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Jesse 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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Journal Articles

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2024
[j22]
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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)
2023
[j21]
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  - journals/jair/TotisDRK23
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Pietro Totis, Jesse Davis, Luc De Raedt, Angelika Kimmig:
Lifted Reasoning for Combinatorial Counting. J. Artif. Intell. Res. 76: 1-58 (2023)
[j20]
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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)
2022
[j19]
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  - journals/sensors/BogaertDRV22
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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]
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  - journals/sensors/EmmerzaalBSBBDJ22
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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)
2021
[j17]
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  - journals/fdgth/PlasVWMD21
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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]
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  - journals/sensors/MertensDDDMTCV21
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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)
2020
[j15]
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Jessa Bekker, Jesse Davis:
Learning from positive and unlabeled data: a survey. Mach. Learn. 109(4): 719-760 (2020)
[j14]
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  - journals/sensors/EmmerzaalBVVSBC20
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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)
2019
[j13]
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  - journals/ml/BerrarLDD19
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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]
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Werner Dubitzky, Philippe Lopes, Jesse Davis, Daniel Berrar:
The Open International Soccer Database for machine learning. Mach. Learn. 108(1): 9-28 (2019)
2018
[j11]
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  - journals/datamine/RavkicZRD18
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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]
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  - journals/datamine/GreeneBFD18
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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]
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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)
2016
[j8]
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  - journals/ml/HaarenBMD16
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Jan Van Haaren, Guy Van den Broeck, Wannes Meert, Jesse Davis:
Lifted generative learning of Markov logic networks. Mach. Learn. 103(1): 27-55 (2016)
[j7]
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Jesse Davis, Jan Ramon:
Guest editors introduction: special issue on inductive logic programming. Mach. Learn. 103(3): 307-308 (2016)
2015
[j6]
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  - journals/bmcbi/PopovicSDMM15
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Dusan Popovic, Alejandro Sifrim, Jesse Davis, Yves Moreau, Bart De Moor:
Problems with the nested granularity of feature domains in bioinformatics: the eXtasy case. BMC Bioinform. 16(S-4): S2 (2015)
[j5]
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Irma Ravkic, Jan Ramon, Jesse Davis:
Learning relational dependency networks in hybrid domains. Mach. Learn. 100(2-3): 217-254 (2015)
2014
[j4]
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  - journals/jmlr/LowdD14
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Daniel Lowd, Jesse Davis:
Improving Markov network structure learning using decision trees. J. Mach. Learn. Res. 15(1): 501-532 (2014)
2013
[j3]
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  - journals/jair/TaghipourFDB13
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Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language. J. Artif. Intell. Res. 47: 393-439 (2013)
2011
[j2]
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  - journals/aim/DavisD11
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Jesse Davis, Pedro M. Domingos:
Deep Transfer: A Markov Logic Approach. AI Mag. 32(1): 51-53 (2011)
2005
[j1]
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  - journals/ijseke/DavisSE05
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Jesse Davis, Douglas Stark, Nicholas Gerard Edmonds:
Method Of Interaction In A Modular Architecture For Sensor Systems (Mass). Int. J. Softw. Eng. Knowl. Eng. 15(2): 419-426 (2005)

Conference and Workshop Papers

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2024
[c106]
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Laurens Devos, Lorenzo Cascioli, Jesse Davis:
