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ORCIDJesse Davis
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- affiliation: Katholieke Universiteit Leuven, Department of Computer Science, Belgium
- affiliation: University of Washington, Department of Computer Science and Engineering, Seattle, WA, USA
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2020 – today
- 2024
[j26]Kilian Hendrickx
, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis:
Machine learning with a reject option: a survey. Mach. Learn. 113(5): 3073-3110 (2024)- [j25]Arne De Brabandere, Tim Op De Beéck, Kilian Hendrickx, Wannes Meert, Jesse Davis:
TSFuse: automated feature construction for multiple time series data. Mach. Learn. 113(8): 5001-5056 (2024) - [j24]Jesse Davis, Lotte Bransen, Laurens Devos, Arne Jaspers, Wannes Meert, Pieter Robberechts, Jan Van Haaren, Maaike Van Roy:
Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned. Mach. Learn. 113(9): 6977-7010 (2024) - [j23]Arne De Brabandere, Christos Chatzichristos, Wim Van Paesschen, Maarten De Vos, Jesse Davis:
Detecting Epileptic Seizures Using Hand-Crafted and Automatically Constructed EEG Features. IEEE Trans. Biomed. Eng. 71(1): 318-325 (2024) - [j22]Wim Govers, Aras Yurtman, Turgay Aslandere, Nicole Eikelenberg, Wannes Meert, Jesse Davis:
Time-Shifted Transformers for Driver Identification Using Vehicle Data. IEEE Trans. Intell. Transp. Syst. 25(5): 3767-3776 (2024) - [c110]Laurens Devos, Lorenzo Cascioli, Jesse Davis:
Robustness Verification of Multi-Class Tree Ensembles. AAAI 2024: 21019-21028 - [c109]Wei Sun, Mingxiao Li, Jingyuan Sun, Jesse Davis, Marie-Francine Moens:
DMON: A Simple Yet Effective Approach for Argument Structure Learning. LREC/COLING 2024: 5109-5118 - [c108]Chaahat Jain, Lorenzo Cascioli, Laurens Devos, Marcel Vinzent, Marcel Steinmetz, Jesse Davis, Jörg Hoffmann:
Safety Verification of Tree-Ensemble Policies via Predicate Abstraction. ECAI 2024: 1189-1197 - [c107]Luca Stradiotti, Lorenzo Perini, Jesse Davis:
Combining Active Learning and Learning to Reject for Anomaly Detection. ECAI 2024: 2266-2273 - [c106]Luca Stradiotti, Lorenzo Perini, Jesse Davis:
Semi-Supervised Isolation Forest for Anomaly Detection. SDM 2024: 670-678 - [e18]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part I. Lecture Notes in Computer Science 14941, Springer 2024, ISBN 978-3-031-70340-9 [contents] - [e17]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part II. Lecture Notes in Computer Science 14942, Springer 2024, ISBN 978-3-031-70343-0 [contents] - [e16]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part III. Lecture Notes in Computer Science 14943, Springer 2024, ISBN 978-3-031-70351-5 [contents] - [e15]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part IV. Lecture Notes in Computer Science 14944, Springer 2024, ISBN 978-3-031-70358-4 [contents] - [e14]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part V. Lecture Notes in Computer Science 14945, Springer 2024, ISBN 978-3-031-70361-4 [contents] - [e13]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VI. Lecture Notes in Computer Science 14946, Springer 2024, ISBN 978-3-031-70364-5 [contents] - [e12]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VII. Lecture Notes in Computer Science 14947, Springer 2024, ISBN 978-3-031-70367-6 [contents] - [e11]Albert Bifet, Povilas Daniusis, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Kai Puolamäki, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VIII. Lecture Notes in Computer Science 14948, Springer 2024, ISBN 978-3-031-70370-6 [contents] - [i35]Jesse Davis, Pieter Robberechts:
Biases in Expected Goals Models Confound Finishing Ability. CoRR abs/2401.09940 (2024) - [i34]Andrea Pugnana, Lorenzo Perini, Jesse Davis, Salvatore Ruggieri:
Deep Neural Network Benchmarks for Selective Classification. CoRR abs/2401.12708 (2024) - [i33]Lorenzo Cascioli, Laurens Devos, Ondrej Kuzelka, Jesse Davis:
