Davis, Jesse

63 publications

TMLR 2026 Dealing with Uncertainty in Contextual Anomaly Detection Luca Bindini, Lorenzo Perini, Stefano Nistri, Jesse Davis, Paolo Frasconi

MLJ 2026 Temporal Graph Network Framework for Quantifying Pass Reception Probabilities Against Defensive Structures Pegah Rahimian, Jesse Davis, László Toka

ICML 2025 Compressing Tree Ensembles Through Level-Wise Optimization and Pruning Laurens Devos, Timo Martens, Deniz Can Oruc, Wannes Meert, Hendrik Blockeel, Jesse Davis

AISTATS 2025 Learning from Biased Positive-Unlabeled Data via Threshold Calibration Paweł Teisseyre, Timo Martens, Jessa Bekker, Jesse Davis

DMLR 2024 Deep Neural Network Benchmarks for Selective Classification Andrea Pugnana, Lorenzo Perini, Jesse Davis, Salvatore Ruggieri

NeurIPS 2024 Faster Repeated Evasion Attacks in Tree Ensembles Lorenzo Cascioli, Laurens Devos, Ondrej Kuzelka, Jesse Davis

MLJ 2024 Machine Learning with a Reject Option: A Survey Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas, Wannes Meert, Jesse Davis

MLJ 2024 Methodology and Evaluation in Sports Analytics: Challenges, Approaches, and Lessons Learned Jesse Davis, Lotte Bransen, Laurens Devos, Arne Jaspers, Wannes Meert, Pieter Robberechts, Jan Van Haaren, Maaike Van Roy

AAAI 2024 Robustness Verification of Multi-Class Tree Ensembles Laurens Devos, Lorenzo Cascioli, Jesse Davis

MLJ 2024 TSFuse: Automated Feature Construction for Multiple Time Series Data Arne De Brabandere, Tim Op De Beéck, Kilian Hendrickx, Wannes Meert, Jesse Davis

JAIR 2023 A Markov Framework for Learning and Reasoning About Strategies in Professional Soccer Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, Jesse Davis

ECML-PKDD 2023 Detecting Evasion Attacks in Deployed Tree Ensembles Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis

JAIR 2023 Lifted Reasoning for Combinatorial Counting Pietro Totis, Jesse Davis, Luc De Raedt, Angelika Kimmig

ECML-PKDD 2023 Semi-Supervised Learning from Active Noisy Soft Labels for Anomaly Detection Timo Martens, Lorenzo Perini, Jesse Davis

NeurIPS 2023 Unsupervised Anomaly Detection with Rejection Lorenzo Perini, Jesse Davis

ECML-PKDD 2022 Looking Beyond the past: Analyzing the Intrinsic Playing Style of Soccer Teams Jeroen Clijmans, Maaike Van Roy, Jesse Davis

ECML-PKDD 2022 Multi-Domain Active Learning for Semi-Supervised Anomaly Detection Vincent Vercruyssen, Lorenzo Perini, Wannes Meert, Jesse Davis

AAAI 2022 Transferring the Contamination Factor Between Anomaly Detection Domains by Shape Similarity Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

AAAI 2022 Unifying Knowledge Base Completion with PU Learning to Mitigate the Observation Bias Jonas Schouterden, Jessa Bekker, Jesse Davis, Hendrik Blockeel

ICML 2021 Versatile Verification of Tree Ensembles Laurens Devos, Wannes Meert, Jesse Davis

IJCAI 2020 Class Prior Estimation in Active Positive and Unlabeled Learning Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

MLJ 2020 Learning from Positive and Unlabeled Data: A Survey Jessa Bekker, Jesse Davis

ECML-PKDD 2020 Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions Lorenzo Perini, Vincent Vercruyssen, Jesse Davis

ECML-PKDD 2020 SoccerMix: Representing Soccer Actions with Mixture Models Tom Decroos, Maaike Van Roy, Jesse Davis

AAAI 2020 Transfer Learning for Anomaly Detection Through Localized and Unsupervised Instance Selection Vincent Vercruyssen, Wannes Meert, Jesse Davis

IJCAI 2020 VAEP: An Objective Approach to Valuing On-the-Ball Actions in Soccer (Extended Abstract) Tom Decroos, Lotte Bransen, Jan Van Haaren, Jesse Davis

ECML-PKDD 2019 Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data Jessa Bekker, Pieter Robberechts, Jesse Davis

ECML-PKDD 2019 Fast Gradient Boosting Decision Trees with Bit-Level Data Structures Laurens Devos, Wannes Meert, Jesse Davis

UAI 2019 Markov Logic Networks for Knowledge Base Completion: A Theoretical Analysis Under the MCAR Assumption Ondřej Kuželka, Jesse Davis

