Research output per year
Research output per year
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Dive into the research topics where Toby Hocking is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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gfpop: An R Package for Univariate Graph-Constrained Change-Point Detection
Runge, V., Hocking, T. D., Romano, G., Afghah, F., Fearnhead, P. & Rigaill, G., 2023, In: Journal of Statistical Software. 106Research output: Contribution to journal › Article › peer-review
Open Access -
Labeled optimal partitioning
Hocking, T. D. & Srivastava, A., Mar 2023, In: Computational Statistics. 38, 1, p. 461-480 20 p.Research output: Contribution to journal › Article › peer-review
1 Scopus citations -
Chatbots Language Design: The Influence of Language Variation on User Experience with Tourist Assistant Chatbots
Chaves, A. P., Egbert, J., Hocking, T., Doerry, E. & Gerosa, M. A., Apr 2022, In: ACM Transactions on Computer-Human Interaction. 29, 2, 13.Research output: Contribution to journal › Article › peer-review
6 Scopus citations -
Classifying Imbalanced Data with AUM Loss
Barr, J. R., Hocking, T. D., Morton, G., Thatcher, T. & Shaw, P., 2022, Proceedings - 2022 4th International Conference on Transdisciplinary AI, TransAI 2022. Institute of Electrical and Electronics Engineers Inc., p. 135-141 7 p. (Proceedings - 2022 4th International Conference on Transdisciplinary AI, TransAI 2022).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data
Hocking, T. D., Rigaill, G., Fearnhead, P. & Bourque, G., 2022, In: Journal of Statistical Software. 101, 10Research output: Contribution to journal › Article › peer-review
Open Access2 Scopus citations
Datasets
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Survival regression with accelerated failure time model in XGBoost
Barnwal, A. (Creator), Cho, H. (Creator) & Hocking, T. (Creator), Taylor & Francis, 2022
DOI: 10.6084/m9.figshare.19651403.v1, https://tandf.figshare.com/articles/dataset/Survival_regression_with_accelerated_failure_time_model_in_XGBoost/19651403/1
Dataset
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Additional file 1 of Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models
Rigaill, G. (Creator), Hocking, T. (Creator) & Liehrmann, A. (Creator), figshare, 2021
DOI: 10.6084/m9.figshare.14782689.v1, https://doi.org/10.6084%2Fm9.figshare.14782689.v1
Dataset
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Linear time dynamic programming for computing breakpoints in the regularization path of models selected from a finite set
Vargovich, J. (Creator) & Hocking, T. (Creator), Taylor & Francis, 2021
DOI: 10.6084/m9.figshare.17014513.v1, https://tandf.figshare.com/articles/dataset/Linear_time_dynamic_programming_for_computing_breakpoints_in_the_regularization_path_of_models_selected_from_a_finite_set/17014513/1
Dataset
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Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models
Liehrmann, A. (Creator), Rigaill, G. (Creator) & Hocking, T. (Creator), figshare, 2021
DOI: 10.6084/m9.figshare.c.5467308.v1, https://springernature.figshare.com/collections/Increased_peak_detection_accuracy_in_over-dispersed_ChIP-seq_data_with_supervised_segmentation_models/5467308/1
Dataset