Faster Repeated Evasion Attacks in Tree Ensembles. CoRR abs/2402.08586 (2024) - [i32]Wei Sun, Mingxiao Li, Jingyuan Sun, Jesse Davis, Marie-Francine Moens:
DMON: A Simple yet Effective Approach for Argument Structure Learning. CoRR abs/2405.01216 (2024) - [i31]Ulf Brefeld, Jesse Davis, Laura de Jong, Stephanie Kovalchik:
Computational Approaches to Strategy and Tactics in Sports (Dagstuhl Seminar 24081). Dagstuhl Reports 14(2): 164-181 (2024) - 2023
- [j21]Pietro Totis, Jesse Davis, Luc De Raedt, Angelika Kimmig:
Lifted Reasoning for Combinatorial Counting. J. Artif. Intell. Res. 76: 1-58 (2023) - [j20]Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, Jesse Davis:
A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer. J. Artif. Intell. Res. 77: 517-562 (2023) - [c105]Lorenzo Perini, Vincent Vercruyssen, Jesse Davis:
Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection. KDD 2023: 1897-1906 - [c104]Pieter Robberechts, Maaike Van Roy, Jesse Davis:
un-xPass: Measuring Soccer Player's Creativity. KDD 2023: 4768-4777 - [c103]Lorenzo Perini, Jesse Davis:
Unsupervised Anomaly Detection with Rejection. NeurIPS 2023 - [c102]Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis:
Detecting Evasion Attacks in Deployed Tree Ensembles. ECML/PKDD (5) 2023: 120-136 - [c101]Timo Martens, Lorenzo Perini, Jesse Davis:
Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection. ECML/PKDD (1) 2023: 219-236 - [c100]Dries Van der Pias, Wannes Meert, Johan Verbraecken, Jesse Davis:
A novel reject option applied to sleep stage scoring. SDM 2023: 820-828 - [e10]Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Machine Learning and Data Mining for Sports Analytics - 9th International Workshop, MLSA 2022, Grenoble, France, September 19, 2022, Revised Selected Papers. Communications in Computer and Information Science 1783, Springer 2023, ISBN 978-3-031-27526-5 [contents] - [i30]Lorenzo Perini, Daniele Giannuzzi, Jesse Davis:
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection. CoRR abs/2301.02909 (2023) - [i29]Lorenzo Perini, Jesse Davis:
Unsupervised Anomaly Detection with Rejection. CoRR abs/2305.13189 (2023) - [i28]Wei Sun, Mingxiao Li, Damien Sileo, Jesse Davis, Marie-Francine Moens:
Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence. CoRR abs/2310.01299 (2023) - 2022
- [j19]Sieglinde Bogaert, Jesse Davis, Sam Van Rossom, Benedicte Vanwanseele:
Impact of Gender and Feature Set on Machine-Learning-Based Prediction of Lower-Limb Overuse Injuries Using a Single Trunk-Mounted Accelerometer. Sensors 22(8): 2860 (2022) - [j18]Jill Emmerzaal, Arne De Brabandere, Rob van der Straaten, Johan Bellemans, Liesbet De Baets, Jesse Davis, Ilse Jonkers, Annick Timmermans, Benedicte Vanwanseele:
Can the Output of a Learned Classification Model Monitor a Person's Functional Recovery Status Post-Total Knee Arthroplasty? Sensors 22(10): 3698 (2022) - [c99]Lorenzo Perini, Vincent Vercruyssen, Jesse Davis:
Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity. AAAI 2022: 4128-4136 - [c98]Jonas Schouterden, Jessa Bekker, Jesse Davis, Hendrik Blockeel:
Unifying Knowledge Base Completion with PU Learning to Mitigate the Observation Bias. AAAI 2022: 4137-4145 - [c97]Arne De Brabandere, Zhenxiang Cao, Maarten De Vos, Alexander Bertrand, Jesse Davis:
Semi-supervised Change Point Detection Using Active Learning. DS 2022: 74-88 - [c96]Pieter Robberechts, Wannes Meert, Jesse Davis:
Elastic Product Quantization for Time Series. DS 2022: 157-172 - [c95]Jesse Davis, Lotte Bransen, Laurens Devos, Wannes Meert, Pieter Robberechts, Jan Van Haaren, Maaike Van Roy:
Evaluating Sports Analytics Models: Challenges, Approaches, and Lessons Learned. EBeM@IJCAI 2022 - [c94]Ioannis Antoniadis, Vincent Vercruyssen, Jesse Davis:
