MBA: Mini-Batch AUC Optimization

no code implementations • 29 May 2018 • San Gultekin, Avishek Saha, Adwait Ratnaparkhi, John Paisley

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 optimizing AUC have recently been proposed.

Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data

no code implementations • 14 Aug 2016 • Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez, Ermin Hodzic, Raunak Shrestha, Avishek Saha, Hua Ouyang, Dawei Yin, Hiroshi Mamitsuka, Cenk Sahinalp, Predrag Radivojac, Filippo Menczer, Yi Chang

However, sophisticated learning models are computationally unfeasible for data with millions of features.

Cloud Computing Dimensionality Reduction +2

N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data

no code implementations • 10 Nov 2014 • Makoto Yamada, Avishek Saha, Hua Ouyang, Dawei Yin, Yi Chang

We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way.

Distributed Computing feature selection +1

A Geometric Algorithm for Scalable Multiple Kernel Learning

no code implementations • 25 Jun 2012 • John Moeller, Parasaran Raman, Avishek Saha, Suresh Venkatasubramanian

We present a geometric formulation of the Multiple Kernel Learning (MKL) problem.

Co-regularization Based Semi-supervised Domain Adaptation

no code implementations • NeurIPS 2010 • Abhishek Kumar, Avishek Saha, Hal Daume

This paper presents a co-regularization based approach to semi-supervised domain adaptation.

Domain Adaptation Generalization Bounds +2