arXiv research

Paper proposes distributed sparse multicategory discriminant analysis for classification.

Proposes an angle-based framework for multicategory cost-sensitive classification.

Develops a novel approach for estimating optimal DTRs with multicategory treatments and censored data.

In many real applications of statistical learning, a decision made from misclassification can be too costly to afford; in this case, a reject option, which defers the decision until further investigation is conducted, is often preferred. In recent years, there has been much development for binary classification with a …

We generalize categorified Jones-Wenzl projectors in odd Khovanov homology.

We show that the multi-class support vector machine (MSVM) proposed by Lee et. al. (2004), can be viewed as a MAP estimation procedure under an appropriate probabilistic interpretation of the classifier. We also show that this interpretation can be extended to a hierarchical Bayesian architecture and to a fully-Bayesia…

Classification is an important statistical learning tool. In real application, besides high prediction accuracy, it is often desirable to estimate class conditional probabilities for new observations. For traditional problems where the number of observations is large, there exist many well developed approaches. Recentl…

Proposes MCLLO for assessing and recalibrating multiclass probability predictions.

Ordinal data are often seen in real applications. Regular multicategory classification methods are not designed for this data type and a more proper treatment is needed. We consider a framework of ordinal classification which pools the results from binary classifiers together. An inherent difficulty of this framework i…

New 2-representations link spectral enhancements in link homology.

Develops methods for near-optimal personalized treatment recommendations.

Proposes a new loss function for better handling mislabeling costs.