Systematic Evaluation of CASH Search Strategies for Unsupervised Anomaly Detection. LIDTA 2022: 8-22 - [c93]Thomas Dierckx, Jesse Davis, Wim Schoutens:
Towards Data-Driven Volatility Modeling with Variational Autoencoders. PKDD/ECML Workshops (2) 2022: 97-111 - [c92]Loren Nuyts, Laurens Devos, Wannes Meert, Jesse Davis:
Bitpaths: Compressing Datasets Without Decreasing Predictive Performance. PKDD/ECML Workshops (1) 2022: 261-268 - [c91]Jeroen Clijmans, Maaike Van Roy, Jesse Davis:
Looking Beyond the Past: Analyzing the Intrinsic Playing Style of Soccer Teams. ECML/PKDD (6) 2022: 370-385 - [c90]Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, Jesse Davis:
Multi-domain Active Learning for Semi-supervised Anomaly Detection. ECML/PKDD (4) 2022: 485-501 - [e9]Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Machine Learning and Data Mining for Sports Analytics - 8th International Workshop, MLSA 2021, Virtual Event, September 13, 2021, Revised Selected Papers. Communications in Computer and Information Science 1571, Springer 2022, ISBN 978-3-031-02043-8 [contents] - [i27]Pieter Robberechts, Wannes Meert, Jesse Davis:
Elastic Product Quantization for Time Series. CoRR abs/2201.01856 (2022) - [i26]Laurens Devos, Wannes Meert, Jesse Davis:
Adversarial Example Detection in Deployed Tree Ensembles. CoRR abs/2206.13083 (2022) - 2021
- [j17]Dries Van der Plas, Johan Verbraecken, Marc Willemen, Wannes Meert, Jesse Davis:
Evaluation of Automated Hypnogram Analysis on Multi-Scored Polysomnographies. Frontiers Digit. Health 3: 707589 (2021) - [j16]Marc Mertens, Glen Debard, Jesse Davis, Els Devriendt, Koen Milisen, Jos Tournoy, Tom Croonenborghs, Bart Vanrumste:
Motion Sensor-Based Detection of Outlier Days Supporting Continuous Health Assessment for Single Older Adults. Sensors 21(18): 6080 (2021) - [c89]Kilian Hendrickx, Wannes Meert, Bram Cornelis, Jesse Davis:
Know Your Limits: Machine Learning with Rejection for Vehicle Engineering. ADMA 2021: 273-288 - [c88]Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc De Raedt, Walter Daelemans:
Mapping probability word problems to executable representations. EMNLP (1) 2021: 3627-3640 - [c87]Laurens Devos, Wannes Meert, Jesse Davis:
Versatile Verification of Tree Ensembles. ICML 2021: 2654-2664 - [c86]Pieter Robberechts, Jan Van Haaren, Jesse Davis:
A Bayesian Approach to In-Game Win Probability in Soccer. KDD 2021: 3512-3521 - [c85]Laurens Devos, Wannes Meert, Jesse Davis:
Verifying Tree Ensembles by Reasoning about Potential Instances. SDM 2021: 450-458 - [i25]Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, Jesse Davis:
Leaving Goals on the Pitch: Evaluating Decision Making in Soccer. CoRR abs/2104.03252 (2021) - [i24]Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis:
Machine Learning with a Reject Option: A survey. CoRR abs/2107.11277 (2021) - [i23]Ulf Brefeld, Jesse Davis, Martin Lames, James J. Little:
Machine Learning in Sports (Dagstuhl Seminar 21411). Dagstuhl Reports 11(9): 45-63 (2021) - 2020
- [j15]Jessa Bekker, Jesse Davis:
Learning from positive and unlabeled data: a survey. Mach. Learn. 109(4): 719-760 (2020) - [j14]Jill Emmerzaal, Arne De Brabandere, Yves Vanrompay, Julie Vranken, Valerie Storms, Liesbet De Baets, Kristoff Corten, Jesse Davis, Ilse Jonkers, Benedicte Vanwanseele, Annick Timmermans:
Towards the Monitoring of Functional Status in a Free-Living Environment for People with Hip or Knee Osteoarthritis: Design and Evaluation of the JOLO Blended Care App. Sensors 20(23): 6967 (2020) - [c84]Vincent Vercruyssen, Wannes Meert, Jesse Davis:
Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection. AAAI 2020: 6054-6061 - [c83]Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
Multi-directional Rule Set Learning. DS 2020: 517-532 - [c82]Martin Svatos, Steven Schockaert, Jesse Davis, Ondrej Kuzelka:
STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment. ECAI 2020: 1515-1522 - [c81]Lorenzo Perini, Vincent Vercruyssen, Jesse Davis:
