Second-order Inductive Learning. - Peter A. Flach (AII, 1989)

Consistent Term Mappings, Term Partitions and Inverse Resolution. - Shan-Hwei Nienhuys-Cheng; Peter A. Flach (EWSL, 1991)

Towards a Theory of Inductive Logic Programming. - Peter A. Flach (ISMIS, 1991)

An Analysis of Various Forms of "Jumping to Conclusions". - Peter A. Flach (AII, 1992)

A Model of Inductive Reasoning. - Peter A. Flach (Logic at Work, 1992)

Predicate Invention in Inductive Data Engineering. - Peter A. Flach (ECML, 1993)

Simply logical - intelligent reasoning by example. - Peter A. Flach (Wiley, 1994)

Rationality Postulates for Induction. - Peter A. Flach (TARK, 1996)

Inductive Logic Databases: From Extensional to Intensional Knowledge. - Peter A. Flach (DOOD, 1997)

Normal Forms for Inductive Logic Programming. - Peter A. Flach (ILP, 1997)

Abductive and Inductive Reasoning: Report of the ECAI'96 Workshop. - Peter A. Flach; Antonis C. Kakas (Log. J. IGPL, 1997)

From Extensional to Intensional Knowledge: Inductive Logic Programming Techniques and Their Application to Deductive Databases. - Peter A. Flach (Transactions and Change in Logic Databases, 1998)

Strongly Typed Inductive Concept Learning. - Peter A. Flach; Christophe G. Giraud-Carrier; John W. Lloyd (ILP, 1998)

Comparing Consequence Relations. - Peter A. Flach (KR, 1998)

Conference Report: Abduction and Induction in AI; Logic, Proofs and Algorithms; Logic in Natural Language; Logic for Concurrency and Synchronisation (LOCUS). - Peter A. Flach; Antonis C. Kakas; Ruy J. G. B. de Queiroz; Katia Silva Guimarães (Log. J. IGPL, 1998)

Knowledge Representation for Inductive Learning. - Peter A. Flach (ESCQARU, 1999)

IBC: A First-Order Bayesian Classifier. - Peter A. Flach; Nicolas Lachiche (ILP, 1999)

Rule Evaluation Measures: A Unifying View. - Nada Lavrac; Peter A. Flach; Blaz Zupan (ILP, 1999)

Database Dependency Discovery: A Machine Learning Approach. - Peter A. Flach; Iztok Savnik (AI Commun., 1999)

Decomposing Probability Distributions on Structured Individuals. - Peter A. Flach; Nicolas Lachiche (ILP Work-in-progress reports, 2000)

Predictive Performance of Weghted Relative Accuracy. - Ljupco Todorovski; Peter A. Flach; Nada Lavrac (PKDD, 2000)

The Use of Functional and Logic Languages in Machine Learning. - Peter A. Flach (WFLP, 2000)

Discovery of multivalued dependencies from relations. - Iztok Savnik; Peter A. Flach (Intell. Data Anal., 2000)

Multi-relational Data Mining: a perspective. - Peter A. Flach (EPIA, 2001)

WBCsvm: Weighted Bayesian Classification based on Support Vector Machines. - Thomas Gärtner 001; Peter A. Flach (ICML, 2001)

On the state of the art in machine learning: A personal review. - Peter A. Flach (Artif. Intell., 2001)

Editorial: Inductive Logic Programming is Coming of Age. - Peter A. Flach; Saso Dzeroski (Mach. Learn., 2001)

Confirmation-Guided Discovery of First-Order Rules with Tertius. - Peter A. Flach; Nicolas Lachiche (Mach. Learn., 2001)

An extended transformation approach to inductive logic programming. - Nada Lavrac; Peter A. Flach (ACM Trans. Comput. Log., 2001)

Learning in Clausal Logic: A Perspective on Inductive Logic Programming. - Peter A. Flach; Nada Lavrac (Computational Logic: Logic Programming and Beyond, 2002)

Improved Dataset Characterisation for Meta-learning. - Yonghong Peng; Peter A. Flach; Carlos Soares; Pavel Brazdil (Discovery Science, 2002)

Adapting classification rule induction to subgroup discovery. - Nada Lavrac; Peter A. Flach; Branko Kavsek; Ljupco Todorovski (ICDM, 2002)

