19 citations · 23 across the 4 of their papers we have counts for
5 papers
A Unified View of Localized Kernel Learning
John Moeller, Sarathkrishna Swaminathan, Suresh Venkatasubramanian
Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to learn not only a classifier/regressor but also the best kernel for the training task, usually from a com…
Certifying and removing disparate impact
Michael Feldman, Sorelle Friedler, John Moeller +2
What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcome…
A Geometric Algorithm for Scalable Multiple Kernel Learning
John Moeller, Parasaran Raman, Avishek Saha +1
We present a geometric formulation of the Multiple Kernel Learning (MKL) problem. To do so, we reinterpret the problem of learning kernel weights as searching for a kernel that max…
Approximate Bregman near neighbors in sublinear time: Beyond the triangle inequality
Amirali Abdullah, John Moeller, Suresh Venkatasubramanian
In this paper we present the first provable approximate nearest-neighbor (ANN) algorithms for Bregman divergences. Our first algorithm processes queries in O(log^d n) time using O(…
Computing Hulls And Centerpoints In Positive Definite Space
P. Thomas Fletcher, John Moeller, Jeff M. Phillips +1
In this paper, we present algorithms for computing approximate hulls and centerpoints for collections of matrices in positive definite space. There are many applications where the…