📚 Bibliography

A non-exhaustive list of references for the implemented learning algorithms and query strategies.

Learning Algorithms

• Logistic Regression (LR): Berkson (1944)

• k-Nearest Neighbors (kNN): Fix et al. (1952)

• Multi-Layer Perceptron (MLP): Werbos (1974)

• Naive Bayes (NB): Kononenko (1990)

• Support Vector Machine (SVM): Boser et al. (1992)

• Random Forest (RF): Breiman (2001)

• Extra Trees (ET): Geurts et al. (2006)

• XGBoost (XGB): Chen and Guestrin (2016)

• CatBoost (CB): Dorogush et al. (2018)

• TabNet: Arik and Pfister (2021)

• TabPFN: Hollmann et al. (2023)

Query Strategies

• Entropy Sampling (ES): Shannon (1948)

• Query by Committee (QBC): Seung et al. (1992)

• Variance Reduction (VR): Cohn et al. (1993)

• Least Confidence (LC): Lewis and Gale (1994)

• Margin Sampling (MS): Scheffer et al. (2001)

• Expected Error Reduction (EER): Roy & McCallum (2001)

• BALD: Houlsby et al. (2011)

• Max Entropy (MaxEnt): Gal et al. (2017)

• Query by Committee (Variance Ratio): Beluch et al. (2018)

• Epistemic Uncertainty Sampling (EUS): Nguyen et al. (2019)

• Power Margin Sampling (PowMS): Kirsch et al. (2021)

• Min Margin Sampling (MinMS): Jiang et al. (2021)

• K-Means: Kang et al. (2004)

• CoreSet: Sener & Savarese (2018)

• Typical Clustering (TypClust): Hacohen et al. (2022)

• FALCUN: Gilhuber et al. (2024)

• Cluster Margin Sampling (CluMS): Citovsky et al. (2021)

• CLUE: Prabhu et al. (2021)