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Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder

Mar 12, 2020
Natasa Sarafijanovic-Djukic, Jesse Davis

* InInternational Conference on Discovery Science 2019 Oct 28 (pp. 493-508). Springer, Cham 
* 22nd International Conference on Discovery Science, DS 2019 

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Additive Tree Ensembles: Reasoning About Potential Instances

Jan 31, 2020
Laurens Devos, Wannes Meert, Jesse Davis


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A general anomaly detection framework for fleet-based condition monitoring of machines

Jan 07, 2020
Kilian Hendrickx, Wannes Meert, Yves Mollet, Johan Gyselinck, Bram Cornelis, Konstantinos Gryllias, Jesse Davis

* Accepted in Mechanical Systems and Signal Processing, SI: Machine Diagnostics by AI 

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Gait Event Detection in Tibial Acceleration Profiles: a Structured Learning Approach

Oct 29, 2019
Pieter Robberechts, Rud Derie, Pieter Van den Berghe, Joeri Gerlo, Dirk De Clercq, Veerle Segers, Jesse Davis


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LazyBum: Decision tree learning using lazy propositionalization

Sep 11, 2019
Jonas Schouterden, Jesse Davis, Hendrik Blockeel

* 15 pages, 5 figures 

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Who Will Win It? An In-game Win Probability Model for Football

Jun 12, 2019
Pieter Robberechts, Jan Van Haaren, Jesse Davis


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Learning From Positive and Unlabeled Data: A Survey

Nov 12, 2018
Jessa Bekker, Jesse Davis


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Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data

Sep 10, 2018
Jessa Bekker, Jesse Davis


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Learning from Positive and Unlabeled Data under the Selected At Random Assumption

Aug 27, 2018
Jessa Bekker, Jesse Davis


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PAC-Reasoning in Relational Domains

Jul 04, 2018
Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert

* Longer version of paper appearing in UAI 2018 

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Relational Marginal Problems: Theory and Estimation

Apr 25, 2018
Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert

* Long version of a paper that appeared in AAAI 2018; added a paragraph to Related Work 

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Actions Speak Louder Than Goals: Valuing Player Actions in Soccer

Feb 18, 2018
Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis


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Induction of Interpretable Possibilistic Logic Theories from Relational Data

May 19, 2017
Ondrej Kuzelka, Jesse Davis, Steven Schockaert

* Longer version of a paper appearing in IJCAI 2017 

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Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks

Dec 09, 2016
Jessa Bekker, Arjen Hommersom, Martijn Lappenschaar, Jesse Davis

* Machine Learning for Health @ NIPS 2016 

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Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)

Nov 18, 2016
Ondrej Kuzelka, Jesse Davis, Steven Schockaert

* Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems 

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Learning Possibilistic Logic Theories from Default Rules

Apr 18, 2016
Ondrej Kuzelka, Jesse Davis, Steven Schockaert

* Long version of a paper accepted at IJCAI 2016 

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Assessing binary classifiers using only positive and unlabeled data

Dec 30, 2015
Marc Claesen, Jesse Davis, Frank De Smet, Bart De Moor

* 14 pages, 8 figures 

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Encoding Markov Logic Networks in Possibilistic Logic

Jun 08, 2015
Ondrej Kuzelka, Jesse Davis, Steven Schockaert

* Extended version of a paper appearing in UAI 2015 

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Lifted Variable Elimination: Decoupling the Operators from the Constraint Language

Feb 04, 2014
Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel

* Journal Of Artificial Intelligence Research, Volume 47, pages 393-439, 2013 

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First-Order Decomposition Trees

Jun 04, 2013
Nima Taghipour, Jesse Davis, Hendrik Blockeel


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Lifted Variable Elimination: A Novel Operator and Completeness Results

Aug 24, 2012
Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel


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Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation

Jul 18, 2012
Kendrick Boyd, Vitor Santos Costa, Jesse Davis, David Page

* ICML2012, fixed citations to use correct tech report number 

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Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events

Jun 27, 2012
Jesse Davis, Vitor Santos Costa, Peggy Peissig, Michael Caldwell, Elizabeth Berg, David Page

* Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012) 

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