Non-exhaustive list of papers for the assignments

Here is a list of possible papers. Hardness of a paper range from + (rather easy to follow) to +++++ (quite hard to follow) and is based on our subjective perception of the paper.

We expect that the easier a paper is, the more of it is covered in the ilustration, and the more worked out this later is.

For each paper, we also specify for which type of assignment we think a paper is suited (since all papers do not lend themselves that well to, e.g., implementation).

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
  Possible assignments: "Being in a teacher shoes", "Being in a TA shoes", "Wake up the blogger in you"
  

• [couso2000survey] Couso, I., Moral, S., & Walley, P. (2000). A survey of concepts of independence for imprecise probabilities. Risk, Decision and Policy, 5(2), 165-181. 
  Topic: independence notions for imprecise probabilities
  Nature: survey paper
  Possible assignments: "Being in a teacher shoes", "Being in a TA shoes", "Explain to your high-school nephew"
  

• [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)
  Possible assignments: "Being in a teacher shoes", "Explain to your high-school nephew", "wake up the blogger in you" 
  

• [zaffalon2012evaluating] Zaffalon, M., Corani, G., & Mauá, D. (2012). Evaluating credal classifiers by utility-discounted predictive accuracy. International Journal of Approximate Reasoning, 53(8), 1282.
  Nature: methodological
  Possible assignments: "Being in a teacher shoes" (Selected; Lecture/Exercise; 1 st assignment), "Explain to your high-school nephew" (Selected; 2nd assignment), "wake up the blogger in you"

• [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
  Possible assignments: "Being in a TA shoes", "Explain to your high-school nephew"
  

• [nguyen2023learning] Vu-Linh Nguyen, Haifei Zhang and Sébastien Destercke. Learning sets of Probabilities through ensemble methods. ECSQARU 2023
  Topic: learning model that uses random forest to derive credal sets
  Nature: methodological
  Possible assignments: "Being in a TA shoes", "wake up the blogger in you", "Being in a teacher shoes"
  

• [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
  Possible assignments: "wake up the blogger in you", "Being in a teacher shoes"

• [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)
  Possible assignments: "wake up the blogger in you", "Being in a teacher shoes", "Explain to your high-school nephew"

• [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)
  Possible assignments: "wake up the blogger in you", "Being in a teacher shoes", "Explain to your high-school nephew"
  
  

• [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)
  Possible assignments: "Being in a teacher shoes", "Explain to your high-school nephew"

• [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
  Possible assignments: "Being in a teacher shoes", "Explain to your high-school nephew", "wake up the blogger in you"