Learning Decision Trees Using the Area Under the ROC Curve. - César Ferri; Peter A. Flach; José Hernández-Orallo (ICML, 2002)

Multi-Instance Kernels. - Thomas Gärtner 001; Peter A. Flach; Adam Kowalczyk; Alexander J. Smola (ICML, 2002)

Kernels for Structured Data. - Thomas Gärtner 001; John W. Lloyd; Peter A. Flach (ILP, 2002)

1BC2: A True First-Order Bayesian Classifier. - Nicolas Lachiche; Peter A. Flach (ILP, 2002)

RSD: Relational Subgroup Discovery through First-Order Feature Construction. - Nada Lavrac; Filip Zelezný; Peter A. Flach (ILP, 2002)

Web-based analysis of data mining and decision support education. - Tanja Urbancic; Maja Skrjanc; Peter A. Flach (AI Commun., 2002)

On Graph Kernels: Hardness Results and Efficient Alternatives. - Thomas Gärtner 001; Peter A. Flach; Stefan Wrobel (COLT, 2003)

Improving the AUC of Probabilistic Estimation Trees. - César Ferri; Peter A. Flach; José Hernández-Orallo (ECML, 2003)

The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics. - Peter A. Flach (ICML, 2003)

An Analysis of Rule Evaluation Metrics. - Johannes Fürnkranz; Peter A. Flach (ICML, 2003)

Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves. - Nicolas Lachiche; Peter A. Flach (ICML, 2003)

Comparative Evaluation of Approaches to Propositionalization. - Mark-A. Krogel; Simon Alan Rawles; Filip Zelezný; Peter A. Flach; Nada Lavrac; Stefan Wrobel (ILP, 2003)

Improved Distances for Structured Data. - Dimitrios Mavroeidis; Peter A. Flach (ILP, 2003)

An Analysis of Stopping and Filtering Criteria for Rule Learning. - Johannes Fürnkranz; Peter A. Flach (ECML, 2004)

Redundant feature elimination for multi-class problems. - Annalisa Appice; Michelangelo Ceci; Simon Alan Rawles; Peter A. Flach (ICML, 2004)

Delegating classifiers. - César Ferri; Peter A. Flach; José Hernández-Orallo (ICML, 2004)

Hierarchical Bayesian Networks: An Approach to Classification and Learning for Structured Data. - Elias Gyftodimos; Peter A. Flach (SETN, 2004)

Subgroup Discovery with CN2-SD. - Nada Lavrac; Branko Kavsek; Peter A. Flach; Ljupco Todorovski (J. Mach. Learn. Res., 2004)

Naive Bayesian Classification of Structured Data. - Peter A. Flach; Nicolas Lachiche (Mach. Learn., 2004)

Kernels and Distances for Structured Data. - Thomas Gärtner 001; John W. Lloyd; Peter A. Flach (Mach. Learn., 2004)

Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned. - Nada Lavrac; Bojan Cestnik; Dragan Gamberger; Peter A. Flach (Mach. Learn., 2004)

The 1st workshop on ROC analysis in artificial intelligence (ROCAI-2004). - José Hernández-Orallo; César Ferri; Nicolas Lachiche; Peter A. Flach (SIGKDD Explor., 2004)

Book review: Logic for Learning: Learning Comprehensible Theories from Structured Data by John W. Lloyd, Springer-Verlag, 2003, ISBN 3-540-42027-4. - Peter A. Flach (Theory Pract. Log. Program., 2004)

Combining Bayesian Networks with Higher-Order Data Representations. - Elias Gyftodimos; Peter A. Flach (IDA, 2005)

Repairing Concavities in ROC Curves. - Peter A. Flach; Shaomin Wu (IJCAI, 2005)

ROCCER: An Algorithm for Rule Learning Based on ROC Analysis. - Ronaldo C. Prati; Peter A. Flach (IJCAI, 2005)

A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. - Tom Fawcett; Peter A. Flach (Mach. Learn., 2005)

ROC 'n' Rule Learning - Towards a Better Understanding of Covering Algorithms. - Johannes Fürnkranz; Peter A. Flach (Mach. Learn., 2005)

Reinventing Machine Learning with ROC Analysis. - Peter A. Flach (IBERAMIA-SBIA, 2006)

Towards Automating Simulation-Based Design Verification Using ILP. - Kerstin Eder; Peter A. Flach; Hsiou-Wen Hsueh (ILP, 2006)

