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ORCIDNiek Tax
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- affiliation: Meta Platforms, Inc., UK
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2020 – today
- 2024
[j11]Thomas Robinson
, Niek Tax, Richard Mudd, Ido Guy:
Active learning with biased non-response to label requests. Data Min. Knowl. Discov. 38(4): 2117-2140 (2024)- [i26]Matteo Russo, Andrea Celli, Riccardo Colini-Baldeschi, Federico Fusco, Daniel Haimovich, Dima Karamshuk, Stefano Leonardi, Niek Tax:
Online Learning with Sublinear Best-Action Queries. CoRR abs/2407.16355 (2024) - 2023
- [c21]David S. Watson, Joshua O'Hara, Niek Tax, Richard Mudd, Ido Guy:
Explaining Predictive Uncertainty with Information Theoretic Shapley Values. NeurIPS 2023 - [c20]Takuo Matsubara, Niek Tax, Richard Mudd, Ido Guy:
TCE: A Test-Based Approach to Measuring Calibration Error. UAI 2023: 1390-1400 - [i25]David S. Watson, Joshua O'Hara, Niek Tax, Richard Mudd, Ido Guy:
Explaining Predictive Uncertainty with Information Theoretic Shapley Values. CoRR abs/2306.05724 (2023) - [i24]Takuo Matsubara, Niek Tax, Richard Mudd, Ido Guy:
TCE: A Test-Based Approach to Measuring Calibration Error. CoRR abs/2306.14343 (2023) - [i23]Thomas Robinson, Niek Tax, Richard Mudd, Ido Guy:
Active learning with biased non-response to label requests. CoRR abs/2312.08150 (2023) - [i22]Daniel Haimovich, Dima Karamshuk, Fridolin Linder, Niek Tax, Milan Vojnovic:
On the convergence of loss and uncertainty-based active learning algorithms. CoRR abs/2312.13927 (2023) - 2022
- [j10]Stephan A. Fahrenkrog-Petersen, Niek Tax, Irene Teinemaa, Marlon Dumas, Massimiliano de Leoni, Fabrizio Maria Maggi, Matthias Weidlich:
Fire now, fire later: alarm-based systems for prescriptive process monitoring. Knowl. Inf. Syst. 64(2): 559-587 (2022) - [c19]Sander J. J. Leemans, Niek Tax:
Causal Reasoning over Control-Flow Decisions in Process Models. CAiSE 2022: 183-200 - 2021
- [i21]Niek Tax, Kees Jan de Vries, Mathijs de Jong, Nikoleta Dosoula, Bram van den Akker, Jon Smith, Olivier Thuong, Lucas Bernardi:
Machine Learning for Fraud Detection in E-Commerce: A Research Agenda. CoRR abs/2107.01979 (2021) - 2020
- [j9]Niek Tax, Irene Teinemaa, Sebastiaan J. van Zelst:
An interdisciplinary comparison of sequence modeling methods for next-element prediction. Softw. Syst. Model. 19(6): 1345-1365 (2020)
2010 – 2019
- 2019
- [j8]Niek Tax, Emin Alasgarov, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst:
Generating time-based label refinements to discover more precise process models. J. Ambient Intell. Smart Environ. 11(2): 165-182 (2019) - [j7]Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst:
Discovering more precise process models from event logs by filtering out chaotic activities. J. Intell. Inf. Syst. 52(1): 107-139 (2019) - [j6]Anja F. Syring, Niek Tax, Wil M. P. van der Aalst:
Evaluating Conformance Measures in Process Mining Using Conformance Propositions. Trans. Petri Nets Other Model. Concurr. 14: 192-221 (2019) - [i20]Stephan A. Fahrenkrog-Petersen, Niek Tax, Irene Teinemaa, Marlon Dumas, Massimiliano de Leoni, Fabrizio Maria Maggi, Matthias Weidlich:
Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring. CoRR abs/1905.09568 (2019) - [i19]Niek Tax:
Mining Insights from Weakly-Structured Event Data. CoRR abs/1909.01421 (2019) - [i18]Anja F. Syring, Niek Tax, Wil M. P. van der Aalst:
Evaluating Conformance Measures in Process Mining using Conformance Propositions (Extended version). CoRR abs/1909.02393 (2019) - 2018
- [j5]Niek Tax, Xixi Lu, Natalia Sidorova, Dirk Fahland, Wil M. P. van der Aalst:
The imprecisions of precision measures in process mining. Inf. Process. Lett. 135: 1-8 (2018) - [j4]Niek Tax, Benjamin Dalmas, Natalia Sidorova, Wil M. P. van der Aalst, Sylvie Norre:
Interest-driven discovery of local process models. Inf. Syst. 77: 105-117 (2018) - [j3]Benjamin Dalmas, Niek Tax, Sylvie Norre:
Heuristic Mining Approaches for High-Utility Local Process Models. Trans. Petri Nets Other Model. Concurr. 13: 27-51 (2018) - [c18]Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma:
LocalProcessModelDiscovery: Bringing Petri Nets to the Pattern Mining World. Petri Nets 2018: 374-384 - [c17]Irene Teinemaa, Niek Tax, Massimiliano de Leoni, Marlon Dumas, Fabrizio Maria Maggi:
Alarm-Based Prescriptive Process Monitoring. BPM (Forum) 2018: 91-107 - [c16]Niek Tax, Sebastiaan J. van Zelst, Irene Teinemaa:
An Experimental Evaluation of the Generalizing Capabilities of Process Discovery Techniques and Black-Box Sequence Models. BPMDS/EMMSAD@CAiSE 2018: 165-180 - [c15]Niek Tax:
Human Activity Prediction in Smart Home Environments with LSTM Neural Networks. Intelligent Environments 2018: 40-47 - [c14]Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst:
Mining Local Process Models with Constraints Efficiently: Applications to the Analysis of Smart Home Data. Intelligent Environments 2018: 56-63 - [c13]Sander J. J. Leemans, Niek Tax, Arthur H. M. ter Hofstede:
