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cs.LG2026
Vectorized Adaptive Histograms for Sparse Oblique Forests
Ariel Lubonja, Jungsang Yoon, Haoyin Xu +6
Classification using sparse oblique random forests provides guarantees on uncertainty and confidence while controlling for specific error types. However, they use more data and mor…
cs.LG2024
Generative Forests
Richard Nock, Mathieu Guillame-Bert
We focus on generative AI for a type of data that still represent one of the most prevalent form of data: tabular data. Our paper introduces two key contributions: a new powerful c…
cs.LG2024
Boosting gets full Attention for Relational Learning
Mathieu Guillame-Bert, Richard Nock
More often than not in benchmark supervised ML, tabular data is flat, i.e. consists of a single (rows, columns) file, but cases abound in the real world where observat…