307 citations · 693 across the 70 of their papers we have counts for
6 papers · 1 filter
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning
Haoyue Wang, Haihao Lu, Rahul Mazumder
The Frank-Wolfe (FW) method is a popular algorithm for solving large-scale convex optimization problems appearing in structured statistical learning. However, the traditional Frank…
Integration of Survival Data from Multiple Studies
Steffen Ventz, Rahul Mazumder, Lorenzo Trippa
We introduce a statistical procedure that integrates survival data from multiple biomedical studies, to improve the accuracy of predictions of survival or other events, based on in…
Subgradient Regularized Multivariate Convex Regression at Scale
Wenyu Chen, Rahul Mazumder
We present new large-scale algorithms for fitting a subgradient regularized multivariate convex regression function to samples in dimensions -- a key problem in shape const…
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 Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
Antoine Dedieu, Hussein Hazimeh, Rahul Mazumder
We consider a discrete optimization formulation for learning sparse classifiers, where the outcome depends upon a linear combination of a small subset of features. Recent work has…