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4 papers
Modeling Text with Decision Forests using Categorical-Set Splits
Mathieu Guillame-Bert, Sebastian Bruch, Petr Mitrichev +2
Decision forest algorithms typically model data by learning a binary tree structure recursively where every node splits the feature space into two sub-regions, sending examples int…
Learning Representations for Axis-Aligned Decision Forests through Input Perturbation
Sebastian Bruch, Jan Pfeifer, Mathieu Guillame-bert
Axis-aligned decision forests have long been the leading class of machine learning algorithms for modeling tabular data. In many applications of machine learning such as learning-t…
An Alternative Cross Entropy Loss for Learning-to-Rank
Sebastian Bruch
Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval. These algorithms learn to rank…
TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank
Rama Kumar Pasumarthi, Sebastian Bruch, Xuanhui Wang +7
Learning-to-Rank deals with maximizing the utility of a list of examples presented to the user, with items of higher relevance being prioritized. It has several practical applicati…