On classification, ranking, and probability estimation. - Peter A. Flach; Edson Takashi Matsubara (Probabilistic, Logical and Relational Learning - A Further Synthesis, 2007)

A Simple Lexicographic Ranker and Probability Estimator. - Peter A. Flach; Edson Takashi Matsubara (ECML, 2007)

An Improved Model Selection Heuristic for AUC. - Shaomin Wu; Peter A. Flach; Cèsar Ferri Ramirez (ECML, 2007)

Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation. - Peter A. Flach (ECML/PKDD, 2007)

Network analysis in natural sciences and engineering. - Susanne Hoche; Andreas Nürnberger; Peter A. Flach (AI Commun., 2007)

A Fast Method for Property Prediction in Graph-Structured Data from Positive and Unlabelled Examples. - Susanne Hoche; Peter A. Flach; David Hardcastle (ECAI, 2008)

Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms. - Simon Price; Peter A. Flach (ILP, 2008)

Learning the morphology of Zulu with different degrees of supervision. - Sebastian Spiegler; Bruno Golénia; Ksenia Shalonova; Peter A. Flach; Roger C. F. Tucker (SLT, 2008)

Cost-Based Sampling of Individual Instances. - William Klement; Peter A. Flach; Nathalie Japkowicz; Stan Matwin (Canadian AI, 2009)

Unsupervised Morpheme Discovery with Ungrade. - Bruno Golénia; Sebastian Spiegler; Peter A. Flach (CLEF (1), 2009)

UNGRADE: UNsupervised GRAph DEcomposition. - Bruno Golénia; Sebastian Spiegler; Peter A. Flach (CLEF (Working Notes), 2009)

Unsupervised Word Decomposition with the Promodes Algorithm. - Sebastian Spiegler; Bruno Golénia; Peter A. Flach (CLEF (1), 2009)

PROMODES: A Probabilistic Generative Model for Word Decomposition. - Sebastian Spiegler; Bruno Golénia; Peter A. Flach (CLEF (Working Notes), 2009)

Using Time Dependent Link Reduction to Improve the Efficiency of Topic Prediction in Co-Authorship Graphs. - Susanne Hoche; David Hardcastle; Peter A. Flach (CompleNet, 2009)

Evaluation Measures for Multi-class Subgroup Discovery. - Tarek Abudawood; Peter A. Flach (ECML/PKDD (1), 2009)

Abduction and Induction in Artificial Intelligence. - Antonis C. Kakas; Peter A. Flach (J. Appl. Log., 2009)

Novel tools to streamline the conference review process: experiences from SIGKDD'09. - Peter A. Flach; Sebastian Spiegler; Bruno Golénia; Simon Price; John Guiver; Ralf Herbrich; Thore Graepel; Mohammed J. Zaki (SIGKDD Explor., 2009)

Towards Learning Morphology for Under-Resourced Fusional and Agglutinating Languages. - Kseniya B. Shalonova; Bruno Golénia; Peter A. Flach (IEEE Trans. Speech Audio Process., 2009)

Enhanced Word Decomposition by Calibrating the Decision Threshold of Probabilistic Models and Using a Model Ensemble. - Sebastian Spiegler; Peter A. Flach (ACL, 2010)

Ukwabelana - An open-source morphological Zulu corpus. - Sebastian Spiegler; Andrew van der Spuy; Peter A. Flach (COLING, 2010)

The Advantages of Seed Examples in First-Order Multi-class Subgroup Discovery. - Tarek Abudawood; Peter A. Flach (ECAI, 2010)

Learning Multi-class Theories in ILP. - Tarek Abudawood; Peter A. Flach (ILP, 2010)

First-Order Multi-class Subgroup Discovery. - Tarek Abudawood; Peter A. Flach (STAIRS, 2010)

Exploiting the High Predictive Power of Multi-class Subgroups. - Tarek Abudawood; Peter A. Flach (ACML, 2010)

SubSift: a novel application of the vector space model to support the academic research process. - Simon Price; Peter A. Flach; Sebastian Spiegler (WAPA, 2010)

The Machine Learning journal: 250 issues and counting. - Peter A. Flach (Mach. Learn., 2010)

First-Order Logic. - Peter A. Flach (Encyclopedia of Machine Learning, 2010)

ROC Analysis. - Peter A. Flach (Encyclopedia of Machine Learning, 2010)

