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2020 – today
- 2024
[i14]Tung L. Nguyen, Toby Dylan Hocking:
Deep Learning Approach for Changepoint Detection: Penalty Parameter Optimization. CoRR abs/2408.00856 (2024)- 2023
- [j17]Toby Dylan Hocking
, Anuraag Srivastava:
Labeled optimal partitioning. Comput. Stat. 38(1): 461-480 (2023) - [j16]Jonathan Hillman, Toby Dylan Hocking:
Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection. J. Mach. Learn. Res. 24: 70:1-70:24 (2023) - [j15]Vincent Runge, Toby Dylan Hocking, Gaetano Romano, Fatemeh Afghah, Paul Fearnhead, Guillem Rigaill:
gfpop: An R Package for Univariate Graph-Constrained Change-Point Detection. J. Stat. Softw. 106(6) (2023) - [j14]Karl Harshe, Jack R. Williams, Toby Dylan Hocking, Zachary F. Lerner:
Predicting Neuromuscular Engagement to Improve Gait Training With a Robotic Ankle Exoskeleton. IEEE Robotics Autom. Lett. 8(8): 5055-5060 (2023) - [i13]Kyle R. Rust, Toby Dylan Hocking:
A Log-linear Gradient Descent Algorithm for Unbalanced Binary Classification using the All Pairs Squared Hinge Loss. CoRR abs/2302.11062 (2023) - [i12]Daniel Agyapong, Jeffrey Ryan Propster, Jane Marks, Toby Dylan Hocking:
Cross-Validation for Training and Testing Co-occurrence Network Inference Algorithms. CoRR abs/2309.15225 (2023) - 2022
- [j13]Joseph Vargovich, Toby Dylan Hocking:
Linear Time Dynamic Programming for Computing Breakpoints in the Regularization Path of Models Selected From a Finite Set. J. Comput. Graph. Stat. 31(1): 313-323 (2022) - [j12]Avinash Barnwal, Hyunsu Cho, Toby Hocking:
Survival Regression with Accelerated Failure Time Model in XGBoost. J. Comput. Graph. Stat. 31(4): 1292-1302 (2022) - [j11]Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque:
Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data. J. Stat. Softw. 101(10) (2022) - [j10]Ana Paula Chaves, Jesse Egbert, Toby Hocking, Eck Doerry, Marco Aurélio Gerosa:
Chatbots Language Design: The Influence of Language Variation on User Experience with Tourist Assistant Chatbots. ACM Trans. Comput. Hum. Interact. 29(2): 13:1-13:38 (2022) - [c11]Joseph R. Barr, Toby Dylan Hocking, Garinn Morton, Tyler Thatcher, Peter Shaw:
Classifying Imbalanced Data with AUM Loss. TransAI 2022: 135-141 - [c10]Joseph R. Barr, Peter Shaw, Faisal N. Abu-Khzam, Tyler Thatcher, Toby Dylan Hocking:
Graph Embedding: A Methodological Survey. TransAI 2022: 142-148 - [c9]Toby Dylan Hocking, Joseph R. Barr, Tyler Thatcher:
Interpretable linear models for predicting security vulnerabilities in source code. TransAI 2022: 149-155 - [i11]Toby Dylan Hocking, Jacob M. Kaufman, Alyssa J. Stenberg:
Functional Labeled Optimal Partitioning. CoRR abs/2210.02580 (2022) - 2021
- [j9]Arnaud Liehrmann, Guillem Rigaill, Toby Dylan Hocking:
Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models. BMC Bioinform. 22(1): 323 (2021) - [j8]Atiyeh Fotoohinasab, Toby Hocking, Fatemeh Afghah:
A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection. Comput. Biol. Medicine 130: 104208 (2021) - [j7]Toby Dylan Hocking:
Wide-to-tall Data Reshaping Using Regular Expressions and the nc Package. R J. 13(1): 69 (2021) - [c8]Akhila Chowdary Kolla, Alex Groce, Toby Dylan Hocking:
Fuzz Testing the Compiled Code in R Packages. ISSRE 2021: 300-308 - [i10]Ana Paula Chaves, Jesse Egbert, Toby Hocking, Eck Doerry, Marco Aurélio Gerosa:
Chatbots language design: the influence of language variation on user experience. CoRR abs/2101.11089 (2021) - [i9]Atiyeh Fotoohinasab, Toby Hocking, Fatemeh Afghah:
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection. CoRR abs/2102.01319 (2021) - [i8]Atiyeh Fotoohinasab, Toby Hocking, Fatemeh Afghah:
A Greedy Graph Search Algorithm Based on Changepoint Analysis for Automatic QRS Complex Detection. CoRR abs/2102.03538 (2021) - [i7]Jonathan Hillman, Toby Dylan Hocking:
Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for Binary Classification and Changepoint Detection. CoRR abs/2107.01285 (2021) - 2020
- [j6]Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque:
Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data. J. Mach. Learn. Res. 21: 87:1-87:40 (2020) - [c7]Atiyeh Fotoohinasab, Toby Hocking, Fatemeh Afghah:
A Graph-Constrained Changepoint Learning Approach for Automatic QRS-Complex Detection. ACSSC 2020: 950-954 - [c6]Atiyeh Fotoohinasab, Toby Hocking, Fatemeh Afghah:
A Graph-constrained Changepoint Detection Approach for ECG Segmentation. EMBC 2020: 332-336 - [c5]Toby Dylan Hocking, Guillaume Bourque:
Machine Learning Algorithms for Simultaneous Supervised Detection of Peaks in Multiple Samples andCell Types. PSB 2020: 367-378 - [i6]Toby Hocking, Joseph Vargovich:
Linear time dynamic programming for the exact path of optimal models selected from a finite set. CoRR abs/2003.02808 (2020) - [i5]Atiyeh Fotoohinasab, Toby Hocking, Fatemeh Afghah:
A Graph-constrained Changepoint Detection Approach for ECG Segmentation. CoRR abs/2004.13558 (2020) - [i4]Avinash Barnwal, Hyunsu Cho, Toby Dylan Hocking:
Survival regression with accelerated failure time model in XGBoost. CoRR abs/2006.04920 (2020) - [i3]Toby Dylan Hocking, Anuraag Srivastava:
Labeled Optimal Partitioning. CoRR abs/2006.13967 (2020)
2010 – 2019
- 2019
- [j5]Toby Dylan Hocking:
Comparing namedCapture with other R packages for regular expressions. R J. 11(2): 328 (2019) - 2018
- [d1]Toby Hocking:
chipseq. UCI Machine Learning Repository, 2018
- 2017
- [j4]Toby Dylan Hocking, Patricia Goerner-Potvin, Andreanne Morin, Xiaojian Shao, Tomi Pastinen, Guillaume Bourque:
Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning. Bioinform. 33(4): 491-499 (2017) - [j3]Robert Maidstone, Toby Hocking, Guillem Rigaill, Paul Fearnhead:
On optimal multiple changepoint algorithms for large data. Stat. Comput. 27(2): 519-533 (2017) - [c4]Alexandre Drouin, Toby Hocking, François Laviolette:
Maximum Margin Interval Trees. NIPS 2017: 4947-4956 - [i2]Alexandre Drouin, Toby Dylan Hocking, François Laviolette:
Maximum Margin Interval Trees. CoRR abs/1710.04234 (2017) - 2015
- [c3]Toby Hocking, Guillem Rigaill, Guillaume Bourque:
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data. ICML 2015: 324-332 - 2014
- [j2]Toby Dylan Hocking, Valentina Boeva, Guillem Rigaill, Gudrun Schleiermacher, Isabelle Janoueix-Lerosey, Olivier Delattre, Wilfrid Richer, Franck Bourdeaut, Miyuki Suguro, Masao Seto, Francis R. Bach, Jean-Philippe Vert:
SegAnnDB: interactive Web-based genomic segmentation. Bioinform. 30(11): 1539-1546 (2014) - [i1]Toby Dylan Hocking, Supaporn Spanurattana, Masashi Sugiyama:
Support vector comparison machines. CoRR abs/1401.8008 (2014) - 2013
- [j1]Toby Dylan Hocking, Gudrun Schleiermacher, Isabelle Janoueix-Lerosey, Valentina Boeva, Julie Cappo, Olivier Delattre, Francis R. Bach, Jean-Philippe Vert:
Learning smoothing models of copy number profiles using breakpoint annotations. BMC Bioinform. 14: 164 (2013) - [c2]Toby Hocking, Guillem Rigaill, Jean-Philippe Vert, Francis R. Bach:
Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression. ICML (3) 2013: 172-180 - 2011
- [c1]Toby Hocking, Jean-Philippe Vert, Francis R. Bach, Armand Joulin:
Clusterpath: an Algorithm for Clustering using Convex Fusion Penalties. ICML 2011: 745-752
Coauthor Index
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last updated on 2024-10-07 21:23 CEST by the dblp team
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