Robustness Verification of Multi-Class Tree Ensembles. AAAI 2024: 21019-21028
2023
[c105]
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  - conf/kdd/PeriniVD23
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Lorenzo Perini, Vincent Vercruyssen, Jesse Davis:
Learning from Positive and Unlabeled Multi-Instance Bags in Anomaly Detection. KDD 2023: 1897-1906
[c104]
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  - conf/kdd/RobberechtsRD23
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Pieter Robberechts, Maaike Van Roy, Jesse Davis:
un-xPass: Measuring Soccer Player's Creativity. KDD 2023: 4768-4777
[c103]
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Lorenzo Perini, Jesse Davis:
Unsupervised Anomaly Detection with Rejection. NeurIPS 2023
[c102]
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  - conf/pkdd/DevosPMD23
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Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis:
Detecting Evasion Attacks in Deployed Tree Ensembles. ECML/PKDD (5) 2023: 120-136
[c101]
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  - conf/pkdd/MartensPD23
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Timo Martens, Lorenzo Perini, Jesse Davis:
Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection. ECML/PKDD (1) 2023: 219-236
[c100]
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  - conf/sdm/PiasMVD23
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Dries Van der Pias, Wannes Meert, Johan Verbraecken, Jesse Davis:
A novel reject option applied to sleep stage scoring. SDM 2023: 820-828
2022
[c99]
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Lorenzo Perini, Vincent Vercruyssen, Jesse Davis:
Transferring the Contamination Factor between Anomaly Detection Domains by Shape Similarity. AAAI 2022: 4128-4136
[c98]
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  - conf/aaai/SchouterdenBDB22
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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]
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  - conf/dis/BrabandereCVBD22
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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]
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Pieter Robberechts, Wannes Meert, Jesse Davis:
Elastic Product Quantization for Time Series. DS 2022: 157-172
[c95]
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  - conf/ijcai/DavisBDMRHR22
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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]
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  - conf/lidta/AntoniadisVD22
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Ioannis Antoniadis, Vincent Vercruyssen, Jesse Davis:
Systematic Evaluation of CASH Search Strategies for Unsupervised Anomaly Detection. LIDTA 2022: 8-22
[c93]
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  - conf/pkdd/DierckxDS22
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Thomas Dierckx, Jesse Davis, Wim Schoutens:
Towards Data-Driven Volatility Modeling with Variational Autoencoders. PKDD/ECML Workshops (2) 2022: 97-111
[c92]
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Loren Nuyts, Laurens Devos, Wannes Meert, Jesse Davis:
Bitpaths: Compressing Datasets Without Decreasing Predictive Performance. PKDD/ECML Workshops (1) 2022: 261-268
[c91]
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  - conf/pkdd/ClijmansRD22
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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]
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Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, Jesse Davis:
Multi-domain Active Learning for Semi-supervised Anomaly Detection. ECML/PKDD (4) 2022: 485-501
2021
[c89]
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  - conf/adma/HendrickxMCD22
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Kilian Hendrickx, Wannes Meert, Bram Cornelis, Jesse Davis:
Know Your Limits: Machine Learning with Rejection for Vehicle Engineering. ADMA 2021: 273-288
[c88]
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  - conf/emnlp/SusterFTKDRD21
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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]
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Laurens Devos, Wannes Meert, Jesse Davis:
Versatile Verification of Tree Ensembles. ICML 2021: 2654-2664
[c86]
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  - conf/kdd/RobberechtsHD21
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Pieter Robberechts, Jan Van Haaren, Jesse Davis:
A Bayesian Approach to In-Game Win Probability in Soccer. KDD 2021: 3512-3521
[c85]
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Laurens Devos, Wannes Meert, Jesse Davis:
Verifying Tree Ensembles by Reasoning about Potential Instances. SDM 2021: 450-458
2020
[c84]
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Vincent Vercruyssen, Wannes Meert, Jesse Davis:
Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection. AAAI 2020: 6054-6061
[c83]
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  - conf/dis/SchouterdenDB20
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Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
