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1
OWL2Vec: embedding of OWL ontologies
OWL2Vec: embedding of OWL ontologies
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OWL2Vec: embedding of OWL ontologies

Machine learning, 2021-07, Vol.110 (7), p.1813-1845 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05997-6

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2
Regularisation of neural networks by enforcing Lipschitz continuity
Regularisation of neural networks by enforcing Lipschitz continuity
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Regularisation of neural networks by enforcing Lipschitz continuity

Machine learning, 2021-02, Vol.110 (2), p.393-416 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-020-05929-w

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3
A survey on semi-supervised learning
A survey on semi-supervised learning
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A survey on semi-supervised learning

Machine learning, 2020-02, Vol.109 (2), p.373-440 [Peer Reviewed Journal]

The Author(s) 2019 ;Machine Learning is a copyright of Springer, (2019). All Rights Reserved. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-019-05855-6

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4
Scalable Bayesian preference learning for crowds
Scalable Bayesian preference learning for crowds
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Scalable Bayesian preference learning for crowds

Machine learning, 2020-04, Vol.109 (4), p.689-718 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-019-05867-2

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5
High-dimensional Bayesian optimization using low-dimensional feature spaces
High-dimensional Bayesian optimization using low-dimensional feature spaces
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High-dimensional Bayesian optimization using low-dimensional feature spaces

Machine learning, 2020-09, Vol.109 (9-10), p.1925-1943 [Peer Reviewed Journal]

The Author(s) 2020 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-020-05899-z

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6
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
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Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Machine learning, 2021-03, Vol.110 (3), p.457-506 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05946-3

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7
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
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Challenges of real-world reinforcement learning: definitions, benchmarks and analysis

Machine learning, 2021-09, Vol.110 (9), p.2419-2468 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2021. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-05961-4

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8
Learning from positive and unlabeled data: a survey
Learning from positive and unlabeled data: a survey
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Learning from positive and unlabeled data: a survey

Machine learning, 2020-04, Vol.109 (4), p.719-760 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020 ;The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2020. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-020-05877-5

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9
Gradient descent optimizes over-parameterized deep ReLU networks
Gradient descent optimizes over-parameterized deep ReLU networks
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Gradient descent optimizes over-parameterized deep ReLU networks

Machine learning, 2020-03, Vol.109 (3), p.467-492 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 ;Machine Learning is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-019-05839-6

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10
Adaptive random forests for evolving data stream classification
Adaptive random forests for evolving data stream classification
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Adaptive random forests for evolving data stream classification

Machine learning, 2017-10, Vol.106 (9-10), p.1469-1495 [Peer Reviewed Journal]

The Author(s) 2017 ;Machine Learning is a copyright of Springer, 2017. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-017-5642-8

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11
Multi-target regression via input space expansion: treating targets as inputs
Multi-target regression via input space expansion: treating targets as inputs
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Multi-target regression via input space expansion: treating targets as inputs

Machine learning, 2016-07, Vol.104 (1), p.55-98 [Peer Reviewed Journal]

The Author(s) 2016 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-016-5546-z

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12
Temporal pattern attention for multivariate time series forecasting
Temporal pattern attention for multivariate time series forecasting
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Temporal pattern attention for multivariate time series forecasting

Machine learning, 2019-09, Vol.108 (8-9), p.1421-1441 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 ;Machine Learning is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-019-05815-0

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13
Optimal classification trees
Optimal classification trees
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Optimal classification trees

Machine learning, 2017-07, Vol.106 (7), p.1039-1082 [Peer Reviewed Journal]

The Author(s) 2017 ;Machine Learning is a copyright of Springer, 2017. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-017-5633-9

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14
A3T: accuracy aware adversarial training
A3T: accuracy aware adversarial training
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A3T: accuracy aware adversarial training

Machine learning, 2023-09, Vol.112 (9), p.3191-3210 [Peer Reviewed Journal]

The Author(s) 2023 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-023-06341-w

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15
Kappa Updated Ensemble for drifting data stream mining
Kappa Updated Ensemble for drifting data stream mining
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Kappa Updated Ensemble for drifting data stream mining

Machine learning, 2020, Vol.109 (1), p.175-218 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 ;Machine Learning is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-019-05840-z

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16
Loda: Lightweight on-line detector of anomalies
Loda: Lightweight on-line detector of anomalies
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Loda: Lightweight on-line detector of anomalies

Machine learning, 2016-02, Vol.102 (2), p.275-304 [Peer Reviewed Journal]

The Author(s) 2015 ;The Author(s) 2016 ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-015-5521-0

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17
Automotive fault nowcasting with machine learning and natural language processing
Automotive fault nowcasting with machine learning and natural language processing
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Automotive fault nowcasting with machine learning and natural language processing

Machine learning, 2024-02, Vol.113 (2), p.843-861 [Peer Reviewed Journal]

The Author(s) 2023 ;ISSN: 0885-6125 ;ISSN: 1573-0565 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-023-06398-7

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18
A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety
A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety
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A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety

Machine learning, 2023-04, Vol.112 (4), p.1287-1313 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022 ;The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2022. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-022-06151-6

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19
HIVE-COTE 2.0: a new meta ensemble for time series classification
HIVE-COTE 2.0: a new meta ensemble for time series classification
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HIVE-COTE 2.0: a new meta ensemble for time series classification

Machine learning, 2021-12, Vol.110 (11-12), p.3211-3243 [Peer Reviewed Journal]

The Author(s) 2021 ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-021-06057-9

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20
Attacking neural machine translations via hybrid attention learning
Attacking neural machine translations via hybrid attention learning
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Attacking neural machine translations via hybrid attention learning

Machine learning, 2022-11, Vol.111 (11), p.3977-4002 [Peer Reviewed Journal]

The Author(s) 2022 ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-022-06249-x

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