On Approximating Minimum Infrequent and Maximum Frequent Sets. - Mario Boley (Discovery Science, 2007)

Efficient Closed Pattern Mining in Strongly Accessible Set Systems. - Mario Boley; Tamás Horváth 001; Axel Poigné; Stefan Wrobel (MLG, 2007)

Efficient Closed Pattern Mining in Strongly Accessible Set Systems (Extended Abstract). - Mario Boley; Tamás Horváth 001; Axel Poigné; Stefan Wrobel (PKDD, 2007)

A Randomized Approach for Approximating the Number of Frequent Sets. - Mario Boley; Henrik Grosskreutz (ICDM, 2008)

On the Complexity of Constraint-Based Theory Extraction. - Mario Boley; Thomas Gärtner 001 (Discovery Science, 2009)

New Results on Listing Closed Sets of Strongly Accessible Set Systems. - Mario Boley; Tamás Horváth 001; Axel Poigné; Stefan Wrobel (MLG/SRL@ILP, 2009)

MAP Structured Prediction by Sampling. - Shankar Vembu; Thomas Gärtner 001; Mario Boley (MLG/SRL@ILP, 2009)

Non-redundant Subgroup Discovery Using a Closure System. - Mario Boley; Henrik Grosskreutz (ECML/PKDD (1), 2009)

Probabilistic Structured Predictors. - Shankar Vembu; Thomas Gärtner 001; Mario Boley (UAI, 2009)

Approximating the number of frequent sets in dense data. - Mario Boley; Henrik Grosskreutz (Knowl. Inf. Syst., 2009)

Efficient discovery of interesting patterns based on strong closedness. - Mario Boley; Tamás Horváth 001; Stefan Wrobel (Stat. Anal. Data Min., 2009)

Subgroup Discovery for Election Analysis: A Case Study in Descriptive Data Mining. - Henrik Grosskreutz; Mario Boley; Maike Krause-Traudes (Discovery Science, 2010)

Listing closed sets of strongly accessible set systems with applications to data. - Mario Boley; Tamás Horváth 001; Axel Poigné; Stefan Wrobel (LWA, 2010)

Formal Concept Sampling for Counting and Threshold-Free Local Pattern Mining. - Mario Boley; Thomas Gärtner 001; Henrik Grosskreutz (SDM, 2010)

Listing closed sets of strongly accessible set systems with applications to data mining. - Mario Boley; Tamás Horváth 001; Axel Poigné; Stefan Wrobel (Theor. Comput. Sci., 2010)

The Efficient Discovery of Interesting Closed Pattern Collections. - Mario Boley (2010)

Direct local pattern sampling by efficient two-step random procedures. - Mario Boley; Claudio Lucchese; Daniel Paurat; Thomas Gärtner 001 (KDD, 2011)

Direct Pattern Sampling with Respect to Pattern Frequency. - Mario Boley; Claudio Lucchese; Daniel Paurat; Thomas Gärtner 001 (LWA, 2011)

Linear space direct pattern sampling using coupling from the past. - Mario Boley; Sandy Moens; Thomas Gärtner 001 (KDD, 2012)

Mining Interesting Patterns in Multi-relational Data with N-ary Relationships. - Eirini Spyropoulou; Tijl De Bie; Mario Boley (Discovery Science, 2013)

Beating Human Analysts in Nowcasting Corporate Earnings by Using Publicly Available Stock Price and Correlation Features. - Michael Kamp; Mario Boley; Thomas Gärtner 001 (ICDM Workshops, 2013)

One click mining: interactive local pattern discovery through implicit preference and performance learning. - Mario Boley; Michael Mampaey; Bo Kang; Pavel Tokmakov; Stefan Wrobel (IDEA@KDD, 2013)

Privacy-Preserving Mobility Monitoring Using Sketches of Stationary Sensor Readings. - Michael Kamp; Christine Kopp; Michael Mock; Mario Boley; Michael May 001 (ECML/PKDD (3), 2013)

Communication-Efficient Distributed Online Prediction using Dynamic Model Synchronizations. - Mario Boley; Michael Kamp; Daniel Keren; Assaf Schuster; Izchak Sharfman (BD3@VLDB, 2013)

Providing Concise Database Covers Instantly by Recursive Tile Sampling. - Sandy Moens; Mario Boley; Bart Goethals (Discovery Science, 2014)

Instant Exceptional Model Mining Using Weighted Controlled Pattern Sampling. - Sandy Moens; Mario Boley (IDA, 2014)

Communication-Efficient Distributed Online Prediction by Dynamic Model Synchronization. - Michael Kamp; Mario Boley; Daniel Keren; Assaf Schuster; Izchak Sharfman (ECML/PKDD (1), 2014)

Interesting pattern mining in multi-relational data. - Eirini Spyropoulou; Tijl De Bie; Mario Boley (Data Min. Knowl. Discov., 2014)

In-Stream Frequent Itemset Mining With Output Proportional Memory Footprint. - Daniel Trabold; Mario Boley; Michael Mock; Tamás Horváth 001 (LWA, 2015)

