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.
A new L2D system produces calibrated probabilities of expert correctness without sacrificing accuracy.
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…
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…
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_…
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…
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…
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…
Paper designs optimal ECOCs using IP for robust multiclass classification.
Study on learning to defer to multiple experts with consistent surrogates and confidence calibration.
New research shows SVM and related methods can overfit without harm in multiclass classification.
A robust multiclass SVM tackles imbalanced data uncertainty.