A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance. - Peter A. Flach; José Hernández-Orallo; Cèsar Ferri Ramirez (ICML, 2011)

Brier Curves: a New Cost-Based Visualisation of Classifier Performance. - José Hernández-Orallo; Peter A. Flach; Cèsar Ferri Ramirez (ICML, 2011)

Smooth Receiver Operating Characteristics (smROC) Curves. - William Klement; Peter A. Flach; Nathalie Japkowicz; Stan Matwin (ECML/PKDD (2), 2011)

Technical Note: Towards ROC Curves in Cost Space - José Hernández-Orallo; Peter A. Flach; Cèsar Ferri Ramirez (CoRR, 2011)

Generic Multiplicative Methods for Implementing Machine Learning Algorithms on MapReduce - Song Liu; Peter A. Flach; Nello Cristianini (CoRR, 2011)

Threshold Choice Methods: the Missing Link - José Hernández-Orallo; Peter A. Flach; César Ferri (CoRR, 2011)

The Machine Learning journal: 25 years young. - Peter A. Flach (Mach. Learn., 2011)

Machine Learning - The Art and Science of Algorithms that Make Sense of Data. - Peter A. Flach (Cambridge University Press, 2012)

Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them). - Daniel P. Berrar; Peter A. Flach (Briefings Bioinform., 2012)

A unified view of performance metrics: translating threshold choice into expected classification loss. - José Hernández-Orallo; Peter A. Flach; César Ferri (J. Mach. Learn. Res., 2012)

ILP turns 20 - Biography and future challenges. - Stephen H. Muggleton; Luc De Raedt; David Poole 001; Ivan Bratko; Peter A. Flach; Katsumi Inoue; Ashwin Srinivasan 001 (Mach. Learn., 2012)

A Higher-order data flow model for heterogeneous Big Data. - Simon Price; Peter A. Flach (IEEE BigData, 2013)

Guest editors' introduction: special section of selected papers from ECML-PKDD 2012. - Tijl De Bie; Peter A. Flach (Data Min. Knowl. Discov., 2013)

SubSift web services and workflows for profiling and comparing scientists and their published works. - Simon Price; Peter A. Flach; Sebastian Spiegler; Christopher Bailey 001; Nikki Rogers (Future Gener. Comput. Syst., 2013)

Guest editors' introduction: special issue of selected papers from ECML-PKDD 2012. - Tijl De Bie; Peter A. Flach (Mach. Learn., 2013)

ROC curves in cost space. - José Hernández-Orallo; Peter A. Flach; César Ferri (Mach. Learn., 2013)

A Machine Learning Approach to Objective Cardiac Event Detection. - Niall Twomey; Peter A. Flach (CISIS, 2014)

LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion. - Reem Al-Otaibi; Meelis Kull; Peter A. Flach (ICMLA, 2014)

Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy. - Meelis Kull; Peter A. Flach (ECML/PKDD (2), 2014)

Rate-Constrained Ranking and the Rate-Weighted AUC. - Louise A. C. Millard; Peter A. Flach; Julian P. T. Higgins (ECML/PKDD (2), 2014)

Rate-Oriented Point-Wise Confidence Bounds for ROC Curves. - Louise A. C. Millard; Meelis Kull; Peter A. Flach (ECML/PKDD (2), 2014)

Subgroup Discovery in Smart Electricity Meter Data. - Nanlin Jin; Peter A. Flach; Tom Wilcox; Royston Sellman; Joshua Thumim; Arno J. Knobbe (IEEE Trans. Ind. Informatics, 2014)

Reframing in Frequent Pattern Mining. - Chowdhury Farhan Ahmed; Md. Samiullah 001; Nicolas Lachiche; Meelis Kull; Peter A. Flach (ICTAI, 2015)

Activity recognition using conditional random field. - Megha Agarwal; Peter A. Flach (iWOAR, 2015)

Precision-Recall-Gain Curves: PR Analysis Done Right. - Peter A. Flach; Meelis Kull (NIPS, 2015)

Versatile Decision Trees for Learning Over Multiple Contexts. - Reem Al-Otaibi; Ricardo B. C. Prudêncio; Meelis Kull; Peter A. Flach (ECML/PKDD (1), 2015)

SVR-based Modelling for the MoReBikeS Challenge: Analysis, Visualisation and Prediction. - Yu Chen; Peter A. Flach (DC@PKDD/ECML, 2015)

Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics. - Tom Diethe; Niall Twomey; Peter A. Flach (ECML/PKDD (2), 2015)

Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration. - Meelis Kull; Peter A. Flach (ECML/PKDD (1), 2015)

Model Reuse with Subgroup Discovery. - Hao Song 007; Peter A. Flach (DC@PKDD/ECML, 2015)

Bridging e-Health and the Internet of Things: The SPHERE Project. - Ni Zhu; Tom Diethe; Massimo Camplani; Lili Tao; Alison Burrows; Niall Twomey; Dritan Kaleshi; Majid Mirmehdi; Peter A. Flach; Ian Craddock (IEEE Intell. Syst., 2015)

Report of the First International Workshop on Learning over Multiple Contexts (LMCE 2014). - Cèsar Ferri Ramirez; Peter A. Flach; Nicolas Lachiche (SIGKDD Explor., 2015)

Declaratively Capturing Local Label Correlations with Multi-Label Trees. - Reem Al-Otaibi; Meelis Kull; Peter A. Flach (ECAI, 2016)

Active transfer learning for activity recognition. - Tom Diethe; Niall Twomey; Peter A. Flach (ESANN, 2016)

Background Check: A General Technique to Build More Reliable and Versatile Classifiers. - Miquel Perelló-Nieto; Telmo de Menezes e Silva Filho; Meelis Kull; Peter A. Flach (ICDM, 2016)

ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home. - Yu Chen; Tom Diethe; Peter A. Flach (IJCAI, 2016)

Activity Recognition in Multiple Contexts for Smart-House Data. - Kacper Sokol; Peter A. Flach (ILP (Short Papers), 2016)

Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test. - Denis Moreira dos Reis; Peter A. Flach; Stan Matwin; Gustavo E. A. P. A. Batista (KDD, 2016)

BDL.NET: Bayesian dictionary learning in Infer.NET. - Tom Diethe; Niall Twomey; Peter A. Flach (MLSP, 2016)

Subgroup Discovery with Proper Scoring Rules. - Hao Song 007; Meelis Kull; Peter A. Flach; Georgios Kalogridis (ECML/PKDD (2), 2016)

Reframing in context: A systematic approach for model reuse in machine learning. - José Hernández-Orallo; Adolfo Martínez Usó; Ricardo B. C. Prudêncio; Meelis Kull; Peter A. Flach; Chowdhury Farhan Ahmed; Nicolas Lachiche (AI Commun., 2016)

The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data. - Niall Twomey; Tom Diethe; Meelis Kull; Hao Song 007; Massimo Camplani; Sion L. Hannuna; Xenofon Fafoutis; Ni Zhu; Pete Woznowski; Peter A. Flach; Ian Craddock (CoRR, 2016)

Cost-sensitive boosting algorithms: Do we really need them? - Nikolaos Nikolaou; Narayanan Unny Edakunni; Meelis Kull; Peter A. Flach; Gavin Brown 001 (Mach. Learn., 2016)

On the need for structure modelling in sequence prediction. - Niall Twomey; Tom Diethe; Peter A. Flach (Mach. Learn., 2016)

Feature Construction and Calibration for Clustering Daily Load Curves from Smart-Meter Data. - Reem Al-Otaibi; Nanlin Jin; Tom Wilcox; Peter A. Flach (IEEE Trans. Ind. Informatics, 2016)

Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers. - Meelis Kull; Telmo de Menezes e Silva Filho; Peter A. Flach (AISTATS, 2017)

The Role of Textualisation and Argumentation in Understanding the Machine Learning Process. - Kacper Sokol; Peter A. Flach (IJCAI, 2017)

Computational support for academic peer review: a perspective from artificial intelligence. - Simon Price; Peter A. Flach (Commun. ACM, 2017)

Probabilistic Sensor Fusion for Ambient Assisted Living. - Tom Diethe; Niall Twomey; Meelis Kull; Peter A. Flach; Ian Craddock (CoRR, 2017)

CASP-DM: Context Aware Standard Process for Data Mining. - Fernando Martínez-Plumed; Lidia Contreras Ochando; César Ferri; Peter A. Flach; José Hernández-Orallo; Meelis Kull; Nicolas Lachiche; María José Ramírez-Quintana (CoRR, 2017)

