arXiv research

New research shows the maximum ℓ1-margin classifier doesn't adapt to sparse ground truths.

This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives. We consider a high-dimensional setting where the number of features (weak learners) $p$ scales with the sample size $n$, in an overparametrized regime. Under a class of …

AdaBoost improves binary classification in robust one-bit compressed sensing with adversarial errors.

The study classifies graphs with specific curvature and maximum degree.

In many real-world applications, data is not collected as one batch, but sequentially over time, and often it is not possible or desirable to wait until the data is completely gathered before analyzing it. Thus, we propose a framework to sequentially update a maximum margin classifier by taking advantage of the Maximum…

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

Ensemble learning is a powerful approach to construct a strong learner from multiple base learners. The most popular way to aggregate an ensemble of classifiers is majority voting, which assigns a sample to the class that most base classifiers vote for. However, improved performance can be obtained by assigning weights…

Paper proposes a method to estimate true positive proportion without knowing it.

Paper develops MRCs for supervised classification using generalized maximum entropy.

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…

Study examines liquidation, leverage, and optimal margin requirements in Bitcoin futures markets.

Study explores how neural networks and Transformers learn modular arithmetic with multiple inputs.

Adversarial training is a principled approach for training robust neural networks. Despite of tremendous successes in practice, its theoretical properties still remain largely unexplored. In this paper, we provide new theoretical insights of gradient descent based adversarial training by studying its computational prop…

New method computes discriminative classifiers from generative models.

Deep neural networks can generalize well even with perfect fits to noisy data.

This paper studies structured sparse training of CNNs with a gradual pruning technique that leads to fixed, sparse weight matrices after a set number of epochs. We simplify the structure of the enforced sparsity so that it reduces overhead caused by regularization. The proposed training methodology Campfire explores pr…

We define a generalized likelihood function based on uncertainty measures and show that maximizing such a likelihood function for different measures induces different types of classifiers. In the probabilistic framework, we obtain classifiers that optimize the cross-entropy function. In the possibilistic framework, we …

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

Mirror flow optimizes separable data problems, converging to a maximum margin classifier.

Reduces quantifier variance with accuracy optimization of base classifier.

The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order…

In a broad range of classification and decision making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier accuracy can be assessed using available labe…

A fast method for training linear classifiers maximizes margins.

$KS$-algebra consists of expressions constructed with four kinds operations, the minimum, maximum, difference and additively homogeneous generalized means. Five families of $Z$-classifiers are investigated on binary classification tasks between English phonemes. It is shown that the classifiers are able to reflect well…

Multithreshold Entropy Linear Classifier (MELC) is a recent classifier idea which employs information theoretic concept in order to create a multithreshold maximum margin model. In this paper we analyze its consistency over multithreshold linear models and show that its objective function upper bounds the amount of mis…

A new method compares image classifiers using adaptive sampling of natural images.

LC-CRFs are equivalent to HMMs, and MPM/MAP classifiers can be reformulated as CRFs.

Adam optimizes linear classifiers with separable data.

Sparse multinomial logistic regression for multiclass classification with feature selection.

We consider the orientation-preserving actions of finite groups $G$ on pairs $(S^3, Γ)$, where $Γ$ is a connected graph of genus $g>1$, embedded in $S^3$. For each $g$ we give the maximum order $m_g$ of such $G$ acting on $(S^3, Γ)$ for all such $Γ\subset S^3$. Indeed we will classify all graphs $Γ\subset S^3$ which re…

New method for multiclass classification reduces error bounds.

Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x|y) stays fixed. Label shift arises in settings like medical diagnosis, where a classifier trained to predict disease given symptoms must be adapted to sc…

Given a task of predicting $Y$ from $X$, a loss function $L$, and a set of probability distributions $Γ$ on $(X,Y)$, what is the optimal decision rule minimizing the worst-case expected loss over $Γ$? In this paper, we address this question by introducing a generalization of the principle of maximum entropy. Applying t…

The paper classifies ruled surfaces in Lorentz-Minkowski space that are stationary for the moment of inertia.

We consider orientation-preserving actions of finite groups $G$ on pairs $(S^3, Σ)$, where $Σ$ denotes a compact connected surface embedded in $S^3$. In a previous paper, we considered the case of closed, necessarily orientable surfaces, determined for each genus $g>1$ the maximum order of such a $G$ for all embeddings…

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

The paper compares one-hot encoding to Naïve Bayes for categorical variables.

A new copula estimation method using classification.

Recently, a backdoor data poisoning attack was proposed, which adds mislabeled examples to the training set, with an embedded backdoor pattern, aiming to have the classifier learn to classify to a target class whenever the backdoor pattern is present in a test sample. Here, we address post-training detection of innocuo…

Study classifies translators for mean curvature flow in 3D.

We consider the problem of classifying data manifolds where each manifold represents invariances that are parameterized by continuous degrees of freedom. Conventional data augmentation methods rely upon sampling large numbers of training examples from these manifolds; instead, we propose an iterative algorithm called M…

Paper introduces a novel method for estimating model confidence in deep neural classifiers.

PolyGraph Discrepancy improves graph generative model evaluation.

The paper classifies translation surfaces in a specific hyperelliptic component and finds the maximum number of disjoint geodesics.

Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data. We propose a novel hyperplane classifier for clustering and semi-supervised classificati…

Study improves adversarial classification using distributionally robust models.

Study axisymmetric surfaces in Euclidean space for energy minimization.

In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be present in the training set. The goal is to minimize the generalization error of the classifier on non-corrupted measurements while controlling th…