9 citations · 24 across the 6 of their papers we have counts for
6 papers
MBA: Mini-Batch AUC Optimization
San Gultekin, Avishek Saha, Adwait Ratnaparkhi +1
Area under the receiver operating characteristics curve (AUC) is an important metric for a wide range of signal processing and machine learning problems, and scalable methods for o…
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible…
NLARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
Makoto Yamada, Avishek Saha, Hua Ouyang +2
We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of t…
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…
Efficient Protocols for Distributed Classification and Optimization
Hal Daume, Jeff M. Phillips, Avishek Saha +1
In distributed learning, the goal is to perform a learning task over data distributed across multiple nodes with minimal (expensive) communication. Prior work (Daume III et al., 20…
Protocols for Learning Classifiers on Distributed Data
Hal Daume, Jeff M. Phillips, Avishek Saha +1
We consider the problem of learning classifiers for labeled data that has been distributed across several nodes. Our goal is to find a single classifier, with small approximation e…