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2020 – today
- 2024
[j10]Ida Rahu
, Meelis Kull, Anneli Kruve:
Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors. J. Chem. Inf. Model. 64(8): 3093-3104 (2024)- [c24]Mari-Liis Allikivi, Joonas Järve, Meelis Kull:
Cautious Calibration in Binary Classification. ECAI 2024: 1503-1510 - [c23]Viacheslav Komisarenko, Meelis Kull:
Improving Calibration by Relating Focal Loss, Temperature Scaling, and Properness. ECAI 2024: 1535-1542 - [c22]Novin Shahroudi, Mihkel Lepson, Meelis Kull:
Evaluation of Trajectory Distribution Predictions with Energy Score. ICML 2024 - [e9]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part I. Lecture Notes in Computer Science 14941, Springer 2024, ISBN 978-3-031-70340-9 [contents] - [e8]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part II. Lecture Notes in Computer Science 14942, Springer 2024, ISBN 978-3-031-70343-0 [contents] - [e7]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part III. Lecture Notes in Computer Science 14943, Springer 2024, ISBN 978-3-031-70351-5 [contents] - [e6]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part IV. Lecture Notes in Computer Science 14944, Springer 2024, ISBN 978-3-031-70358-4 [contents] - [e5]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part V. Lecture Notes in Computer Science 14945, Springer 2024, ISBN 978-3-031-70361-4 [contents] - [e4]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VI. Lecture Notes in Computer Science 14946, Springer 2024, ISBN 978-3-031-70364-5 [contents] - [e3]Albert Bifet, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VII. Lecture Notes in Computer Science 14947, Springer 2024, ISBN 978-3-031-70367-6 [contents] - [e2]Albert Bifet, Povilas Daniusis, Jesse Davis, Tomas Krilavicius, Meelis Kull, Eirini Ntoutsi, Kai Puolamäki, Indre Zliobaite:
Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part VIII. Lecture Notes in Computer Science 14948, Springer 2024, ISBN 978-3-031-70370-6 [contents] - [i16]Mari-Liis Allikivi, Joonas Järve, Meelis Kull:
Cautious Calibration in Binary Classification. CoRR abs/2408.05120 (2024) - [i15]Viacheslav Komisarenko, Meelis Kull:
Improving Calibration by Relating Focal Loss, Temperature Scaling, and Properness. CoRR abs/2408.11598 (2024) - 2023
- [j9]Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach:
Classifier calibration: a survey on how to assess and improve predicted class probabilities. Mach. Learn. 112(9): 3211-3260 (2023) - [j8]Kaspar Valk, Meelis Kull:
Assuming Locally Equal Calibration Errors for Non-Parametric Multiclass Calibration. Trans. Mach. Learn. Res. 2023 (2023) - [c21]Bhawani Shankar Leelar, Meelis Kull:
Generality-Training of a Classifier for Improved Calibration in Unseen Contexts. ECML/PKDD (5) 2023: 374-391 - 2022
- [c20]Daniel Urda, Nuño Basurto, Meelis Kull, Álvaro Herrero:
Evaluating Classifiers' Performance to Detect Attacks in Website Traffic. CISIS-ICEUTE 2022: 205-215 - [i14]Kacper Sokol, Meelis Kull, Jeffrey Chan, Flora Dilys Salim:
Ethical and Fairness Implications of Model Multiplicity. CoRR abs/2203.07139 (2022) - [i13]Markus Kängsepp, Kaspar Valk, Meelis Kull:
On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers. CoRR abs/2203.08958 (2022) - [i12]Markus Kängsepp, Meelis Kull:
Calibrated Perception Uncertainty Across Objects and Regions in Bird's-Eye-View. CoRR abs/2211.04340 (2022) - 2021
- [j7]Fernando Martínez-Plumed, Lidia Contreras Ochando, Cèsar Ferri, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana, Peter A. Flach:
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Trans. Knowl. Data Eng. 33(8): 3048-3061 (2021) - [c19]Mohamed Maher, Meelis Kull:
Instance-based Label Smoothing For Better Calibrated Classification Networks. ICMLA 2021: 746-753 - [i11]Mohamed Maher, Meelis Kull:
Instance-based Label Smoothing For Better Calibrated Classification Networks. CoRR abs/2110.05355 (2021) - [i10]Theodore James Thibault Heiser, Mari-Liis Allikivi, Meelis Kull:
Shift Happens: Adjusting Classifiers. CoRR abs/2111.02529 (2021) - [i9]Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach:
Classifier Calibration: How to assess and improve predicted class probabilities: a survey. CoRR abs/2112.10327 (2021) - 2020
- [i8]Anti Ingel, Novin Shahroudi, Markus Kängsepp, Andre Tättar, Viacheslav Komisarenko, Meelis Kull:
Correlated daily time series and forecasting in the M4 competition. CoRR abs/2003.12796 (2020)
2010 – 2019
- 2019
- [c18]Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach:
Distribution calibration for regression. ICML 2019: 5897-5906 - [c17]Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach:
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration. NeurIPS 2019: 12295-12305 - [c16]Theodore James Thibault Heiser, Mari-Liis Allikivi, Meelis Kull:
Shift Happens: Adjusting Classifiers. ECML/PKDD (2) 2019: 55-70 - [c15]Mari-Liis Allikivi, Meelis Kull:
Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary Classification. ECML/PKDD (2) 2019: 103-120 - [i7]Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach:
