arXiv research

A new method combines simple binary classifiers to build complex multiclass classifiers, achieving performance limits in a Gaussian setting.

Classifies surfaces with no Gaussian curvature.

A Gaussian mixture model improves generalization for long-tailed data.

Linear classifiers can resist adversarial attacks on Gaussian data.

We study the robustness of classifiers to various kinds of random noise models. In particular, we consider noise drawn uniformly from the $\ell\_p$ ball for $p \in [1, \infty]$ and Gaussian noise with an arbitrary covariance matrix. We characterize this robustness to random noise in terms of the distance to the decisio…

Regularization improves robustness of smoothed classifiers.

DeepCCG adapts classifiers to representation shifts in one step.

Study shows how over-parameterized classifiers can still perform well on noisy data.

Optimal classifiers derived from GMMs are approximated by deep neural networks.

In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data. Samples from outside the distribution may be classified as an existing class with high probability by softmax-based classifiers; such incorrect classifications affect the performance of …

Paper explores Bayes rule for Gaussian mixtures with missing data, outperforming supervised classifiers.

Support vector data description (SVDD) is a popular technique for detecting anomalies. The SVDD classifier partitions the whole space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD class…

Machine learning (ML) classification is increasingly used in safety-critical systems. Protecting ML classifiers from adversarial examples is crucial. We propose that the main threat is that of an attacker perturbing a confidently classified input to produce a confident misclassification. To protect against this we devi…

GMVAE improves open-set classification by clustering latent representations.

We introduce a novel approach for training adversarial models by replacing the discriminator score with a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target bi-modal distribution implicitly through meta-adversarial training. W…

Classifies surfaces in hyperbolic space with constant Gaussian curvature.

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with CO…

The paper presents a probabilistic framework for SPD matrices in machine learning.

In this paper we classify compact minimal surfaces in $S^5$ with non-negative Gaussian curvature using the notion of a contact angle.

Paper uses GMM and MAF for probabilistic classification, outperforming simpler models.

Gaussian processes classify graphs using vertex and edge features.

We study high-dimensional Gaussian mixture classification using statistical physics methods.

Skew Gaussian Processes improve classification performance by allowing asymmetry.

The paper explores robust classifiers for imbalanced Gaussian data.

Max-margin classifiers' behavior is studied in high dimensions with non-Gaussian features.

Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an important task to measure tumor responses to treatments. However, automatic segmentation is very challenging. This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Ve…

In the framework of supervised classification (discrimination) for functional data, it is shown that the optimal classification rule can be explicitly obtained for a class of Gaussian processes with "triangular" covariance functions. This explicit knowledge has two practical consequences. First, the consistency of the …

Support vector data description (SVDD) is a popular anomaly detection technique. The SVDD classifier partitions the whole data space into an inlier region, which consists of the region near the training data, and an outlier region, which consists of points away from the training data. The computation of the SVDD classi…

GNIs induce a regulariser that penalizes high-frequency components in neural network activations.

We present a scalable end-to-end classifier that uses streaming physiological and medication data to accurately predict the onset of sepsis, a life-threatening complication from infections that has high mortality and morbidity. Our proposed framework models the multivariate trajectories of continuous-valued physiologic…

The study classifies surfaces with specific curvature properties.

We propose an active set selection framework for Gaussian process classification for cases when the dataset is large enough to render its inference prohibitive. Our scheme consists of a two step alternating procedure of active set update rules and hyperparameter optimization based upon marginal likelihood maximization.…

We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\ell_2$ norm. This "randomized smoothing" technique has been proposed recently in the literature, but existing guarantees are loose. We prove a tight robu…

We introduce a wrapped Gaussian for SPD matrices, enhancing data analysis.

As increasing amounts of sensitive personal information is aggregated into data repositories, it has become important to develop mechanisms for processing the data without revealing information about individual data instances. The differential privacy model provides a framework for the development and theoretical analy…

Study on clustering in high dimensions with anisotropic Gaussian mixtures, showing interpolation can be optimal and robust.

A new method reduces energy consumption in machine learning by using multiple, less costly data sources.

Study modular surfaces in Lorentz-Minkowski 3-space, classifying and analyzing their curvature and applications.

Proposes a deep neural network for multi-dimensional functional data classification.

Unified smoothing for robust classification improves accuracy.

Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In any probabilistic setting, Bayesian averaging (BA) provides theoretically optimal…

The study classifies parallel mean curvature spheres in a sphere-hyperbolic product space.

Study selective classification with halfspaces, achieving error bounds under Gaussian distributions.

Variational methods have been recently considered for scaling the training process of Gaussian process classifiers to large datasets. As an alternative, we describe here how to train these classifiers efficiently using expectation propagation. The proposed method allows for handling datasets with millions of data insta…

We classify complete biharmonic surfaces with parallel mean curvature vector field and non-negative Gaussian curvature in complex space forms.

Model predicts epileptic seizures with high accuracy using EEG signals.

We present a general framework for classification of sparse and irregularly-sampled time series. The properties of such time series can result in substantial uncertainty about the values of the underlying temporal processes, while making the data difficult to deal with using standard classification methods that assume …

Adversarial training can lead to overfitting without compromising robustness.