Unsupervised learning of sensor topologies for improving activity recognition in smart environments. - Niall Twomey; Tom Diethe; Ian Craddock; Peter A. Flach (Neurocomputing, 2017)

Classifier Calibration. - Peter A. Flach (Encyclopedia of Machine Learning and Data Mining, 2017)

First-Order Logic. - Peter A. Flach (Encyclopedia of Machine Learning and Data Mining, 2017)

ROC Analysis. - Peter A. Flach (Encyclopedia of Machine Learning and Data Mining, 2017)

Anomaly detection in star light curves using hierarchical Gaussian processes. - Haoyan Chen; Tom Diethe; Niall Twomey; Peter A. Flach (ESANN, 2018)

Analysis of Patient Domestic Activity in Recovery From Hip or Knee RePlacement Surgery: Modelling Wrist-worn Wearable RSSI and Accelerometer Data in The Wild. - Mike Holmes; Hao Song 007; Emma Tonkin; Miquel Perelló-Nieto; Sabrina Grant; Peter A. Flach (KDH@IJCAI, 2018)

The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI. - Fernando Martínez-Plumed; Bao Sheng Loe; Peter A. Flach; Seán Ó hÉigeartaigh; Karina Vold; José Hernández-Orallo (IJCAI, 2018)

Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements. - Kacper Sokol; Peter A. Flach (IJCAI, 2018)

Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. - Kacper Sokol; Peter A. Flach (IJCAI, 2018)

Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. - Tom Diethe; Mike Holmes; Meelis Kull; Miquel Perelló-Nieto; Kacper Sokol; Hao Song 007; Emma Tonkin; Niall Twomey; Peter A. Flach (KDD, 2018)

Non-Parametric Calibration of Probabilistic Regression. - Hao Song 007; Meelis Kull; Peter A. Flach (CoRR, 2018)

Introduction to the special issue on Data Science in Europe. - Peter A. Flach; Myra Spiliopoulou; Serge Allegrezza; Matthias Böhmer 002; Burkhard Hess; Berthold Lausen (Int. J. Data Sci. Anal., 2018)

A Comprehensive Study of Activity Recognition Using Accelerometers. - Niall Twomey; Tom Diethe; Xenofon Fafoutis; Atis Elsts; Ryan McConville; Peter A. Flach; Ian Craddock (Informatics, 2018)

Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation. - Przemyslaw Woznowski; Emma Tonkin; Peter A. Flach (Sensors, 2018)

Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward. - Peter A. Flach (AAAI, 2019)

Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety. - Kacper Sokol; Peter A. Flach (SafeAI@AAAI, 2019)

Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals. - Kacper Sokol; Peter A. Flach (AAAI, 2019)

$β^3$-IRT: A New Item Response Model and its Applications. - Yu Chen; Telmo de Menezes e Silva Filho; Ricardo B. C. Prudêncio; Tom Diethe; Peter A. Flach (AISTATS, 2019)

Distribution calibration for regression. - Hao Song 007; Tom Diethe; Meelis Kull; Peter A. Flach (ICML, 2019)

Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration. - Meelis Kull; Miquel Perelló-Nieto; Markus Kängsepp; Telmo de Menezes e Silva Filho; Hao Song 007; Peter A. Flach (NeurIPS, 2019)

A Big Data platform for smart meter data analytics. - Tom Wilcox; Nanlin Jin; Peter A. Flach; Joshua Thumim (Comput. Ind., 2019)

β3-IRT: A New Item Response Model and its Applications. - Yu Chen; Telmo de Menezes e Silva Filho; Ricardo B. C. Prudêncio; Tom Diethe; Peter A. Flach (CoRR, 2019)

HyperStream: a Workflow Engine for Streaming Data. - Tom Diethe; Meelis Kull; Niall Twomey; Kacper Sokol; Hao Song 007; Miquel Perelló-Nieto; Emma Tonkin; Peter A. Flach (CoRR, 2019)

Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration. - Meelis Kull; Miquel Perelló-Nieto; Markus Kängsepp; Telmo de Menezes e Silva Filho; Hao Song 007; Peter A. Flach (CoRR, 2019)

bLIMEy: Surrogate Prediction Explanations Beyond LIME. - Kacper Sokol; Alexander Hepburn; Raúl Santos-Rodríguez; Peter A. Flach (CoRR, 2019)

