Non-exhaustive list of papers for the assignments

Here is a list of possible papers.

Suggestion of papers to select from:

• [quost2018classification] Quost, B., & Destercke, S. (2018). Classification by pairwise coupling of imprecise probabilities. Pattern Recognition, 77, 412-425.
  Topic: pairwise decomposition in classification
  Nature: methodological paper
  
  

• [maua2018robustifying] Mauá, D. D., Conaty, D., Cozman, F. G., Poppenhaeger, K., & de Campos, C. P. (2018). Robustifying sum-product networks. International Journal of Approximate Reasoning, 101, 163-180.
  Topic: extending a specific probabilistic circuit (can be seen as a specific neural network) to deal with probability sets
  Nature: mostly methodological (some theory)
  

• [yang2016cost]Yang, Gen, Sébastien Destercke, and Marie-Hélène Masson (2016). "The costs of indeterminacy: how to determine them?." IEEE transactions on cybernetics 47.12: 4316-4327.
  Topic: Desirable properties of utilities
  Nature: methodological
  
  

• [bernard2005introduction] Bernard, J. M. (2005). An introduction to the imprecise Dirichlet model for multinomial data. International Journal of Approximate Reasoning, 39(2-3), 123-150.
  Topic: extending the Dirichlet model used in Bayesian approaches to estimate multinomials to the imprecise case
  Nature: detailed and technical introduction to the model
  
  

• [nguyen2025credal] Vu-Linh Nguyen, Haifei Zhang and Sébastien Destercke (2025). Credal ensemble in multi-class classification. Machine Learning , 114 (1), 19.
  Topic: learning model that uses random forest to derive credal sets
  Nature: methodological
  
  

• [alarcon2021imprecise] Alarcon, Y. C. C., & Destercke, S. (2021). Imprecise gaussian discriminant classification. Pattern Recognition, 112, 107739
  Topic: learning model that generalises discriminant analysis
  Nature: methodological
  
  

• [angelopoulos2021gentle] Angelopoulos, A. N., & Bates, S. (2021). A gentle introduction to conformal prediction and distribution-free uncertainty quantification.
  Topic: general introduction to conformal prediction, and up-to-date survey
  Nature: survey of many results (groups can consider only a part of it)
  

• [silva2023classifier] Silva Filho, T., Song, H., Perello-Nieto, M., Santos-Rodriguez, R., Kull, M., & Flach, P. (2023). Classifier calibration: a survey on how to assess and improve predicted class probabilities. Machine Learning, 1-50.
  Topic: general introduction to calibration methods, and up-to-date survey of many results (groups can consider only a part of it)
  Nature: survey of many results (groups can consider only a part of it)

• [corani2008learning] Corani, G., & Zaffalon, M. (2008). Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2. Journal of Machine Learning Research, 9(4).
  Topic: Extending Naive Bayes Classifier 
  Nature: mostly methodological (some theory)

• [mantas2014credal] Mantas, Carlos J., and Joaquin Abellan. "Credal-C4. 5: Decision tree based on imprecise probabilities to classify noisy data." Expert Systems with Applications 41.10 (2014): 4625-4637.
  Topic: extending decision trees
  Nature: methodological