A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improves uncertainty estimation and OOD detection in neural networks.
We prove new fast learning rates for the one-vs-all multiclass plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging drifting distribution. These are two typical scenarios where training data are not iid. The learning rates are obtained under a multiclass vers…
A new L2D system produces calibrated probabilities of expert correctness without sacrificing accuracy.
Paper studies multiclass classifiers from binary classifiers, proving methods and demonstrating advantages.
Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles origin…
Paper designs optimal ECOCs using IP for robust multiclass classification.
We consider the problem of -class classification (), where the classifier can choose to abstain from making predictions at a given cost, say, a factor of the cost of misclassification. Designing consistent algorithms for such -class classification problems with a `reject option' is the main goal of t…
We present a framework to derive risk bounds for vector-valued learning with a broad class of feature maps and loss functions. Multi-task learning and one-vs-all multi-category learning are treated as examples. We discuss in detail vector-valued functions with one hidden layer, and demonstrate that the conditions under…
New research shows SVM and related methods can overfit without harm in multiclass classification.
Any given classification problem can be modeled using multi-class or One-vs-All (OVA) architecture. An OVA system consists of as many OVA models as the number of classes, providing the advantage of asynchrony, where each OVA model can be re-trained independent of other models. This is particularly advantageous in setti…
New linear algorithms improve wSVMs for multiclass probability estimation.
Paper explains learning property of logistic and softmax losses for balanced and imbalanced class data.
Conditional modeling x \to y is a central problem in machine learning. A substantial research effort is devoted to such modeling when x is high dimensional. We consider, instead, the case of a high dimensional y, where x is either low dimensional or high dimensional. Our approach is based on selecting a small subset y_…
A robust multiclass SVM tackles imbalanced data uncertainty.
Classification with a large number of classes is a key problem in machine learning and corresponds to many real-world applications like tagging of images or textual documents in social networks. If one-vs-all methods usually reach top performance in this context, these approaches suffer from a high inference complexity…
This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient conditions) on the number of measurements required to drive the probability of misclassification to zero in the low-noise regime, both for rando…
Study on learning to defer to multiple experts with consistent surrogates and confidence calibration.
Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDs), which are then used to reason about t…
Study on the structure of classifier boundaries in DNA sequencing.
RCAM-based ensemble combines binary classifiers using similarity and vote scheme.
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…
When concept drift is detected during classification in a data stream, a common remedy is to retrain a framework's classifier. However, this loses useful information if the classifier has learnt the current concept well, and this concept will recur again in the future. Some frameworks retain and reuse classifiers, but …
New method computes discriminative classifiers from generative models.
Study examines how classifier performance is affected by training data quality.
Improves multi-label classification with a new network model.
A review of statistical SSL methods showing improved classifier performance.
A bias classifier is introduced to resist adversarial attacks.
New method controls classifier guidance in diffusion models.
Paper introduces algorithms for explaining monotonic classifiers.
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context…
Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has been towards improving classifier performance, understanding when a classifier's predictions should and should not be trusted has received …
In this paper we present a new Bayesian network model for classification that combines the naive-Bayes (NB) classifier and the finite-mixture (FM) classifier. The resulting classifier aims at relaxing the strong assumptions on which the two component models are based, in an attempt to improve on their classification pe…
New uniqueness concept for adversarial Bayes classifier.
Suppose some classifiers are selected from a set of hypothesis classifiers to form an equally-weighted ensemble that selects a member classifier at random for each input example. Then the ensemble has an error bound consisting of the average error bound for the member classifiers, a term for selectivity that varies fro…
Generative text classifiers are most vulnerable to membership inference attacks.
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…
This paper presents a novel kernel-based generative classifier which is defined in a distortion subspace using polynomial series expansion, named Kernel-Distortion (KD) classifier. An iterative kernel selection algorithm is developed to steadily improve classification performance by repeatedly removing and adding kerne…
Bayesian model fuses multiple classifiers with explicit correlation modeling.
Rule-based classifiers quantify uncertainty using Bernoulli random variables.
Flexible classifier using Mahalanobis distances for non-elliptical distributions.
Random Hyperboxes is a simple yet effective ensemble classifier.
New methods detect unfairness in multiclass classifiers using DCP.
Randomised classifiers outperform deterministic ones in strategic classification.
The paper extends calibration to sets of probabilistic classifiers, finding many ensembles are poorly calibrated.
New classifiers account for context-specific independences.
Enhances mixture models with classifier-defined weights.
This paper proposes a systematic framework to design a classification model that yields a classifier which optimizes a utility function based on prior knowledge. Specifically, as the data size grows, we prove that the produced classifier asymptotically converges to the optimal classifier, an extended version of the Bay…
In Dynamic Ensemble Selection (DES) techniques, only the most competent classifiers are selected to classify a given query sample. Hence, the key issue in DES is how to estimate the competence of each classifier in a pool to select the most competent ones. In order to deal with this issue, we proposed a novel dynamic e…