18 citations · 32 across the 15 of their papers we have counts for
4 papers · 1 filter
Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection
Afek Ilay Adler, Amichai Painsky
Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state of the art results in many prediction tasks. Despite its popularity, the GBM fr…
Lossless (and Lossy) Compression of Random Forests
Amichai Painsky, Saharon Rosset
Ensemble methods are among the state-of-the-art predictive modeling approaches. Applied to modern big data, these methods often require a large number of sub-learners, where the co…
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach
Amichai Painsky, Meir Feder, Naftali Tishby
Canonical Correlation Analysis (CCA) is a linear representation learning method that seeks maximally correlated variables in multi-view data. Non-linear CCA extends this notion to…
Gaussian Lower Bound for the Information Bottleneck Limit
Amichai Painsky, Naftali Tishby
The Information Bottleneck (IB) is a conceptual method for extracting the most compact, yet informative, representation of a set of variables, with respect to the target. It genera…