
ASK: Adversarial Soft kNearest Neighbor Attack and Defense
KNearest Neighbor (kNN)based deep learning methods have been applied t...
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Immunomimetic Deep Neural Networks (ImmunoNet)
Biomimetics has played a key role in the evolution of artificial neural ...
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SGPALM: a Fast Physically Interpretable Tensor Graphical Model
We propose a new graphical model inference procedure, called SGPALM, fo...
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Achievable Resolution Limits for the Noisy Adaptive 20 Questions Problem
We study the achievable performance of adaptive query procedures for the...
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Resolution Limits of 20 Questions Search Strategies for Moving Targets
We establish fundamental limits of tracking a moving target over the uni...
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Resolution Limits of NonAdaptive 20 Questions Search for Multiple Targets
We study the problem of simultaneous search for multiple targets over a ...
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RAILS: A Robust Adversarial Immuneinspired Learning System
Adversarial attacks against deep neural networks are continuously evolvi...
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Approximate Weighted CR Coded Matrix Multiplication
One of the most common, but at the same time expensive operations in lin...
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SecondOrder Asymptotically Optimal Universal Outlying Sequence Detection with Reject Option
Motivated by practical machine learning applications, we revisit the out...
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Resolution Limits of NonAdaptive Querying for Noisy 20 Questions Estimation
We study fundamental limits of estimation accuracy for the noisy 20 ques...
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The Sylvester Graphical Lasso (SyGlasso)
This paper introduces the Sylvester graphical lasso (SyGlasso) that capt...
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Resolution Limits of Noisy 20 Questions Estimation
We establish fundamental limits on estimation accuracy for the noisy 20 ...
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Learning to Benchmark: Determining Best Achievable Misclassification Error from Training Data
We address the problem of learning to benchmark the best achievable clas...
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A Hierarchical Bayesian Approach to Neutron Spectrum Unfolding with Organic Scintillators
We propose a hierarchical Bayesian model and stateofart Monte Carlo sa...
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TimeVarying Interaction Estimation Using Ensemble Methods
Directed information (DI) is a useful tool to explore timedirected inte...
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Enterprise Cyber Resiliency Against Lateral Movement: A Graph Theoretic Approach
Lateral movement attacks are a serious threat to enterprise security. In...
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Minimum Volume Topic Modeling
We propose a new topic modeling procedure that takes advantage of the fa...
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Hierarchical network models for structured exchangeable interaction processes
Network data often arises via a series of structured interactions among ...
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Exponential Strong Converse for Successive Refinement with Causal Decoder Side Information
We revisit the successive refinement problem with causal decoder side in...
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Latent heterogeneous multilayer community detection
We propose a method for simultaneously detecting shared and unshared com...
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Anomaly Detection in Partially Observed Traffic Networks
This paper addresses the problem of detecting anomalous activity in traf...
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Nonstationary Distance Metric Learning
Recent work in distance metric learning has focused on learning transfor...
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Multimodal MRI Neuroimaging with Motion Compensation Based on Particle Filtering
Head movement during scanning impedes activation detection in fMRI studi...
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Statistical Estimation and Clustering of Groupinvariant Orientation Parameters
We treat the problem of estimation of orientation parameters whose value...
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Coercive Regionlevel Registration for Multimodal Images
We propose a coercive approach to simultaneously register and segment mu...
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Twostage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS)
This paper proposes a general adaptive procedure for budgetlimited pred...
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Parameter estimation in spherical symmetry groups
This paper considers statistical estimation problems where the probabili...
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Detection of Anomalous Crowd Behavior Using SpatioTemporal Multiresolution Model and Kronecker Sum Decompositions
In this work we consider the problem of detecting anomalous spatiotempo...
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Predictive Correlation Screening: Application to Twostage Predictor Design in High Dimension
We introduce a new approach to variable selection, called Predictive Cor...
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Manifold Learning with Geodesic Minimal Spanning Trees
In the manifold learning problem one seeks to discover a smooth low dime...
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