Setting decision thresholds when operating conditions are uncertain. - Cèsar Ferri; José Hernández-Orallo; Peter A. Flach (Data Min. Knowl. Discov., 2019)

An application of hierarchical Gaussian processes to the detection of anomalies in star light curves. - Niall Twomey; Haoyan Chen; Tom Diethe; Peter A. Flach (Neurocomputing, 2019)

FACE: Feasible and Actionable Counterfactual Explanations. - Rafael Poyiadzi; Kacper Sokol; Raúl Santos-Rodríguez; Tijl De Bie; Peter A. Flach (AIES, 2020)

Model-Based Reinforcement Learning for Type 1 Diabetes Blood Glucose Control. - Taku Yamagata; Aisling Ann O'Kane; Amid Ayobi; Dmitri S. Katz; Katarzyna Stawarz; Paul Marshall; Peter A. Flach; Raúl Santos-Rodríguez (AAI4H@ECAI, 2020)

Explainability fact sheets: a framework for systematic assessment of explainable approaches. - Kacper Sokol; Peter A. Flach (FAT*, 2020)

Polsar Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field. - Haixia Bi; Raúl Santos-Rodríguez; Peter A. Flach (IGARSS, 2020)

One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency. - Kacper Sokol; Peter A. Flach (CoRR, 2020)

LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees. - Kacper Sokol; Peter A. Flach (CoRR, 2020)

Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning. - Yu Chen; Tom Diethe; Peter A. Flach (CoRR, 2020)

Towards Faithful and Meaningful Interpretable Representations. - Kacper Sokol; Peter A. Flach (CoRR, 2020)

Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control. - Taku Yamagata; Aisling Ann O'Kane; Amid Ayobi; Dmitri S. Katz; Katarzyna Stawarz; Paul Marshall; Peter A. Flach; Raúl Santos-Rodríguez (CoRR, 2020)

Uni- and multivariate probability density models for numeric subgroup discovery. - Marvin Meeng; Harm de Vries; Peter A. Flach; Siegfried Nijssen; Arno J. Knobbe (Intell. Data Anal., 2020)

Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery. - Michael Holmes; Miquel Perelló-Nieto; Hao Song 007; Emma Tonkin; Sabrina Grant; Peter A. Flach (J. Heal. Informatics Res., 2020)

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. - Kacper Sokol; Alexander Hepburn; Rafael Poyiadzi; Matthew Clifford; Raúl Santos-Rodríguez; Peter A. Flach (J. Open Source Softw., 2020)

One Explanation Does Not Fit All. - Kacper Sokol; Peter A. Flach (Künstliche Intell., 2020)

Reflections on reciprocity in research. - Peter A. Flach (Mach. Learn., 2020)

FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. - Kacper Sokol; Alexander Hepburn; Rafael Poyiadzi; Matthew Clifford; Raúl Santos-Rodríguez; Peter A. Flach (Zenodo, 2020)

Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine Learning. - Amid Ayobi; Katarzyna Stawarz; Dmitri S. Katz; Paul Marshall; Taku Yamagata; Raúl Santos-Rodríguez; Peter A. Flach; Aisling Ann O'Kane (IUI Workshops, 2021)

Continual Density Ratio Estimation in an Online Setting. - Yu Chen; Song Liu; Tom Diethe; Peter A. Flach (CoRR, 2021)

You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source. - Kacper Sokol; Peter A. Flach (CoRR, 2021)

Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning. - Stefan Radic Webster; Peter A. Flach (CoRR, 2021)

Classifier Calibration: How to assess and improve predicted class probabilities: a survey. - Telmo de Menezes e Silva Filho; Hao Song 007; Miquel Perelló-Nieto; Raúl Santos-Rodríguez; Meelis Kull; Peter A. Flach (CoRR, 2021)

Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence. - Kacper Sokol; Peter A. Flach (CoRR, 2021)

Efficient and Robust Model Benchmarks with Item Response Theory and Adaptive Testing. - Hao Song 007; Peter A. Flach (Int. J. Interact. Multim. Artif. Intell., 2021)

Multi-label thresholding for cost-sensitive classification. - Reem Alotaibi; Peter A. Flach (Neurocomputing, 2021)

Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies. - Amid Ayobi; Katarzyna Stawarz; Dmitri S. Katz; Paul Marshall; Taku Yamagata; Raúl Santos-Rodríguez; Peter A. Flach; Aisling Ann O'Kane (Proc. ACM Hum. Comput. Interact., 2021)