Indulpet Miner: Combining Discovery Algorithms. OTM Conferences (1) 2018: 97-115 - [i17]Irene Teinemaa, Niek Tax, Massimiliano de Leoni, Marlon Dumas, Fabrizio Maria Maggi:
Foundations of Prescriptive Process Monitoring. CoRR abs/1803.08706 (2018) - [i16]Irene Teinemaa, Niek Tax, Carlos Bentes:
Automatic Playlist Continuation through a Composition of Collaborative Filters. CoRR abs/1808.04288 (2018) - [i15]Niek Tax, Irene Teinemaa, Sebastiaan J. van Zelst:
An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction. CoRR abs/1811.00062 (2018) - 2017
- [c12]Benjamin Dalmas, Niek Tax, Sylvie Norre:
Heuristics for High-Utility Local Process Model Mining. ATAED@Petri Nets/ACSD 2017: 106-121 - [c11]Niek Tax, Ilya Verenich, Marcello La Rosa, Marlon Dumas:
Predictive Business Process Monitoring with LSTM Neural Networks. CAiSE 2017: 477-492 - [c10]Felix Mannhardt, Niek Tax:
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models. RADAR+EMISA@CAiSE 2017: 55-63 - [c9]Djoerd Hiemstra, Niek Tax, Sander Bockting:
Ranking Learning-to-Rank Methods. LEARNER@ICTIR 2017 - [c8]Niek Tax, Laura Genga, Nicola Zannone:
On the Use of Hierarchical Subtrace Mining for Efficient Local Process Model Mining. SIMPDA 2017: 8-22 - [c7]Laura Genga, Niek Tax, Nicola Zannone:
Mining Local Process Models and Their Correlations. SIMPDA (Revised Selected Papers) 2017: 65-88 - [i14]Niek Tax, Benjamin Dalmas, Natalia Sidorova, Wil M. P. van der Aalst, Sylvie Norre:
Interest-Driven Discovery of Local Process Models. CoRR abs/1703.07116 (2017) - [i13]Felix Mannhardt, Niek Tax:
Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models. CoRR abs/1704.03520 (2017) - [i12]Niek Tax, Xixi Lu, Natalia Sidorova, Dirk Fahland, Wil M. P. van der Aalst:
The Imprecisions of Precision Measures in Process Mining. CoRR abs/1705.03303 (2017) - [i11]Niek Tax, Emin Alasgarov, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma:
Time-Based Label Refinements to Discover More Precise Process Models. CoRR abs/1705.09359 (2017) - [i10]Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst:
Mining Process Model Descriptions of Daily Life through Event Abstraction. CoRR abs/1705.10202 (2017) - [i9]Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst:
Discovering More Precise Process Models from Event Logs by Filtering Out Chaotic Activities. CoRR abs/1711.01287 (2017) - [i8]Niek Tax, Marlon Dumas:
Mining Non-Redundant Sets of Generalizing Patterns from Sequence Databases. CoRR abs/1712.04159 (2017) - 2016
- [j2]Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst:
Mining local process models. J. Innov. Digit. Ecosyst. 3(2): 183-196 (2016) - [c6]Niek Tax, Emin Alasgarov, Natalia Sidorova, Reinder Haakma:
On Generation of Time-based Label Refinements. CS&P 2016: 25-36 - [c5]Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst:
Event Abstraction for Process Mining Using Supervised Learning Techniques. IntelliSys (1) 2016: 251-269 - [c4]Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst:
Log-based Evaluation of Label Splits for Process Models. KES 2016: 63-72 - [c3]Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma:
Heuristic approaches for generating Local Process Models through log projections. SSCI 2016: 1-8 - [i7]Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst:
Mining Local Process Models. CoRR abs/1606.06066 (2016) - [i6]Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst:
Log-based Evaluation of Label Splits for Process Models. CoRR abs/1606.07259 (2016) - [i5]Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst:
Event Abstraction for Process Mining using Supervised Learning Techniques. CoRR abs/1606.07283 (2016) - [i4]Niek Tax, Emin Alasgarov, Natalia Sidorova, Reinder Haakma:
On Generation of Time-based Label Refinements. CoRR abs/1609.03333 (2016) - [i3]Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst, Reinder Haakma:
Heuristic Approaches for Generating Local Process Models through Log Projections. CoRR abs/1610.02876 (2016) - [i2]Niek Tax, Ilya Verenich, Marcello La Rosa, Marlon Dumas:
Predictive Business Process Monitoring with LSTM Neural Networks. CoRR abs/1612.02130 (2016) - 2015
- [j1]Niek Tax, Sander Bockting, Djoerd Hiemstra:
A cross-benchmark comparison of 87 learning to rank methods. Inf. Process. Manag. 51(6): 757-772 (2015) - 2014
- [c2]Chintan Amrit, Niek Tax:
Towards understanding the understandability of UML models. MiSE 2014: 49-54 - [i1]Niek Tax:
Analyzing a practitioner perspective on relevance of published empirical research in Requirements Engineering. CoRR abs/1403.1112 (2014) - 2013
- [c1]Dan Ionita, Niek Tax, Djoerd Hiemstra:
An API-based Search System for One Click Access to Information. NTCIR 2013
Coauthor Index
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last updated on 2024-10-07 21:22 CEST by the dblp team
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