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02/14/2022
Two Gaussian regularization methods for time-varying networks
We model time-varying network data as realizations from multivariate Gau...
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02/07/2022
Dependence model assessment and selection with DecoupleNets
Neural networks are suggested for learning a map from d-dimensional samp...
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12/02/2021
RafterNet: Probabilistic predictions in multi-response regression
A fully nonparametric approach for making probabilistic predictions in m...
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01/21/2021
Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey
Defensive deception is a promising approach for cyberdefense. Although d...
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01/08/2021
Foureye: Defensive Deception based on Hypergame Theory Against Advanced Persistent Threats
Defensive deception techniques have emerged as a promising proactive def...
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12/15/2020
Applications of multivariate quasi-random sampling with neural networks
Generative moment matching networks (GMMNs) are suggested for modeling t...
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08/07/2020
Role-Based Deception in Enterprise Networks
Historically, enterprise network reconnaissance is an active process, of...
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02/25/2020
Multivariate time-series modeling with generative neural networks
Generative moment matching networks (GMMNs) are introduced as dependence...
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02/21/2020
Optimizing Vulnerability-Driven Honey Traffic Using Game Theory
Enterprises are increasingly concerned about adversaries that slowly and...
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11/01/2018
Quasi-random number generators for multivariate distributions based on generative neural networks
Generative moment matching networks are introduced as quasi-random numbe...
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02/16/2018
High-dimensional covariance matrix estimation using a low-rank and diagonal decomposition
We study high-dimensional covariance/precision matrix estimation under t...
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01/11/2018
A framework for measuring dependence between random vectors
A framework for quantifying dependence between random vectors is introdu...
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04/26/2017
Pruning variable selection ensembles
In the context of variable selection, ensemble learning has gained incre...
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12/12/2014
Expanded Alternating Optimization of Nonconvex Functions with Applications to Matrix Factorization and Penalized Regression
We propose a general technique for improving alternating optimization (A...
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07/24/2013
When is the majority-vote classifier beneficial?
In his seminal work, Schapire (1990) proved that weak classifiers could ...
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10/20/2012
Content-boosted Matrix Factorization Techniques for Recommender Systems
Many businesses are using recommender systems for marketing outreach. Re...
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01/22/2010
Classifying Network Data with Deep Kernel Machines
Inspired by a growing interest in analyzing network data, we study the p...
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04/08/2008
On the underestimation of model uncertainty by Bayesian K-nearest neighbors
When using the K-nearest neighbors method, one often ignores uncertainty...
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12/06/2007