arXiv research

Method finds optimal binary classification rules under weighted misclassification loss.

This work aims to reduce inexplicable errors in deep neural networks by obtaining class-level semantics and penalizing misclassifications.

This paper optimizes binary linear classifiers by tuning their weight vectors.

We study losses for binary classification and class probability estimation and extend the understanding of them from margin losses to general composite losses which are the composition of a proper loss with a link function. We characterise when margin losses can be proper composite losses, explicitly show how to determ…

We carefully study how well minimizing convex surrogate loss functions, corresponds to minimizing the misclassification error rate for the problem of binary classification with linear predictors. In particular, we show that amongst all convex surrogate losses, the hinge loss gives essentially the best possible bound, o…

Study uses spectral risk for learning with heavy-tailed data.

This research improves PAC-Bayesian bounds for classification tasks using convexified loss.

Proposes a new loss function for better handling mislabeling costs.

We study the misclassification error for community detection in general heterogeneous stochastic block models (SBM) with noisy or partial label information. We establish a connection between the misclassification rate and the notion of minimum energy on the local neighborhood of the SBM. We develop an optimally weighte…

New research shows logistic regression can achieve optimal error rate for agnostic learning of halfspaces.

New algorithm reduces misclassification costs in neural networks.

Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner. Most algorithms for obtaining tree ensembles are based on versions of boosting or Random Forest. Previous work showed that boosting algorithms exhibit a cyclic b…

The paper improves uncertainty quantification for node classification using distance-based regularization.

Convolutional neural networks converge quickly with gradient descent.

Dropout is a simple but effective technique for learning in neural networks and other settings. A sound theoretical understanding of dropout is needed to determine when dropout should be applied and how to use it most effectively. In this paper we continue the exploration of dropout as a regularizer pioneered by Wager,…

A novel method for classification with rejection using ensemble of cost-sensitive classifiers.

We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification cost…

This work investigates square loss in overparametrized neural networks, revealing its advantages in robustness and calibration.

The paper analyzes the maximum margin algorithm's performance on noisy data.

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost. However, previous existing methods only considered firs…

We present a growing dimension asymptotic formalism. The perspective in this paper is classification theory and we show that it can accommodate probabilistic networks classifiers, including naive Bayes model and its augmented version. When represented as a Bayesian network these classifiers have an important advantage:…

New scoring rules improve probabilistic classification model evaluation.

The paper analyzes classification algorithms on Korobov space and derives learning rates.

Study identifies and mitigates causes of image misclassifications in CNN models.

We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-conv…

New algorithm optimizes PAC-Bayes bound without surrogate loss.

Proposes a novel SVM model for binary classification with different misclassification costs.

A lot of attention has been devoted to multimedia indexing over the past few years. In the literature, we often consider two kinds of fusion schemes: The early fusion and the late fusion. In this paper we focus on late classifier fusion, where one combines the scores of each modality at the decision level. To tackle th…

Deep learning has become the method of choice in many application domains of machine learning in recent years, especially for multi-class classification tasks. The most common loss function used in this context is the cross-entropy loss, which reduces to the log loss in the typical case when there is a single correct r…

Paper introduces a new uncertainty measure for misclassification detection.

New insights into when benign overfitting occurs in linear and classification tasks.

In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early fusion and the late fusion. We focus on late classifier fusion, where one combines the scores of each modality at the decision level. To ta…

Non-convex SGD learns halfspaces with adversarial label noise efficiently.

Detects misclassifications and adversarial examples using neural network logits.

AWP improves robustness by flattening weight loss landscape.

A new framework selects information sources to test hypotheses robustly, even with misclassifications.

New method optimizes matrix denoising for weighted loss functions and heterogeneous signals.

Introduces MWLD to measure loss inequality across groups.

Efficient algorithm for CLSBM reduces misclassification rate.

SoftAdapt dynamically adjusts loss weights for multi-part functions.

In this study, a novel sparsity-driven weighted ensemble classifier (SDWEC) that improves classification accuracy and minimizes the number of classifiers is proposed. Using pre-trained classifiers, an ensemble in which base classifiers votes according to assigned weights is formed. These assigned weights directly affec…

Adversarial training makes logistic regression weight loss landscapes sharper.

New test for SGD in binary classification reduces computation time.

Investigates multiclass classification with rejection, achieving state-of-the-art performance and deriving calibration conditions.

This research improves deep neural network calibration using a new loss function.

Proposes an angle-based framework for multicategory cost-sensitive classification.

Study improves adversarial classification using distributionally robust models.

A new algorithm improves semi-supervised learning in unbalanced and heterogeneous networks.