arXiv research

This paper compares two clustering evaluation metrics, revealing their differences and properties.

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

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…

The paper develops a method to accurately estimate the Bayes misclassification error rate.

Subspace models play an important role in a wide range of signal processing tasks, and this paper explores how the pairwise geometry of subspaces influences the probability of misclassification. When the mismatch between the signal and the model is vanishingly small, the probability of misclassification is determined b…

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

Adversarial attacks for image classification are small perturbations to images that are designed to cause misclassification by a model. Adversarial attacks formally correspond to an optimization problem: find a minimum norm image perturbation, constrained to cause misclassification. A number of effective attacks have b…

Detects misclassifications and adversarial examples using neural network logits.

A new method for high-dimensional data classification reduces misclassification errors.

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

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

Unsupervised classification methods learn a discriminative classifier from unlabeled data, which has been proven to be an effective way of simultaneously clustering the data and training a classifier from the data. Various unsupervised classification methods obtain appealing results by the classifiers learned in an uns…

Unified framework for cost-sensitive ensemble learning.

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…

Bayesian method reduces misclassification errors in ranking Pareto-optimal solutions.

New algorithm reduces misclassification costs in neural networks.

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

Develops probabilistic safety regions for scalable classifiers.

This paper proposes CSADA to make DNNs cost-sensitive.

Robust GQDA improves classification accuracy in non-Normal data.

Minimizes indecisions in selective classification to control misclassification rates.

We analyze the problem of learning a single user's preferences in an active learning setting, sequentially and adaptively querying the user over a finite time horizon. Learning is conducted via choice-based queries, where the user selects her preferred option among a small subset of offered alternatives. These queries …

Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in the…

Efficient method classifies locally stationary time series based on second-order characteristics.

Study on error probability for classification of heavy-tailed renewal processes.

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the e…

Polynomial-time tester-learner for general halfspaces with Gaussian adversarial noise.

Boosting algorithm reduces error in noisy data.

Optimal subset selection for hypothesis testing with penalties.

Optimizes active learning for machine learning models with Bayesian approach.

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

We consider high-dimensional quadratic classifiers in non-sparse settings. The target of classification rules is not Bayes error rates in the context. The classifier based on the Mahalanobis distance does not always give a preferable performance even if the populations are normal distributions having known covariance m…

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

We identify spectral conditions for reliable neural probe interpretation.

The study sets limits on how well halfspaces can be learned when labels are corrupted.

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

Self-training algorithm improves classifier performance with labeled and unlabeled data.

The paper explores how benign overfitting occurs in heavy-tailed input distributions.

New method compresses large sample data for faster discriminant analysis.

TWIST algorithm detects communities in multi-layer networks with tensor decomposition.

Study improves theoretical understanding of Bayesian deep learning for classification tasks.

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

The paper studies how to use AI-generated labels in econometrics to avoid bias.

PANDA improves linear discriminant analysis in high dimensions with minimal tuning.

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situati…

We propose an empirical Bayes estimator based on Dirichlet process mixture model for estimating the sparse normalized mean difference, which could be directly applied to the high dimensional linear classification. In theory, we build a bridge to connect the estimation error of the mean difference and the misclassificat…

Even todays most advanced machine learning models are easily fooled by almost imperceptible perturbations of their inputs. Foolbox is a new Python package to generate such adversarial perturbations and to quantify and compare the robustness of machine learning models. It is build around the idea that the most comparabl…

Optimal posterior distributions improve SVM classifiers and parameter selection.