Distribution Calibration for Regression. CoRR abs/1905.06023 (2019) - [i6]Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perelló-Nieto, Emma Tonkin, Peter A. Flach:
HyperStream: a Workflow Engine for Streaming Data. CoRR abs/1908.02858 (2019) - [i5]Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach:
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration. CoRR abs/1910.12656 (2019) - 2018
- [c14]Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach:
Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. KDD 2018: 243-252 - [i4]Hao Song, Meelis Kull, Peter A. Flach:
Non-Parametric Calibration of Probabilistic Regression. CoRR abs/1806.07690 (2018) - 2017
- [c13]Meelis Kull, Telmo de Menezes e Silva Filho, Peter A. Flach:
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers. AISTATS 2017: 623-631 - [i3]Tom Diethe, Niall Twomey, Meelis Kull, Peter A. Flach, Ian Craddock:
Probabilistic Sensor Fusion for Ambient Assisted Living. CoRR abs/1702.01209 (2017) - [i2]Fernando Martínez-Plumed, Lidia Contreras Ochando, César Ferri, Peter A. Flach, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana:
CASP-DM: Context Aware Standard Process for Data Mining. CoRR abs/1709.09003 (2017) - 2016
- [j6]José Hernández-Orallo, Adolfo Martínez Usó, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach, Chowdhury Farhan Ahmed, Nicolas Lachiche:
Reframing in context: A systematic approach for model reuse in machine learning. AI Commun. 29(5): 551-566 (2016) - [j5]Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown:
Cost-sensitive boosting algorithms: Do we really need them? Mach. Learn. 104(2-3): 359-384 (2016) - [c12]Reem Al-Otaibi, Meelis Kull, Peter A. Flach:
Declaratively Capturing Local Label Correlations with Multi-Label Trees. ECAI 2016: 1467-1475 - [c11]Miquel Perelló-Nieto, Telmo de Menezes e Silva Filho, Meelis Kull, Peter A. Flach:
Background Check: A General Technique to Build More Reliable and Versatile Classifiers. ICDM 2016: 1143-1148 - [c10]Hao Song, Meelis Kull, Peter A. Flach, Georgios Kalogridis:
Subgroup Discovery with Proper Scoring Rules. ECML/PKDD (2) 2016: 492-510 - [i1]Niall Twomey, Tom Diethe, Meelis Kull, Hao Song, Massimo Camplani, Sion L. Hannuna, Xenofon Fafoutis, Ni Zhu, Pete Woznowski, Peter A. Flach, Ian Craddock:
The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data. CoRR abs/1603.00797 (2016) - 2015
- [c9]Chowdhury Farhan Ahmed, Md. Samiullah, Nicolas Lachiche, Meelis Kull, Peter A. Flach:
Reframing in Frequent Pattern Mining. ICTAI 2015: 799-806 - [c8]Peter A. Flach, Meelis Kull:
Precision-Recall-Gain Curves: PR Analysis Done Right. NIPS 2015: 838-846 - [c7]Meelis Kull, Peter A. Flach:
Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration. ECML/PKDD (1) 2015: 68-85 - [c6]Reem Al-Otaibi, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach:
Versatile Decision Trees for Learning Over Multiple Contexts. ECML/PKDD (1) 2015: 184-199 - [c5]Meelis Kull, Nicolas Lachiche, Adolfo Martínez Usó:
Model Reuse with Bike rental Station data (Preamble). DC@PKDD/ECML 2015 - [e1]Adolfo Martínez Usó, João Mendes-Moreira, Luís Moreira-Matias, Meelis Kull, Nicolas Lachiche:
Proceedings of the ECML/PKDD 2015 Discovery Challenges co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2015), Porto, Portugal, September 7-11, 2015. CEUR Workshop Proceedings 1526, CEUR-WS.org 2015 [contents] - 2014
- [c4]Reem Al-Otaibi, Meelis Kull, Peter A. Flach:
LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion. ICMLA 2014: 74-79 - [c3]Meelis Kull, Peter A. Flach:
Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy. ECML/PKDD (2) 2014: 18-33 - [c2]Louise A. C. Millard, Meelis Kull, Peter A. Flach:
Rate-Oriented Point-Wise Confidence Bounds for ROC Curves. ECML/PKDD (2) 2014: 404-421 - 2010
- [c1]Meelis Kull, Konstantin Tretyakov, Jaak Vilo:
An Evolutionary Model of DNA Substring Distribution. Algorithms and Applications 2010: 147-157
2000 – 2009
- 2009
- [j4]Darya Krushevskaya, Hedi Peterson, Jüri Reimand, Meelis Kull, Jaak Vilo:
VisHiC - hierarchical functional enrichment analysis of microarray data. Nucleic Acids Res. 37(Web-Server-Issue): 587-592 (2009) - 2008
- [j3]Meelis Kull, Jaak Vilo:
Fast approximate hierarchical clustering using similarity heuristics. BioData Min. 1 (2008) - 2007
- [j2]Jüri Reimand, Meelis Kull, Hedi Peterson, Jaanus Hansen, Jaak Vilo:
g: Profiler - a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35(Web-Server-Issue): 193-200 (2007) - 2004
- [j1]Misha Kapushesky, Patrick Kemmeren, Aedín C. Culhane, Steffen Durinck, Jan Ihmels, Christine Körner, Meelis Kull, Aurora Torrente, Ugis Sarkans, Jaak Vilo, Alvis Brazma:
Expression Profiler: next generation - an online platform for analysis of microarray data. Nucleic Acids Res. 32(Web-Server-Issue): 465-470 (2004)
Coauthor Index
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last updated on 2024-10-28 20:14 CET by the dblp team
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