6 papers
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles
Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder
Decision tree ensembles are widely used and competitive learning models. Despite their success, popular toolkits for learning tree ensembles have limited modeling capabilities. For…
Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives
Hussein Hazimeh, Rahul Mazumder, Peter Radchenko
We present a new algorithmic framework for grouped variable selection that is based on discrete mathematical optimization. While there exist several appealing approaches based on c…
Sparse Regression at Scale: Branch-and-Bound rooted in First-Order Optimization
Hussein Hazimeh, Rahul Mazumder, Ali Saab
We consider the least squares regression problem, penalized with a combination of the and squared penalty functions (a.k.a. regularization). R…
The Tree Ensemble Layer: Differentiability meets Conditional Computation
Hussein Hazimeh, Natalia Ponomareva, Petros Mol +2
Neural networks and tree ensembles are state-of-the-art learners, each with its unique statistical and computational advantages. We aim to combine these advantages by introducing a…
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
Hussein Hazimeh, Rahul Mazumder
In many learning settings, it is beneficial to augment the main features with pairwise interactions. Such interaction models can be often enhanced by performing variable selection…
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms
Hussein Hazimeh, Rahul Mazumder
The -regularized least squares problem (a.k.a. best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, mac…