ECML-PKDD 2019 Player Vectors: Characterizing Soccer Players' Playing Style from Match Event Streams Tom Decroos, Jesse Davis

MLJ 2019 The Open International Soccer Database for Machine Learning Werner Dubitzky, Philippe Lopes, Jesse Davis, Daniel Berrar

AAAI 2018 Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction Jessa Bekker, Jesse Davis

UAI 2018 PAC-Reasoning in Relational Domains Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert

AAAI 2018 Relational Marginal Problems: Theory and Estimation Ondrej Kuzelka, Yuyi Wang, Jesse Davis, Steven Schockaert

IJCAI 2017 Induction of Interpretable Possibilistic Logic Theories from Relational Data Ondrej Kuzelka, Jesse Davis, Steven Schockaert

AAAI 2017 Predicting Soccer Highlights from Spatio-Temporal Match Event Streams Tom Decroos, Vladimir Dzyuba, Jan Van Haaren, Jesse Davis

IJCAI 2017 Solving Probability Problems in Natural Language Anton Dries, Angelika Kimmig, Jesse Davis, Vaishak Belle, Luc De Raedt

IJCAI 2016 Dynamic Early Stopping for Naive Bayes Aäron Verachtert, Hendrik Blockeel, Jesse Davis

IJCAI 2016 Learning Possibilistic Logic Theories from Default Rules Ondrej Kuzelka, Jesse Davis, Steven Schockaert

MLJ 2016 Lifted Generative Learning of Markov Logic Networks Jan Van Haaren, Guy Van den Broeck, Wannes Meert, Jesse Davis

UAI 2015 Encoding Markov Logic Networks in Possibilistic Logic Ondrej Kuzelka, Jesse Davis, Steven Schockaert

MLJ 2015 Learning Relational Dependency Networks in Hybrid Domains Irma Ravkic, Jan Ramon, Jesse Davis

AAAI 2015 TODTLER: Two-Order-Deep Transfer Learning Jan Van Haaren, Andrey Kolobov, Jesse Davis

NeurIPS 2015 Tractable Learning for Complex Probability Queries Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, Guy Van den Broeck

IJCAI 2015 Unsupervised Learning of an IS-A Taxonomy from a Limited Domain-Specific Corpus Daniele Alfarone, Jesse Davis

JMLR 2014 Improving Markov Network Structure Learning Using Decision Trees Daniel Lowd, Jesse Davis

AISTATS 2013 Completeness Results for Lifted Variable Elimination Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel

NeurIPS 2013 First-Order Decomposition Trees Nima Taghipour, Jesse Davis, Hendrik Blockeel

JAIR 2013 Lifted Variable Elimination: Decoupling the Operators from the Constraint Language Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel

AAAI 2012 Conditioning in First-Order Knowledge Compilation and Lifted Probabilistic Inference Guy Van den Broeck, Jesse Davis

ICML 2012 Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events Jesse Davis, Vítor Santos Costa, Elizabeth Berg, David Page, Peggy L. Peissig, Michael Caldwell

AISTATS 2012 Lifted Variable Elimination with Arbitrary Constraints Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel

AAAI 2012 Markov Network Structure Learning: A Randomized Feature Generation Approach Jan Van Haaren, Jesse Davis

ICML 2012 Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation Kendrick Boyd, Jesse Davis, David Page, Vítor Santos Costa

IJCAI 2011 Lifted Probabilistic Inference by First-Order Knowledge Compilation Guy Van den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis, Luc De Raedt

ICML 2010 Bottom-up Learning of Markov Network Structure Jesse Davis, Pedro M. Domingos

ICML 2009 Deep Transfer via Second-Order Markov Logic Jesse Davis, Pedro M. Domingos

ICML 2007 An Integrated Approach to Feature Invention and Model Construction for Drug Activity Prediction Jesse Davis, Vítor Santos Costa, Soumya Ray, David Page

IJCAI 2007 Change of Representation for Statistical Relational Learning Jesse Davis, Irene M. Ong, Jan Struyf, Elizabeth S. Burnside, David Page, Vítor Santos Costa

ECML-PKDD 2006 An Efficient Approximation to Lookahead in Relational Learners Jan Struyf, Jesse Davis, C. David Page Jr.

ICML 2006 The Relationship Between Precision-Recall and ROC Curves Jesse Davis, Mark H. Goadrich

ECML-PKDD 2005 An Integrated Approach to Learning Bayesian Networks of Rules Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Vítor Santos Costa

IJCAI 2005 View Learning for Statistical Relational Learning: With an Application to Mammography Jesse Davis, Elizabeth S. Burnside, Inês de Castro Dutra, David Page, Raghu Ramakrishnan, Vítor Santos Costa, Jude W. Shavlik