Multi-directional Rule Set Learning. DS 2020: 517-532
[c82]
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  - conf/ecai/SvatosSDK20
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Martin Svatos, Steven Schockaert, Jesse Davis, Ondrej Kuzelka:
STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment. ECAI 2020: 1515-1522
[c81]
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Lorenzo Perini, Vincent Vercruyssen, Jesse Davis:
Class Prior Estimation in Active Positive and Unlabeled Learning. IJCAI 2020: 2915-2921
[c80]
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  - conf/ijcai/DecroosBHD20
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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]
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Pieter Robberechts, Jesse Davis:
How Data Availability Affects the Ability to Learn Good xG Models. MLSA@PKDD/ECML 2020: 17-27
[c78]
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Lorenzo Perini, Vincent Vercruyssen, Jesse Davis:
Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions. ECML/PKDD (3) 2020: 227-243
[c77]
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Tom Decroos, Maaike Van Roy, Jesse Davis:
SoccerMix: Representing Soccer Actions with Mixture Models. ECML/PKDD (5) 2020: 459-474
[c76]
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  - conf/sdm/VercruyssenMD20
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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
2019
[c75]
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  - conf/dis/Sarafijanovic-Djukic19
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Natasa Sarafijanovic-Djukic, Jesse Davis:
Fast Distance-Based Anomaly Detection in Images Using an Inception-Like Autoencoder. DS 2019: 493-508
[c74]
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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
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Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
LazyBum: Decision Tree Learning Using Lazy Propositionalization. ILP 2019: 98-113
[c72]
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Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis:
Actions Speak Louder than Goals: Valuing Player Actions in Soccer. KDD 2019: 1851-1861
[c71]
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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]
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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]
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Tom Decroos, Jesse Davis:
Player Vectors: Characterizing Soccer Players' Playing Style from Match Event Streams. ECML/PKDD (3) 2019: 569-584
[c68]
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Laurens Devos, Wannes Meert, Jesse Davis:
Fast Gradient Boosting Decision Trees with Bit-Level Data Structures. ECML/PKDD (1) 2019: 590-606
[c67]
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Ondrej Kuzelka, Jesse Davis:
Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption. UAI 2019: 1138-1148
2018
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Jessa Bekker, Jesse Davis:
Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction. AAAI 2018: 2712-2719
[c65]
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Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
Relational Marginal Problems: Theory and Estimation. AAAI 2018: 6384-6391
[c64]
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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]
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Tom Decroos, Jan Van Haaren, Jesse Davis:
Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. KDD 2018: 223-232
[c62]
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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]
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Jessa Bekker, Jesse Davis:
Learning from Positive and Unlabeled Data under the Selected At Random Assumption. LIDTA@ECML/PKDD 2018: 8-22
[c60]
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Pieter Robberechts, Jesse Davis:
Forecasting the FIFA World Cup - Combining Result- and Goal-Based Team Ability Parameters. MLSA@PKDD/ECML 2018: 16-30
[c59]
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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]
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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]
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Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
PAC-Reasoning in Relational Domains. UAI 2018: 927-936
[c56]
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Kaja Zupanc, Jesse Davis:
Estimating Rule Quality for Knowledge Base Completion with the Relationship between Coverage Assumption. WWW 2018: 1073-1081
2017
[c55]
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Tom Decroos, Vladimir Dzyuba, Jan Van Haaren, Jesse Davis:
Predicting Soccer Highlights from Spatio-Temporal Match Event Streams. AAAI 2017: 1302-1308
[c54]
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Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Induction of Interpretable Possibilistic Logic Theories from Relational Data. IJCAI 2017: 1153-1159