Communication-Efficient Distributed Online Learning with Kernels. - Michael Kamp; Sebastian Bothe; Mario Boley; Michael Mock (ECML/PKDD (2), 2016)

Efficiently Discovering Locally Exceptional Yet Globally Representative Subgroups. - Janis Kalofolias; Mario Boley; Jilles Vreeken (ICDM, 2017)

Discovering Reliable Approximate Functional Dependencies. - Panagiotis Mandros; Mario Boley; Jilles Vreeken (KDD, 2017)

Effective Parallelisation for Machine Learning. - Michael Kamp; Mario Boley; Olana Missura; Thomas Gärtner 001 (NIPS, 2017)

Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery. - Mario Boley; Bryan R. Goldsmith; Luca M. Ghiringhelli; Jilles Vreeken (Data Min. Knowl. Discov., 2017)

Discovering Robustly Connected Subgraphs with Simple Descriptions. - Janis Kalofolias; Mario Boley; Jilles Vreeken (ICDM, 2019)

Discovering Reliable Correlations in Categorical Data. - Panagiotis Mandros; Mario Boley; Jilles Vreeken (ICDM, 2019)

Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms. - Panagiotis Mandros; Mario Boley; Jilles Vreeken (IJCAI, 2019)

Adaptive Communication Bounds for Distributed Online Learning. - Michael Kamp; Mario Boley; Michael Mock; Daniel Keren; Assaf Schuster; Izchak Sharfman (CoRR, 2019)

Discovering Functional Dependencies from Mixed-Type Data. - Panagiotis Mandros; David Kaltenpoth; Mario Boley; Jilles Vreeken (KDD, 2020)

Discovering dependencies with reliable mutual information. - Panagiotis Mandros; Mario Boley; Jilles Vreeken (Knowl. Inf. Syst., 2020)

Relative Flatness and Generalization. - Henning Petzka; Michael Kamp; Linara Adilova; Cristian Sminchisescu; Mario Boley (NeurIPS, 2021)

Better Short than Greedy: Interpretable Models through Optimal Rule Boosting. - Mario Boley; Simon Teshuva; Pierre Le Bodic; Geoffrey I. Webb (SDM, 2021)

Discovering Reliable Causal Rules. - Kailash Budhathoki; Mario Boley; Jilles Vreeken (SDM, 2021)

Enhanced Extra Trees Classifier for Epileptic Seizure Prediction. - Maurice Ntahobari; Levin Kuhlmann; Mario Boley; Zhinoos Razavi Hesabi (ICSPIS, 2022)

Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm. - Yun Zhao; Mario Boley; Andria Pelentritou; Philippa J. Karoly; Dean R. Freestone; Yueyang Liu; Suresh D. Muthukumaraswamy; William Woods; David T. J. Liley; Levin Kuhlmann (NeuroImage, 2022)

Inference-based time-resolved stability analysis of nonlinear whole-cortex modeling: application to Xenon anaesthesia. - Yun Zhao; Mario Boley; Andria Pelentritou; William Woods; David T. J. Liley; Levin Kuhlmann (EMBC, 2023)

Bayes beats Cross Validation: Efficient and Accurate Ridge Regression via Expectation Maximization. - Shu Yu Tew; Mario Boley; Daniel F. Schmidt (NeurIPS, 2023)

From Prediction to Action: The Critical Role of Proper Performance Estimation for Machine-Learning-Driven Materials Discovery. - Mario Boley; Felix Luong; Simon Teshuva; Daniel F. Schmidt; Lucas Foppa; Matthias Scheffler (CoRR, 2023)

Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions. - Yun Zhao; Felix Luong; Simon Teshuva; Andria Pelentritou; William Woods; David T. J. Liley; Daniel F. Schmidt; Mario Boley; Levin Kuhlmann (Int. J. Neural Syst., 2023)

Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly. - Yiwen Lu; Dilek Yalcin; Paul J. Pigram; Lewis D. Blackman; Mario Boley (J. Chem. Inf. Model., 2023)

Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles. - Fan Yang; Pierre Le Bodic; Michael Kamp; Mario Boley (AISTATS, 2024)

Inference-based time-resolved chaos analysis of brain models: application to focal epilepsy. - Yun Zhao; David B. Grayden; Mario Boley; Yueyang Liu; Philippa J. Karoly; Mark J. Cook; Levin Kuhlmann (FUSION, 2024)

Improving Random Forests by Smoothing. - Ziyi Liu; Phuc Luong; Mario Boley; Daniel F. Schmidt (CoRR, 2025)

GRASP: Grouped Regression with Adaptive Shrinkage Priors. - Shu Yu Tew; Daniel F. Schmidt; Mario Boley (CoRR, 2025)

Interpretable Representation Learning for Additive Rule Ensembles. - Shahrzad Behzadimanesh; Pierre Le Bodic; Geoffrey I. Webb; Mario Boley (CoRR, 2025)