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Tensor Embedding: A Supervised Framework for Human Behavioral Data Mining and Prediction
Today's densely instrumented world offers tremendous opportunities for c...
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Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails
Robots hold promise in many scenarios involving outdoor use, such as sea...
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Stochastic Substitute Training: A Gray-box Approach to Craft Adversarial Examples Against Gradient Obfuscation Defenses
It has been shown that adversaries can craft example inputs to neural ne...
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"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
To support human decision making with machine learning models, we often ...
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On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection
Humans are the final decision makers in critical tasks that involve ethi...
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Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness
Various domain users are increasingly leveraging real-time social media ...
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Equal Opportunity in Online Classification with Partial Feedback
We study an online classification problem with partial feedback in which...
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Decentralized & Collaborative AI on Blockchain
Machine learning has recently enabled large advances in artificial intel...
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Wandering Within a World: Online Contextualized Few-Shot Learning
We aim to bridge the gap between typical human and machine-learning envi...
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A tunable multiresolution smoother for scattered data with application to particle filtering
A smoothing algorithm is presented that can reduce the small-scale conte...
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Online Red Packets: A Large-scale Empirical Study of Gift Giving on WeChat
Gift giving is a ubiquitous social phenomenon, and red packets have been...
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Discrete Event, Continuous Time RNNs
We investigate recurrent neural network architectures for event-sequence...
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"Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
As technology becomes more advanced, those who design, use and are other...
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Characterizing the structural diversity of complex networks across domains
The structure of complex networks has been of interest in many scientifi...
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Improving Human-Machine Cooperative Visual Search With Soft Highlighting
Advances in machine learning have produced systems that attain human-lev...
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"I can assure you [...] that it's going to be all right" -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships
As technology become more advanced, those who design, use and are otherw...
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Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge
Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is demo...
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Improved Fixed-Rank Nyström Approximation via QR Decomposition: Practical and Theoretical Aspects
The Nyström method is a popular technique for computing fixed-rank appro...
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How deep is knowledge tracing?
In theoretical cognitive science, there is a tension between highly stru...
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A Neural Framework for Generalized Topic Models
Topic models for text corpora comprise a popular family of methods that ...
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Classifying Graphs as Images with Convolutional Neural Networks
The task of graph classification is currently dominated by graph kernels...
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Hybrid Repeat/Multi-point Sampling for Highly Volatile Objective Functions
A key drawback of the current generation of artificial decision-makers i...
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Randomized Clustered Nystrom for Large-Scale Kernel Machines
The Nystrom method has been popular for generating the low-rank approxim...
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A Signaling Game Approach to Databases Querying and Interaction
As most database users cannot precisely express their information needs,...
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The ground truth about metadata and community detection in networks
Across many scientific domains, there is a common need to automatically ...
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Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game
Interpersonal relations are fickle, with close friendships often dissolv...
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Dual Smoothing and Level Set Techniques for Variational Matrix Decomposition
We focus on the robust principal component analysis (RPCA) problem, and ...
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Estimation of the sample covariance matrix from compressive measurements
This paper focuses on the estimation of the sample covariance matrix fro...
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Preconditioned Data Sparsification for Big Data with Applications to PCA and K-means
We analyze a compression scheme for large data sets that randomly keeps ...
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Detectability thresholds and optimal algorithms for community structure in dynamic networks
We study the fundamental limits on learning latent community structure i...
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Efficient Dictionary Learning via Very Sparse Random Projections
Performing signal processing tasks on compressive measurements of data h...
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A unified view of generative models for networks: models, methods, opportunities, and challenges
Research on probabilistic models of networks now spans a wide variety of...
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An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length
Bayesian networks are convenient graphical expressions for high dimensio...
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Learning Latent Block Structure in Weighted Networks
Community detection is an important task in network analysis, in which w...
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Detecting change points in the large-scale structure of evolving networks
Interactions among people or objects are often dynamic in nature and can...
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Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective
We represent planning as a set of loosely coupled network flow problems,...
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Active Discovery of Network Roles for Predicting the Classes of Network Nodes
Nodes in real world networks often have class labels, or underlying attr...
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Adapting the Stochastic Block Model to Edge-Weighted Networks
We generalize the stochastic block model to the important case in which ...
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Inverting Nonlinear Dimensionality Reduction with Scale-Free Radial Basis Function Interpolation
Nonlinear dimensionality reduction embeddings computed from datasets do ...
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Keeping greed good: sparse regression under design uncertainty with application to biomass characterization
In this paper, we consider the classic measurement error regression scen...
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Online SLAM with Any-time Self-calibration and Automatic Change Detection
A framework for online simultaneous localization, mapping and self-calib...
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Simultaneous Localization, Mapping, and Manipulation for Unsupervised Object Discovery
We present an unsupervised framework for simultaneous appearance-based o...
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Non-Asymptotic Analysis of Tangent Space Perturbation
Constructing an efficient parameterization of a large, noisy data set of...
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A random walk on image patches
In this paper we address the problem of understanding the success of alg...
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Adapting to Non-stationarity with Growing Expert Ensembles
When dealing with time series with complex non-stationarities, low retro...
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Coherence, Belief Expansion and Bayesian Networks
We construct a probabilistic coherence measure for information sets whic...
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Locality and low-dimensions in the prediction of natural experience from fMRI
Functional Magnetic Resonance Imaging (fMRI) provides dynamical access i...
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Low Dimensional Embedding of fMRI datasets
We propose a novel method to embed a functional magnetic resonance imagi...
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Autocomplete Textures for 3D Printing
Texture is an essential property of physical objects that affects aesthe...
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Coping with Construals in Broad-Coverage Semantic Annotation of Adpositions
We consider the semantics of prepositions, revisiting a broad-coverage a...
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