[c53]
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Anton Dries, Angelika Kimmig, Jesse Davis, Vaishak Belle, Luc De Raedt:
Solving Probability Problems in Natural Language. IJCAI 2017: 3981-3987
[c52]
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Jessa Bekker, Jesse Davis:
Positive and Unlabeled Relational Classification Through Label Frequency Estimation. ILP 2017: 16-30
[c51]
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Ruben Vroonen, Tom Decroos, Jan Van Haaren, Jesse Davis:
Predicting the Potential of Professional Soccer Players. MLSA@PKDD/ECML 2017: 1-10
[c50]
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Tom Decroos, Jan Van Haaren, Vladimir Dzyuba, Jesse Davis:
STARSS: A Spatio-Temporal Action Rating System for Soccer. MLSA@PKDD/ECML 2017: 11-20
[c49]
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Vincent Vercruyssen, Wannes Meert, Jesse Davis:
Transfer Learning for Time Series Anomaly Detection. IAL@PKDD/ECML 2017: 27-36
2016
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Sarah ElShal, Mithila Mathad, Jaak Simm, Jesse Davis, Yves Moreau:
Topic modeling of biomedical text. BIBM 2016: 712-716
[c47]
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Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Interpretable Encoding of Densities Using Possibilistic Logic. ECAI 2016: 1239-1247
[c46]
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Davide Nitti, Irma Ravkic, Jesse Davis, Luc De Raedt:
Learning the Structure of Dynamic Hybrid Relational Models. ECAI 2016: 1283-1290
[c45]
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Cheryl A. Bodnar, Renee M. Clark, Jesse Davis, Tom Congedo, Daniel Cole:
Student perspectives on application of game-based learning within a graduate-level engineering course. FIE 2016: 1-5
[c44]
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Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Learning Possibilistic Logic Theories from Default Rules. IJCAI 2016: 1167-1173
[c43]
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Aäron Verachtert, Hendrik Blockeel, Jesse Davis:
Dynamic Early Stopping for Naive Bayes. IJCAI 2016: 2082-2088
[c42]
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Sarah ElShal, Jaak Simm, Adam Arany, Pooya Zakeri, Jesse Davis, Yves Moreau:
A Comprehensive Comparison of Two MEDLINE Annotators for Disease and Gene Linkage: Sometimes Less is More. IWBBIO 2016: 765-778
[c41]
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Jan Van Haaren, Horesh Ben Shitrit, Jesse Davis, Pascal Fua:
Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques. KDD 2016: 627-634
[c40]
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Vincent Vercruyssen, Luc De Raedt, Jesse Davis:
Qualitative Spatial Reasoning for Soccer Pass Prediction. MLSA@PKDD/ECML 2016
2015
[c39]
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Jan Van Haaren, Andrey Kolobov, Jesse Davis:
TODTLER: Two-Order-Deep Transfer Learning. AAAI 2015: 3007-3015
[c38]
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Tim Op De Beéck, Arjen Hommersom, Jan Van Haaren, Maarten van der Heijden, Jesse Davis, Peter J. F. Lucas, Lucy Overbeek, Iris Nagtegaal:
Mining Hierarchical Pathology Data Using Inductive Logic Programming. AIME 2015: 76-85
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Jan Van Haaren, Vladimir Dzyuba, Siebe Hannosset, Jesse Davis:
Automatically Discovering Offensive Patterns in Soccer Match Data. IDA 2015: 286-297
[c36]
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Daniele Alfarone, Jesse Davis:
Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus. IJCAI 2015: 1434-1441
[c35]
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Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Constructing Markov Logic Networks from First-Order Default Rules. ILP 2015: 91-105
[c34]
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Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, Guy Van den Broeck:
Tractable Learning for Complex Probability Queries. NIPS 2015: 2242-2250
[c33]
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Dusan Popovic, Jesse Davis, Alejandro Sifrim, Bart De Moor:
A Note on the Evaluation of Mutation Prioritization Algorithms. SSCI 2015: 1351-1357
[c32]
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Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Encoding Markov logic networks in Possibilistic Logic. UAI 2015: 454-463
2014
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Elie Merhej, Steven Schockaert, Martine De Cock, Marjon Blondeel, Daniele Alfarone, Jesse Davis:
Repairing Inconsistent Taxonomies Using MAP Inference and Rules of Thumb. Web-KRM@CIKM 2014: 31-36
[c30]
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Wouter Bancken, Daniele Alfarone, Jesse Davis:
Automatically Detecting and Rating Product Aspects from Textual Customer Reviews. DMNLP@PKDD/ECML 2014: 1-16
2013
[c29]
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Guy Van den Broeck, Wannes Meert, Jesse Davis:
Lifted Generative Parameter Learning. StarAI@AAAI 2013
[c28]
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Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
