| 2024 | ECAI | Explaining a Probabilistic Prediction on the Simplex with Shapley Compositions. | Paul-Gauthier No, Miquel Perell-Nieto, Jean-Franois Bonastre, Peter A. Flach |
| 2023 | CIKM | Reconciling Training and Evaluation Objectives in Location Agnostic Surrogate Explainers. | Matthew Clifford, Jonathan Erskine, Alexander Hepburn, Peter A. Flach, Ral Santos-Rodrguez |
| 2023 | PERCOM | When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations. | Taku Yamagata, Emma L. Tonkin, Benjamin Arana Sanchez, Ian Craddock, Miquel Perell-Nieto, Ral Santos-Rodrguez, Weisong Yang, Peter A. Flach |
| 2022 | AISTATS | LIMESegment: Meaningful, Realistic Time Series Explanations. | Torty Sivill, Peter A. Flach |
| 2022 | ICANN | Self-Enhancer: A Self-supervised Framework for Low-Supervision, Drifted Data with Significant Missing Values. | Yu Chen, Peter A. Flach |
| 2022 | ICANN | Understanding Reinforcement Learning Based Localisation as a Probabilistic Inference Algorithm. | Taku Yamagata, Ral Santos-Rodrguez, Robert J. Piechocki, Peter A. Flach |
| 2022 | PERCOM | The Weak Supervision Landscape. | Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jess Cid-Sueiro, Miquel Perell-Nieto, Peter A. Flach, Ral Santos-Rodrguez |
| 2021 | IUI | 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, Ral Santos-Rodrguez, Peter A. Flach, Aisling Ann O'Kane |
| 2020 | AIES | FACE: Feasible and Actionable Counterfactual Explanations. | Rafael Poyiadzi, Kacper Sokol, Ral Santos-Rodrguez, Tijl De Bie, Peter A. Flach |
| 2020 | ECAI | 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, Ral Santos-Rodrguez |
| 2020 | IGARSS | Polsar Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field. | Haixia Bi, Ral Santos-Rodrguez, Peter A. Flach |
| 2019 | AAAI | Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward. | Peter A. Flach |
| 2019 | AAAI | Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety. | Kacper Sokol, Peter A. Flach |
| 2019 | AAAI | Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals. | Kacper Sokol, Peter A. Flach |
| 2019 | AISTATS | $β^3$-IRT: A New Item Response Model and its Applications. | Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudncio, Tom Diethe, Peter A. Flach |
| 2019 | ICML | Distribution calibration for regression. | Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach |
| 2018 | ESANN | Anomaly detection in star light curves using hierarchical Gaussian processes. | Haoyan Chen, Tom Diethe, Niall Twomey, Peter A. Flach |
| 2018 | IJCAI | 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, Emma Tonkin, Miquel Perell-Nieto, Sabrina Grant, Peter A. Flach |
| 2018 | IJCAI | The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI. | Fernando Martnez-Plumed, Bao Sheng Loe, Peter A. Flach, Sen higeartaigh, Karina Vold, Jos Hernndez-Orallo |
| 2018 | IJCAI | Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements. | Kacper Sokol, Peter A. Flach |
| 2018 | IJCAI | Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. | Kacper Sokol, Peter A. Flach |
| 2018 | KDD | Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. | Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perell-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach |
| 2017 | AISTATS | 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 |
| 2017 | IJCAI | The Role of Textualisation and Argumentation in Understanding the Machine Learning Process. | Kacper Sokol, Peter A. Flach |
| 2016 | ECAI | Declaratively Capturing Local Label Correlations with Multi-Label Trees. | Reem Al-Otaibi, Meelis Kull, Peter A. Flach |
| 2016 | ESANN | Active transfer learning for activity recognition. | Tom Diethe, Niall Twomey, Peter A. Flach |
| 2016 | ICDM | Background Check: A General Technique to Build More Reliable and Versatile Classifiers. | Miquel Perell-Nieto, Telmo de Menezes e Silva Filho, Meelis Kull, Peter A. Flach |
| 2016 | IJCAI | ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home. | Yu Chen, Tom Diethe, Peter A. Flach |
| 2016 | ILP | Activity Recognition in Multiple Contexts for Smart-House Data. | Kacper Sokol, Peter A. Flach |
| 2016 | KDD | 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 |
| 2015 | ICTAI | Reframing in Frequent Pattern Mining. | Chowdhury Farhan Ahmed, Md. Samiullah, Nicolas Lachiche, Meelis Kull, Peter A. Flach |
| 2014 | CISIS | A Machine Learning Approach to Objective Cardiac Event Detection. | Niall Twomey, Peter A. Flach |
| 2014 | ICMLA | LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion. | Reem Al-Otaibi, Meelis Kull, Peter A. Flach |
