| 2025 | ECAI | Probability Density from Latent Diffusion Models for Out-of-Distribution Detection. | Joonas Jrve, Karl Kaspar Haavel, Meelis Kull |
| 2025 | ECAI | Aligning the Evaluation of Probabilistic Predictions with Downstream Value. | Novin Shahroudi, Viacheslav Komisarenko, Meelis Kull |
| 2024 | ECAI | Cautious Calibration in Binary Classification. | Mari-Liis Allikivi, Joonas Jrve, Meelis Kull |
| 2024 | ECAI | Improving Calibration by Relating Focal Loss, Temperature Scaling, and Properness. | Viacheslav Komisarenko, Meelis Kull |
| 2024 | ICML | Evaluation of Trajectory Distribution Predictions with Energy Score. | Novin Shahroudi, Mihkel Lepson, Meelis Kull |
| 2021 | ICMLA | Instance-based Label Smoothing For Better Calibrated Classification Networks. | Mohamed Maher, Meelis Kull |
| 2019 | ICML | Distribution calibration for regression. | Hao Song, Tom Diethe, Meelis Kull, 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 |
| 2016 | ECAI | Declaratively Capturing Local Label Correlations with Multi-Label Trees. | Reem Al-Otaibi, Meelis Kull, 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 |
| 2015 | ICTAI | Reframing in Frequent Pattern Mining. | Chowdhury Farhan Ahmed, Md. Samiullah, Nicolas Lachiche, Meelis Kull, 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 |