning Using Uncertainty Information
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 our selection on.
Robust Semi-supervised Least Squares Classification by Implicit Constraints
Experimental results show that also in the general multidimensional case performance improvements can be expected, both in terms of the squared loss that is intrinsic to the classifier, as well as in terms of the expected classification error.
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Consider a classification problem where we have both labeled and unlabeled data available.
Reproducible Pattern Recognition Research: The Case of Optimistic SSL
In this paper, we discuss the approaches we took and trade-offs involved in making a paper on a conceptual topic in pattern recognition research fully reproducible.
A Benchmark and Comparison of Active Learning for Logistic Regression
Logistic regression is by far the most widely used classifier in real-world applications.
The Peaking Phenomenon in Semi-supervised Learning
For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first increase the error rate before decreasing it.
Optimistic Semi-supervised Least Squares Classification
The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples.
Feature-Level Domain Adaptation
Our empirical evaluation of FLDA focuses on problems comprising binary and count data in which the transfer can be naturally modeled via a dropout distribution, which allows the classifier to adapt to differences in the marginal probability of features in the source and the target domain.
Projected Estimators for Robust Semi-supervised Classification
For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts.
Implicitly Constrained Semi-Supervised Least Squares Classification
We introduce a novel semi-supervised version of the least squares classifier.
Implicitly Constrained Semi-Supervised Linear Discriminant Analysis
Using any one of these methods is not guaranteed to outperform the supervised classifier which does not take the additional unlabeled data into account.
On Classification with Bags, Groups and Sets
To better deal with such problems, several extensions of supervised learning have been proposed, where either training and/or test objects are sets of feature vectors.
Multiple Instance Learning with Bag Dissimilarities
Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous.
Quantile Representation for Indirect Immunofluorescence Image Classification
Considering one whole slide as a collection (a bag) of feature vectors, however, poses the problem of how to handle this bag.
Dissimilarity-based Ensembles for Multiple Instance Learning
In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors.
Target Robust Discriminant Analysis
In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of the original training data.
Distance Based Source Domain Selection for Sentiment Classification
The performance of the proposed methodology is validated through an SC case study in which our numerical experiments suggest a significant improvement in the cross domain classification error in comparison with a random selected source domain for both a naive and adaptive learning setting.
An introduction to domain adaptation and transfer learning
Domain adaptation and transfer learning are sub-fields within machine learning that are concerned with accounting for these types of changes.
A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in particular done in Manifold regularization.
How to Manipulate CNNs to Make Them Lie: the GradCAM Case
This illustrates that GradCAM cannot explain the decision of every CNN and provides a proof of concept showing that it is possible to obfuscate the inner workings of a CNN.
Improvability Through Semi-Supervised Learning: A Survey of Theoretical Results
In this review we gather results about the possible gains one can achieve when using semi-supervised learning as well as results about the limits of such methods.
Consistency and Finite Sample Behavior of Binary Class Probability Estimation
Our main contribution is to present a way to derive finite sample L1-convergence rates of this estimator for different surrogate loss functions.
A Brief Prehistory of Double Descent
In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves in the context of modern high-complexity learners.
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization
In this work, we define DIRs employed by existing works in probabilistic terms and show that by learning DIRs, overly strict requirements are imposed concerning the invariance.
Nearest Neighbor-based Importance Weighting
Importance weighting is widely applicable in machine learning in general and in techniques dealing with data covariate shift problems in particular.
Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings
These developments provide additional understanding of what can be done to make Wikipedia more gender neutral and how important time of writing can be when considering biases in word embeddings trained from Wikipedia or from other text corpora.
Enhancing Classifier Conservativeness and Robustness by Polynomiality
We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression.
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts
In this work, we introduce and address the following question: given a probabilistic forecasting model how can we identify observed windows that the model considers salient when making its forecasts?
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations
This result makes it possible to extend the guarantees of existing MBRL algorithms to the setting with abstraction.
Model-based Reinforcement Learning
reinforcement-learning
+1
A view on model misspecification in uncertainty quantification
Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide.
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 size results in a so-called learning curve.