| 2011 | ICML | A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance. | Peter A. Flach, Jos Hernndez-Orallo, Csar Ferri Ramirez |
| 2011 | ICML | Brier Curves: a New Cost-Based Visualisation of Classifier Performance. | Jos Hernndez-Orallo, Peter A. Flach, Csar Ferri Ramirez |
| 2010 | ACL | Enhanced Word Decomposition by Calibrating the Decision Threshold of Probabilistic Models and Using a Model Ensemble. | Sebastian Spiegler, Peter A. Flach |
| 2010 | COLING | Ukwabelana - An open-source morphological Zulu corpus. | Sebastian Spiegler, Andrew van der Spuy, Peter A. Flach |
| 2010 | ECAI | The Advantages of Seed Examples in First-Order Multi-class Subgroup Discovery. | Tarek Abudawood, Peter A. Flach |
| 2010 | ILP | Learning Multi-class Theories in ILP. | Tarek Abudawood, Peter A. Flach |
| 2009 | AI | Cost-Based Sampling of Individual Instances. | William Klement, Peter A. Flach, Nathalie Japkowicz, Stan Matwin |
| 2008 | ECAI | A Fast Method for Property Prediction in Graph-Structured Data from Positive and Unlabelled Examples. | Susanne Hoche, Peter A. Flach, David Hardcastle |
| 2008 | ILP | Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms. | Simon Price, Peter A. Flach |
| 2006 | ILP | Towards Automating Simulation-Based Design Verification Using ILP. | Kerstin Eder, Peter A. Flach, Hsiou-Wen Hsueh |
| 2005 | IJCAI | Repairing Concavities in ROC Curves. | Peter A. Flach, Shaomin Wu |
| 2005 | IJCAI | ROCCER: An Algorithm for Rule Learning Based on ROC Analysis. | Ronaldo C. Prati, Peter A. Flach |
| 2005 | IDA | Combining Bayesian Networks with Higher-Order Data Representations. | Elias Gyftodimos, Peter A. Flach |
| 2004 | ICML | Redundant feature elimination for multi-class problems. | Annalisa Appice, Michelangelo Ceci, Simon Alan Rawles, Peter A. Flach |
| 2004 | ICML | Delegating classifiers. | Csar Ferri, Peter A. Flach, Jos Hernndez-Orallo |
| 2003 | COLT | On Graph Kernels: Hardness Results and Efficient Alternatives. | Thomas Grtner, Peter A. Flach, Stefan Wrobel |
| 2003 | ICML | The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics. | Peter A. Flach |
| 2003 | ICML | An Analysis of Rule Evaluation Metrics. | Johannes Frnkranz, Peter A. Flach |
| 2003 | ICML | Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves. | Nicolas Lachiche, Peter A. Flach |
| 2003 | ILP | Comparative Evaluation of Approaches to Propositionalization. | Mark-A. Krogel, Simon Alan Rawles, Filip Zelezn, Peter A. Flach, Nada Lavrac, Stefan Wrobel |
| 2003 | ILP | Improved Distances for Structured Data. | Dimitrios Mavroeidis, Peter A. Flach |
| 2002 | DIS | Improved Dataset Characterisation for Meta-learning. | Yonghong Peng, Peter A. Flach, Carlos Soares, Pavel Brazdil |
| 2002 | ICDM | Adapting classification rule induction to subgroup discovery. | Nada Lavrac, Peter A. Flach, Branko Kavsek, Ljupco Todorovski |
| 2002 | ICML | Learning Decision Trees Using the Area Under the ROC Curve. | Csar Ferri, Peter A. Flach, Jos Hernndez-Orallo |
| 2002 | ICML | Multi-Instance Kernels. | Thomas Grtner, Peter A. Flach, Adam Kowalczyk, Alexander J. Smola |
| 2002 | ILP | Kernels for Structured Data. | Thomas Grtner, John W. Lloyd, Peter A. Flach |
| 2002 | ILP | 1BC2: A True First-Order Bayesian Classifier. | Nicolas Lachiche, Peter A. Flach |
| 2002 | ILP | RSD: Relational Subgroup Discovery through First-Order Feature Construction. | Nada Lavrac, Filip Zelezn, Peter A. Flach |
| 2001 | EPIA | Multi-relational Data Mining: a perspective. | Peter A. Flach |
| 2001 | ICML | WBCsvm: Weighted Bayesian Classification based on Support Vector Machines. | Thomas Grtner, Peter A. Flach |
| 2000 | ILP | Decomposing Probability Distributions on Structured Individuals. | Peter A. Flach, Nicolas Lachiche |
| 1999 | ECSQARU | Knowledge Representation for Inductive Learning. | Peter A. Flach |
| 1999 | ILP | IBC: A First-Order Bayesian Classifier. | Peter A. Flach, Nicolas Lachiche |
| 1999 | ILP | Rule Evaluation Measures: A Unifying View. | Nada Lavrac, Peter A. Flach, Blaz Zupan |
| 1998 | ILP | Strongly Typed Inductive Concept Learning. | Peter A. Flach, Christophe G. Giraud-Carrier, John W. Lloyd |
| 1998 | KR | Comparing Consequence Relations. | Peter A. Flach |
| 1997 | ILP | Normal Forms for Inductive Logic Programming. | Peter A. Flach |
| 1996 | TARK | Rationality Postulates for Induction. | Peter A. Flach |
| 1991 | ISMIS | Towards a Theory of Inductive Logic Programming. | Peter A. Flach |