Human Activity Recognition Based on Dynamic Active Learning. - Haixia Bi; Miquel Perelló-Nieto; Raúl Santos-Rodríguez; Peter A. Flach (IEEE J. Biomed. Health Informatics, 2021)

CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. - Fernando Martínez-Plumed; Lidia Contreras Ochando; Cèsar Ferri; José Hernández-Orallo; Meelis Kull; Nicolas Lachiche; María José Ramírez-Quintana; Peter A. Flach (IEEE Trans. Knowl. Data Eng., 2021)

LIMESegment: Meaningful, Realistic Time Series Explanations. - Torty Sivill; Peter A. Flach (AISTATS, 2022)

Self-Enhancer: A Self-supervised Framework for Low-Supervision, Drifted Data with Significant Missing Values. - Yu Chen 092; Peter A. Flach (ICANN (4), 2022)

Understanding Reinforcement Learning Based Localisation as a Probabilistic Inference Algorithm. - Taku Yamagata; Raúl Santos-Rodríguez; Robert J. Piechocki; Peter A. Flach (ICANN (2), 2022)

The Weak Supervision Landscape. - Rafael Poyiadzi; Daniel Bacaicoa-Barber; Jesús Cid-Sueiro; Miquel Perelló-Nieto; Peter A. Flach; Raúl Santos-Rodríguez (PerCom Workshops, 2022)

Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). - Peter A. Flach; Kacper Sokol (CoRR, 2022)

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. - Kacper Sokol; Alexander Hepburn; Raúl Santos-Rodríguez; Peter A. Flach (CoRR, 2022)

Empirical Evaluation of Predictive Models: A keynote at ECIR 2022. - Peter A. Flach (SIGIR Forum, 2022)

Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). - Peter A. Flach; Kacper Sokol (Zenodo, 2022)

What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. - Kacper Sokol; Alexander Hepburn; Raúl Santos-Rodriguez; Peter A. Flach (Zenodo, 2022)

FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency. - Kacper Sokol; Raúl Santos-Rodríguez; Peter A. Flach (Softw. Impacts, 2022)

Reconciling Training and Evaluation Objectives in Location Agnostic Surrogate Explainers. - Matthew Clifford; Jonathan Erskine; Alexander Hepburn; Peter A. Flach; Raúl Santos-Rodríguez (CIKM, 2023)

When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations. - Taku Yamagata; Emma L. Tonkin; Benjamin Arana Sanchez; Ian Craddock; Miquel Perelló-Nieto; Raúl Santos-Rodríguez; Weisong Yang; Peter A. Flach (PerCom Workshops, 2023)

MIDI-Draw: Sketching to Control Melody Generation. - Tashi Namgyal; Peter A. Flach; Raúl Santos-Rodríguez (CoRR, 2023)

Shapley Sets: Feature Attribution via Recursive Function Decomposition. - Torty Sivill; Peter A. Flach (CoRR, 2023)

Simply Logical - The First Three Decades. - Peter A. Flach; Kacper Sokol; Jan Wielemaker (Prolog: The Next 50 Years, 2023)

Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making. - Katarzyna Stawarz; Dmitri S. Katz; Amid Ayobi; Paul Marshall; Taku Yamagata; Raúl Santos-Rodríguez; Peter A. Flach; Aisling Ann O'Kane (Int. J. Hum. Comput. Stud., 2023)

Classifier calibration: a survey on how to assess and improve predicted class probabilities. - Telmo de Menezes e Silva Filho; Hao Song 007; Miquel Perelló-Nieto; Raúl Santos-Rodríguez; Meelis Kull; Peter A. Flach (Mach. Learn., 2023)

An active semi-supervised deep learning model for human activity recognition. - Haixia Bi; Miquel Perelló-Nieto; Raúl Santos-Rodríguez; Peter A. Flach; Ian Craddock (J. Ambient Intell. Humaniz. Comput., 2023)

Explaining a Probabilistic Prediction on the Simplex with Shapley Compositions. - Paul-Gauthier Noé; Miquel Perelló-Nieto; Jean-François Bonastre; Peter A. Flach (ECAI, 2024)

Interpretable representations in explainable AI: from theory to practice. - Kacper Sokol; Peter A. Flach (Data Min. Knowl. Discov., 2024)