Toby Hocking
Orcid: 0000-0002-3146-0865
According to our database1,
Toby Hocking
authored at least 36 papers
between 2011 and 2023.
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Bibliography
2023
Predicting Neuromuscular Engagement to Improve Gait Training With a Robotic Ankle Exoskeleton.
IEEE Robotics Autom. Lett., 2023
J. Stat. Softw., 2023
Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection.
J. Mach. Learn. Res., 2023
Cross-Validation for Training and Testing Co-occurrence Network Inference Algorithms.
CoRR, 2023
A Log-linear Gradient Descent Algorithm for Unbalanced Binary Classification using the All Pairs Squared Hinge Loss.
CoRR, 2023
2022
J. Comput. Graph. Stat., October, 2022
Linear Time Dynamic Programming for Computing Breakpoints in the Regularization Path of Models Selected From a Finite Set.
J. Comput. Graph. Stat., January, 2022
Chatbots Language Design: The Influence of Language Variation on User Experience with Tourist Assistant Chatbots.
ACM Trans. Comput. Hum. Interact., 2022
Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data.
J. Stat. Softw., 2022
Proceedings of the Fourth International Conference on Transdisciplinary AI, 2022
Proceedings of the Fourth International Conference on Transdisciplinary AI, 2022
Proceedings of the Fourth International Conference on Transdisciplinary AI, 2022
2021
Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for Binary Classification and Changepoint Detection.
CoRR, 2021
CoRR, 2021
A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection.
Comput. Biol. Medicine, 2021
Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models.
BMC Bioinform., 2021
Proceedings of the 32nd IEEE International Symposium on Software Reliability Engineering, 2021
2020
Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data.
J. Mach. Learn. Res., 2020
Linear time dynamic programming for the exact path of optimal models selected from a finite set.
CoRR, 2020
Machine Learning Algorithms for Simultaneous Supervised Detection of Peaks in Multiple Samples andCell Types.
Proceedings of the Pacific Symposium on Biocomputing 2020, 2020
Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection.
Proceedings of the 54th Asilomar Conference on Signals, Systems, and Computers, 2020
2019
2018
2017
Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.
Bioinform., 2017
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017
2015
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data.
Proceedings of the 32nd International Conference on Machine Learning, 2015
2014
2013
BMC Bioinform., 2013
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression.
Proceedings of the 30th International Conference on Machine Learning, 2013
2011
Proceedings of the 28th International Conference on Machine Learning, 2011