31 citations · 61 across the 12 of their papers we have counts for
12 papers
Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks
Ivan Garibay, Toktam A. Oghaz, Niloofar Yousefi +23
This paper explains the design of a social network analysis framework, developed under DARPA's SocialSim program, with novel architecture that models human emotional, cognitive and…
Learning Hash Function through Codewords
Yinjie Huang, Michael Georgiopoulos, Georgios C. Anagnostopoulos
In this paper, we propose a novel hash learning approach that has the following main distinguishing features, when compared to past frameworks. First, the codewords are utilized in…
Reduced-Rank Local Distance Metric Learning for k-NN Classification
YInjie Huang, Cong Li, Michael Georgiopoulos +1
We propose a new method for local distance metric learning based on sample similarity as side information. These local metrics, which utilize conical combinations of metric weight…
Multi-Task Learning Using Neighborhood Kernels
Niloofar Yousefi, Cong Li, Mansooreh Mollaghasemi +2
This paper introduces a new and effective algorithm for learning kernels in a Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here, our approach can be easi…
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning
Niloofar Yousefi, Yunwen Lei, Marius Kloft +2
We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), using which we establish sharp excess risk bounds for MTL in terms of distribution- and data-depend…
Hash Function Learning via Codewords
Yinjie Huang, Michael Georgiopoulos, Georgios C. Anagnostopoulos
In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches. First, it utilizes codewords in the Hamming…
Multi-Task Learning with Group-Specific Feature Space Sharing
Niloofar Yousefi, Michael Georgiopoulos, Georgios C. Anagnostopoulos
When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance. Multi-Task…
Conic Multi-Task Classification
Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Averag…
Pareto-Path Multi-Task Multiple Kernel Learning
Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) share…
A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems
Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Over the past few years, Multi-Kernel Learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning. MKL…
Multi-Task Classification Hypothesis Space with Improved Generalization Bounds
Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
This paper presents a RKHS, in general, of vector-valued functions intended to be used as hypothesis space for multi-task classification. It extends similar hypothesis spaces that…
Kernel-based Distance Metric Learning in the Output Space
Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
In this paper we present two related, kernel-based Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output spac…