On the Completeness of Lifted Variable Elimination. StarAI@AAAI 2013
[c27]
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Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
Completeness Results for Lifted Variable Elimination. AISTATS 2013: 572-580
[c26]
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Bogdan Moldovan, Ingo Thon, Jesse Davis, Luc De Raedt:
MCMC Estimation of Conditional Probabilities in Probabilistic Programming Languages. ECSQARU 2013: 436-448
[c25]
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Nima Taghipour, Jesse Davis, Hendrik Blockeel:
Generalized Counting for Lifted Variable Elimination. ILP 2013: 107-122
[c24]
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Nima Taghipour, Jesse Davis, Hendrik Blockeel:
First-order Decomposition Trees. NIPS 2013: 1052-1060
2012
[c23]
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Jan Van Haaren, Jesse Davis:
Markov Network Structure Learning: A Randomized Feature Generation Approach. AAAI 2012: 1148-1154
[c22]
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Guy Van den Broeck, Jesse Davis:
Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference. AAAI 2012: 1961-1967
[c21]
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Mathias Verbeke, Jesse Davis:
A Text Mining Approach as Baseline for QA4MRE'12. CLEF (Online Working Notes/Labs/Workshop) 2012
[c20]
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Kendrick Boyd, Jesse Davis, David Page, Vítor Santos Costa:
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation. ICML 2012
[c19]
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Jesse Davis, Vítor Santos Costa, Elizabeth Berg, David Page, Peggy L. Peissig, Michael Caldwell:
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events. ICML 2012
[c18]
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Daan Fierens, Kristian Kersting, Jesse Davis, Jian Chen, Martin Mladenov:
Pairwise Markov Logic. ILP 2012: 58-73
[c17]
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Irma Ravkic, Jan Ramon, Jesse Davis:
Hybrid Logical Bayesian Networks. ILP (Late Breaking Papers) 2012: 62-67
[c16]
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Nima Taghipour, Jesse Davis:
Generalized Counting for Lifted Variable Elimination. StarAI@UAI 2012
[c15]
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Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination with Arbitrary Constraints. AISTATS 2012: 1194-1202
2011
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Guy Van den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis, Luc De Raedt:
Lifted Probabilistic Inference by First-Order Knowledge Compilation. IJCAI 2011: 2178-2185
2010
[c13]
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Stefan Schoenmackers, Jesse Davis, Oren Etzioni, Daniel S. Weld:
Learning First-Order Horn Clauses from Web Text. EMNLP 2010: 1088-1098
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Daniel Lowd, Jesse Davis:
Learning Markov Network Structure with Decision Trees. ICDM 2010: 334-343
[c11]
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Jesse Davis, Pedro M. Domingos:
Bottom-Up Learning of Markov Network Structure. ICML 2010: 271-278
2009
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Jesse Davis, Pedro M. Domingos:
Deep transfer via second-order Markov logic. ICML 2009: 217-224
2007
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Jesse Davis, Vítor Santos Costa, Soumya Ray, David Page:
An integrated approach to feature invention and model construction for drug activity prediction. ICML 2007: 217-224
[c8]
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Jesse Davis, Irene M. Ong, Jan Struyf, Elizabeth S. Burnside, David Page, Vítor Santos Costa:
Change of Representation for Statistical Relational Learning. IJCAI 2007: 2719-2726
2006
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Jan Struyf, Jesse Davis, C. David Page Jr.:
An Efficient Approximation to Lookahead in Relational Learners. ECML 2006: 775-782
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Jesse Davis, Mark Goadrich:
The relationship between Precision-Recall and ROC curves. ICML 2006: 233-240
2005
[c5]
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Elizabeth S. Burnside, Jesse Davis, Vítor Santos Costa, Inês de Castro Dutra, Charles E. Kahn Jr., Jason Fine, David Page:
Knowledge Discovery from Structured Mammography Reports Using Inductive Logic Programming. AMIA 2005
[c4]
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Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Vítor Santos Costa:
An Integrated Approach to Learning Bayesian Networks of Rules. ECML 2005: 84-95
[c3]
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Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Raghu Ramakrishnan, Vítor Santos Costa, Jude W. Shavlik:
View Learning for Statistical Relational Learning: With an Application to Mammography. IJCAI 2005: 677-683
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Nicholas Gerard Edmonds, Douglas Stark, Jesse Davis:
Mass: modular architecture for sensor systems. IPSN 2005: 393-397
2004
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Douglas Stark, Jesse Davis:
