34 citations · 84 across the 24 of their papers we have counts for
6 papers · 1 filter
SOAK: Same/Other/All K-fold cross-validation for estimating similarity of patterns in data subsets
Toby Dylan Hocking, Gabrielle Thibault, Cameron Scott Bodine +3
In many real-world applications of machine learning, we are interested to know if it is possible to train on the data that we have gathered so far, and obtain accurate predictions…
Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for Binary Classification and Changepoint Detection
Jonathan Hillman, Toby Dylan Hocking
Receiver Operating Characteristic (ROC) curves are plots of true positive rate versus false positive rate which are useful for evaluating binary classification models, but difficul…
Labeled Optimal Partitioning
Toby Dylan Hocking, Anuraag Srivastava
In data sequences measured over space or time, an important problem is accurate detection of abrupt changes. In partially labeled data, it is important to correctly predict presenc…
Maximum Margin Interval Trees
Alexandre Drouin, Toby Dylan Hocking, François Laviolette
Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine. The goal is to learn a real-valued pre…
PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples
Toby Dylan Hocking, Guillaume Bourque
Joint peak detection is a central problem when comparing samples in genomic data analysis, but current algorithms for this task are unsupervised and limited to at most 2 sample typ…
Support vector comparison machines
David Venuto, Toby Dylan Hocking, Lakjaree Sphanurattana +1
In ranking problems, the goal is to learn a ranking function from labeled pairs of input points. In this paper, we consider the related comparison problem, where the label indicate…