Toby Hocking

NeurIPS Conference 2017 Conference Paper

Maximum Margin Interval Trees

• Alexandre Drouin
• Toby Hocking
• Francois 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 prediction function, and the training output labels indicate an interval of possible values. Whereas most existing algorithms for this task are linear models, in this paper we investigate learning nonlinear tree models. We propose to learn a tree by minimizing a margin-based discriminative objective function, and we provide a dynamic programming algorithm for computing the optimal solution in log-linear time. We show empirically that this algorithm achieves state-of-the-art speed and prediction accuracy in a benchmark of several data sets.

ICML Conference 2015 Conference Paper

PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data

• Toby Hocking
• Guillem Rigaill
• Guillaume Bourque

Peak detection is a central problem in genomic data analysis, and current algorithms for this task are unsupervised and mostly effective for a single data type and pattern (e. g. H3K4me3 data with a sharp peak pattern). We propose PeakSeg, a new constrained maximum likelihood segmentation model for peak detection with an efficient inference algorithm: constrained dynamic programming. We investigate unsupervised and supervised learning of penalties for the critical model selection problem. We show that the supervised method has state-of-the-art peak detection across all data sets in a benchmark that includes both sharp H3K4me3 and broad H3K36me3 patterns.

ICML Conference 2013 Conference Paper

Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression

• Toby Hocking
• Guillem Rigaill
• Jean-Philippe Vert
• Francis R. Bach

In segmentation models, the number of change-points is typically chosen using a penalized cost function. In this work, we propose to learn the penalty and its constants in databases of signals with weak change-point annotations. We propose a convex relaxation for the resulting interval regression problem, and solve it using accelerated proximal gradient methods. We show that this method achieves state-of-the-art change-point detection in a database of annotated DNA copy number profiles from neuroblastoma tumors.

ICML Conference 2011 Conference Paper

Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties

• Toby Hocking
• Jean-Philippe Vert
• Francis R. Bach
• Armand Joulin