arXiv research

New methods detect unfairness in multiclass classifiers using DCP.

Paper studies multiclass classifiers from binary classifiers, proving methods and demonstrating advantages.

Paper proposes MCMA architecture for neural approximate computing with higher invocation rate and energy savings.

New algorithm selects optimal subset for multiclass classifier training.

The use of Reinforcement Learning in real-world scenarios is strongly limited by issues of scale. Most RL learning algorithms are unable to deal with problems composed of hundreds or sometimes even dozens of possible actions, and therefore cannot be applied to many real-world problems. We consider the RL problem in the…

Predicts accuracy of classifiers on unseen classes.

Deep learning identifies unknown IoT devices in network traffic.

Paper presents a new method for multiclass classification using hyperplane arrangements.

In this paper we propose solving localized multiple kernel learning (LMKL) using LMKL-Net, a feedforward deep neural network. In contrast to previous works, as a learning principle we propose {\em parameterizing} both the gating function for learning kernel combination weights and the multiclass classifier in LMKL usin…

Paper tackles noisy bandit feedback for multiclass classification.

We propose a new splitting criterion for a meta-learning approach to multiclass classifier design that adaptively merges the classes into a tree-structured hierarchy of increasingly difficult binary classification problems. The classification tree is constructed from empirical estimates of the Henze-Penrose bounds on t…

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

New framework for evaluating multiclass classifier calibration.

We propose Rademacher complexity bounds for multiclass classifiers trained with a two-step semi-supervised model. In the first step, the algorithm partitions the partially labeled data and then identifies dense clusters containing $κ$ predominant classes using the labeled training examples such that the proportion of t…

Proposes a new method to improve multiclass probability calibration.

Gradient boosted decision trees are a popular machine learning technique, in part because of their ability to give good accuracy with small models. We describe two extensions to the standard tree boosting algorithm designed to increase this advantage. The first improvement extends the boosting formalism from scalar-val…

Paper designs optimal ECOCs using IP for robust multiclass classification.

Data mining techniques predict breast cancer types with high accuracy.

Geometry-aware KDE model improves multiclass quantification.

Binary and multiclass epilepsy detection methods using EEG features.

A new weighted MCC measure improves classifier performance evaluation.

Proposes a new binary classification model inspired by fluid phase separation.

Proposes top-label calibration and M2B framework for multiclass to binary calibration.

New method gives provable error bounds for neural nets under distribution shift.

EAST aligns neural network classifiers with user-defined evaluation metrics.

Delaytron learns multi-class classifiers with delayed feedbacks, achieving efficient regret bounds.