Friendly Object Tracking and Foreign Object Detection and Localization with an SDAC Wireless Sensor Network. FTDCS 2004: 30-36

Parts in Books or Collections

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2015
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Jesse Davis, Luis Enrique Sucar, Felipe Orihuela-Espina:
Treatment of Disease: The Role of Knowledge Representation for Treatment Selection. Foundations of Biomedical Knowledge Representation 2015: 235-241
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Jesse Davis, Vítor Santos Costa, Peggy L. Peissig, Michael Caldwell, David Page:
Predicting Adverse Drug Events from Electronic Medical Records. Foundations of Biomedical Knowledge Representation 2015: 243-257

Editorship

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2023
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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]
2022
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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]
2020
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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]
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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]
2019
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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]
2017
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Jan Van Haaren, Mehdi Kaytoue, Jesse Davis:
Proceedings of the Workshop on Machine Learning and Data Mining for Sports Analytics 2016 co-located with the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, MLSA@PKDD/ECML 2016, Riva del Garda, Italy, September 19, 2016. CEUR Workshop Proceedings 1842, CEUR-WS.org 2017 [contents]
[e4]
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Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics co-located with 2015 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Porto, Portugal, September 11th, 2015. CEUR Workshop Proceedings 1970, CEUR-WS.org 2017 [contents]
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Jesse Davis, Mehdi Kaytoue, Albrecht Zimmermann:
Proceedings of the 4th Workshop on Machine Learning and Data Mining for Sports Analytics co-located with 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017), Skopje, Macedonia, September 18th, 2017. CEUR Workshop Proceedings 1971, CEUR-WS.org 2017 [contents]
2015
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Jesse Davis, Jan Ramon:
Inductive Logic Programming - 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers. Lecture Notes in Computer Science 9046, Springer 2015, ISBN 978-3-319-23707-7 [contents]
2013
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Jesse Davis, Jan Van Haaren, Albrecht Zimmermann:
Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics co-located with 2013 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2013), Prague, Czech Republic, September 27th, 2013. CEUR Workshop Proceedings 1969, CEUR-WS.org 2013 [contents]

Informal and Other Publications

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2024
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Jesse Davis, Pieter Robberechts:
Biases in Expected Goals Models Confound Finishing Ability. CoRR abs/2401.09940 (2024)
[i32]
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Andrea Pugnana, Lorenzo Perini, Jesse Davis, Salvatore Ruggieri:
Deep Neural Network Benchmarks for Selective Classification. CoRR abs/2401.12708 (2024)
[i31]
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  - journals/corr/abs-2402-08586
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Lorenzo Cascioli, Laurens Devos, Ondrej Kuzelka, Jesse Davis:
Faster Repeated Evasion Attacks in Tree Ensembles. CoRR abs/2402.08586 (2024)
2023
[i30]
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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]
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  - journals/corr/abs-2305-13189
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Lorenzo Perini, Jesse Davis:
Unsupervised Anomaly Detection with Rejection. CoRR abs/2305.13189 (2023)
[i28]
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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
[i27]
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Pieter Robberechts, Wannes Meert, Jesse Davis:
Elastic Product Quantization for Time Series. CoRR abs/2201.01856 (2022)
[i26]
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  - journals/corr/abs-2206-13083
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Laurens Devos, Wannes Meert, Jesse Davis:
Adversarial Example Detection in Deployed Tree Ensembles. CoRR abs/2206.13083 (2022)
2021
[i25]
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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]
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  - journals/corr/abs-2107-11277
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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]
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Ulf Brefeld, Jesse Davis, Martin Lames, James J. Little:
Machine Learning in Sports (Dagstuhl Seminar 21411). Dagstuhl Reports 11(9): 45-63 (2021)
2020
[i22]