Class Prior Estimation in Active Positive and Unlabeled Learning. IJCAI 2020: 2915-2921 - [c80]Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis:
VAEP: An Objective Approach to Valuing On-the-Ball Actions in Soccer (Extended Abstract). IJCAI 2020: 4696-4700 - [c79]Pieter Robberechts, Jesse Davis:
How Data Availability Affects the Ability to Learn Good xG Models. MLSA@PKDD/ECML 2020: 17-27 - [c78]Lorenzo Perini, Vincent Vercruyssen, Jesse Davis:
Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions. ECML/PKDD (3) 2020: 227-243 - [c77]Tom Decroos, Maaike Van Roy, Jesse Davis:
SoccerMix: Representing Soccer Actions with Mixture Models. ECML/PKDD (5) 2020: 459-474 - [c76]Vincent Vercruyssen, Wannes Meert, Jesse Davis:
"Now you see it, now you don't!" Detecting Suspicious Pattern Absences in Continuous Time Series. SDM 2020: 127-135 - [e8]Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Machine Learning and Data Mining for Sports Analytics - 7th International Workshop, MLSA 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings. Communications in Computer and Information Science 1324, Springer 2020, ISBN 978-3-030-64911-1 [contents] - [e7]Jesse Davis, Karim Tabia:
Scalable Uncertainty Management - 14th International Conference, SUM 2020, Bozen-Bolzano, Italy, September 23-25, 2020, Proceedings. Lecture Notes in Computer Science 12322, Springer 2020, ISBN 978-3-030-58448-1 [contents] - [d1]Wannes Meert, Kilian Hendrickx, Toon van Craenendonck, Pieter Robberechts, Hendrik Blockeel, Jesse Davis:
DTAIDistance. Zenodo, 2020
- [i22]Laurens Devos, Wannes Meert, Jesse Davis:
Additive Tree Ensembles: Reasoning About Potential Instances. CoRR abs/2001.11905 (2020) - [i21]Natasa Sarafijanovic-Djukic, Jesse Davis:
Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder. CoRR abs/2003.08731 (2020) - [i20]Laurens Devos, Wannes Meert, Jesse Davis:
Versatile Verification of Tree Ensembles. CoRR abs/2010.13880 (2020)
2010 – 2019
- 2019
- [j13]Daniel Berrar, Philippe Lopes, Jesse Davis, Werner Dubitzky:
Guest editorial: special issue on machine learning for soccer. Mach. Learn. 108(1): 1-7 (2019) - [j12]Werner Dubitzky, Philippe Lopes, Jesse Davis, Daniel Berrar:
The Open International Soccer Database for machine learning. Mach. Learn. 108(1): 9-28 (2019) - [c75]Natasa Sarafijanovic-Djukic, Jesse Davis:
Fast Distance-Based Anomaly Detection in Images Using an Inception-Like Autoencoder. DS 2019: 493-508 - [c74]Marc Mertens, Julie Raepsaet, Glen Debard, Mieke Mondelaers, Bart Vanrumste, Jesse Davis:
Use of wearable technology to quantify fall risk in psychogeriatric environments: a feasability study. EMBC 2019: 3187-3190 - [c73]Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
LazyBum: Decision Tree Learning Using Lazy Propositionalization. ILP 2019: 98-113 - [c72]Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis:
Actions Speak Louder than Goals: Valuing Player Actions in Soccer. KDD 2019: 1851-1861 - [c71]Jessa Bekker, Pieter Robberechts, Jesse Davis:
Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data. ECML/PKDD (2) 2019: 71-85 - [c70]Kenneth Verstraete, Tom Decroos, Bruno Coussement, Nick Vannieuwenhoven, Jesse Davis:
Analyzing Soccer Players' Skill Ratings Over Time Using Tensor-Based Methods. PKDD/ECML Workshops (2) 2019: 225-234 - [c69]Tom Decroos, Jesse Davis:
Player Vectors: Characterizing Soccer Players' Playing Style from Match Event Streams. ECML/PKDD (3) 2019: 569-584 - [c68]Laurens Devos, Wannes Meert, Jesse Davis:
Fast Gradient Boosting Decision Trees with Bit-Level Data Structures. ECML/PKDD (1) 2019: 590-606 - [c67]Ondrej Kuzelka, Jesse Davis:
Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption. UAI 2019: 1138-1148 - [e6]Ulf Brefeld, Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Machine Learning and Data Mining for Sports Analytics - 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Proceedings. Lecture Notes in Computer Science 11330, Springer 2019, ISBN 978-3-030-17273-2 [contents] - [i19]Pieter Robberechts, Jan Van Haaren, Jesse Davis:
Who Will Win It? An In-game Win Probability Model for Football. CoRR abs/1906.05029 (2019) - [i18]Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
LazyBum: Decision tree learning using lazy propositionalization. CoRR abs/1909.05044 (2019) - [i17]Pieter Robberechts, Rud Derie, Pieter Van den Berghe, Joeri Gerlo, Dirk De Clercq, Veerle Segers, Jesse Davis:
Gait Event Detection in Tibial Acceleration Profiles: a Structured Learning Approach. CoRR abs/1910.13372 (2019) - [i16]Kilian Hendrickx, Wannes Meert, Yves Mollet, Johan Gyselinck, Bram Cornelis, Konstantinos C. Gryllias, Jesse Davis:
A general anomaly detection framework for fleet-based condition monitoring of machines. CoRR abs/1912.12941 (2019) - 2018
- [j11]Irma Ravkic, Martin Znidarsic, Jan Ramon, Jesse Davis:
Graph sampling with applications to estimating the number of pattern embeddings and the parameters of a statistical relational model. Data Min. Knowl. Discov. 32(4): 913-948 (2018) - [j10]Derek Greene, Björn Bringmann, Élisa Fromont, Jesse Davis:
Introduction to the special issue for the ECML PKDD 2018 journal track. Data Min. Knowl. Discov. 32(5): 1177-1178 (2018) - [j9]Jesse Davis, Björn Bringmann, Élisa Fromont, Derek Greene:
Guest editors introduction to the special issue for the ECML PKDD 2018 journal track. Mach. Learn. 107(8-10): 1207-1208 (2018) - [c66]Jessa Bekker, Jesse Davis:
Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction. AAAI 2018: 2712-2719 - [c65]Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
Relational Marginal Problems: Theory and Estimation. AAAI 2018: 6384-6391 - [c64]Vincent Vercruyssen, Wannes Meert, Gust Verbruggen, Koen Maes, Ruben Baumer, Jesse Davis:
Semi-Supervised Anomaly Detection with an Application to Water Analytics. ICDM 2018: 527-536 - [c63]Tom Decroos, Jan Van Haaren, Jesse Davis:
Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. KDD 2018: 223-232 - [c62]Tim Op De Beéck, Wannes Meert, Kurt Schütte, Benedicte Vanwanseele, Jesse Davis:
Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. KDD 2018: 606-615 - [c61]Jessa Bekker, Jesse Davis:
Learning from Positive and Unlabeled Data under the Selected At Random Assumption. LIDTA@ECML/PKDD 2018: 8-22 - [c60]Pieter Robberechts, Jesse Davis:
Forecasting the FIFA World Cup - Combining Result- and Goal-Based Team Ability Parameters. MLSA@PKDD/ECML 2018: 16-30 - [c59]Pieter Robberechts, Maarten Bosteels, Jesse Davis, Wannes Meert:
Query Log Analysis: Detecting Anomalies in DNS Traffic at a TLD Resolver. DMLE/IOTSTREAMING@PKDD/ECML 2018: 55-67 - [c58]Tom Decroos, Kurt Schütte, Tim Op De Beéck, Benedicte Vanwanseele, Jesse Davis:
AMIE: Automatic Monitoring of Indoor Exercises. ECML/PKDD (3) 2018: 424-439 - [c57]Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
PAC-Reasoning in Relational Domains. UAI 2018: 927-936 - [c56]Kaja Zupanc, Jesse Davis:
Estimating Rule Quality for Knowledge Base Completion with the Relationship between Coverage Assumption. WWW 2018: 1073-1081 - [i15]Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
PAC-Reasoning in Relational Domains. CoRR abs/1803.05768 (2018) - [i14]Jessa Bekker, Jesse Davis:
Learning from Positive and Unlabeled Data under the Selected At Random Assumption. CoRR abs/1808.08755 (2018) - [i13]Jessa Bekker, Jesse Davis:
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data. CoRR abs/1809.03207 (2018) - [i12]Jessa Bekker, Jesse Davis:
Learning From Positive and Unlabeled Data: A Survey. CoRR abs/1811.04820 (2018) - 2017
- [c55]Tom Decroos, Vladimir Dzyuba, Jan Van Haaren, Jesse Davis:
Predicting Soccer Highlights from Spatio-Temporal Match Event Streams. AAAI 2017: 1302-1308 - [c54]