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  - journals/corr/abs-2001-11905
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Laurens Devos, Wannes Meert, Jesse Davis:
Additive Tree Ensembles: Reasoning About Potential Instances. CoRR abs/2001.11905 (2020)
[i21]
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Natasa Sarafijanovic-Djukic, Jesse Davis:
Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder. CoRR abs/2003.08731 (2020)
[i20]
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Laurens Devos, Wannes Meert, Jesse Davis:
Versatile Verification of Tree Ensembles. CoRR abs/2010.13880 (2020)
2019
[i19]
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  - journals/corr/abs-1906-05029
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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]
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  - journals/corr/abs-1909-05044
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Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
LazyBum: Decision tree learning using lazy propositionalization. CoRR abs/1909.05044 (2019)
[i17]
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  - journals/corr/abs-1910-13372
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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]
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  - journals/corr/abs-1912-12941
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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
[i15]
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Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
PAC-Reasoning in Relational Domains. CoRR abs/1803.05768 (2018)
[i14]
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  - journals/corr/abs-1808-08755
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Jessa Bekker, Jesse Davis:
Learning from Positive and Unlabeled Data under the Selected At Random Assumption. CoRR abs/1808.08755 (2018)
[i13]
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  - journals/corr/abs-1809-03207
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Jessa Bekker, Jesse Davis:
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data. CoRR abs/1809.03207 (2018)
[i12]
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  - journals/corr/abs-1811-04820
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Jessa Bekker, Jesse Davis:
Learning From Positive and Unlabeled Data: A Survey. CoRR abs/1811.04820 (2018)
2017
[i11]
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Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Induction of Interpretable Possibilistic Logic Theories from Relational Data. CoRR abs/1705.07095 (2017)
[i10]
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  - journals/corr/abs-1709-05825
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Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert:
Relational Marginal Problems: Theory and Estimation. CoRR abs/1709.05825 (2017)
2016
[i9]
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Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Learning Possibilistic Logic Theories from Default Rules. CoRR abs/1604.05273 (2016)
[i8]
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Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract). CoRR abs/1611.06174 (2016)
[i7]
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  dblp key:
  - journals/corr/BekkerHLD16
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Jessa Bekker, Arjen Hommersom, Martijn Lappenschaar, Jesse Davis:
Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks. CoRR abs/1612.03055 (2016)
2015
[i6]
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  - journals/corr/ClaesenDSM15
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Marc Claesen, Jesse Davis, Frank De Smet, Bart De Moor:
Assessing binary classifiers using only positive and unlabeled data. CoRR abs/1504.06837 (2015)
[i5]
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Ondrej Kuzelka, Jesse Davis, Steven Schockaert:
Encoding Markov Logic Networks in Possibilistic Logic. CoRR abs/1506.01432 (2015)
2014
[i4]
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  - journals/corr/TaghipourFDB14
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Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language. CoRR abs/1402.0565 (2014)
2013
[i3]
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  - journals/corr/TaghipourDB13
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Nima Taghipour, Jesse Davis, Hendrik Blockeel:
First-Order Decomposition Trees. CoRR abs/1306.0751 (2013)
2012
[i2]
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  - journals/corr/abs-1206-4667
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Kendrick Boyd, Vítor Santos Costa, Jesse Davis, David Page:
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation. CoRR abs/1206.4667 (2012)
[i1]
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  - journals/corr/abs-1208-3809
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Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination: A Novel Operator and Completeness Results. CoRR abs/1208.3809 (2012)

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