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ORCIDDSW 2019: Minneapolis, MN, USA
- IEEE Data Science Workshop, DSW 2019, Minneapolis, MN, USA, June 2-5, 2019. IEEE 2019, ISBN 978-1-7281-0708-0
Tensor Factorizations
- Georgios Tsitsikas, Evangelos E. Papalexakis
:
The Core Consistency of a Compressed Tensor. 1-5 - Alexander Ritchie, Clayton D. Scott, Laura Balzano, Daniel Kessler, Chandra Sekhar Sripada:
Supervised Principal Component Analysis Via Manifold Optimization. 6-10 - Genevera I. Allen, Michael Weylandt:
Sparse and Functional Principal Components Analysis. 11-16 - Chang Ye, Gonzalo Mateos:
Online Tensor Decomposition and Imputation for Count Data. 17-21
Distributed Algorithms
- Or Ohev Shalom, Amir Leshem, Anna Scaglione:
Localization of Data Injection Attacks on Distributed M-Estimation. 22-26 - Davoud Ataee Tarzanagh, Mohamad Kazem Shirani Faradonbeh, George Michailidis:
Online Distributed Estimation of Principal Eigenspaces. 27-31 - Ping Xu, Zhi Tian, Zhe Zhang, Yue Wang:
Coke: Communication-Censored Kernel Learning Via Random Features. 32-36 - Fakhteh Saadatniaki, Ran Xin, Usman A. Khan:
Distributed Training with Mobile Agents: Optimization Over Dynamic Directed Graphs. 37-41 - Emre Ozfatura, Deniz Gündüz, Sennur Ulukus:
Gradient Coding with Clustering and Multi-Message Communication. 42-46 - Anuththara Rupasinghe, Behtash Babadi:
Multitaper Analysis of Evolutionary Spectral Density Matrix From Multivariate Spiking Observations. 47-51
Graph Learning
- Mahmoud Ramezani-Mayiami, Mohammad Hajimirsadeghi, Karl Skretting, Rick S. Blum, H. Vincent Poor:
Graph Topology Learning and Signal Recovery Via Bayesian Inference. 52-56 - Andersen Chang, Tianyi Yao, Genevera I. Allen:
Graphical Models and Dynamic Latent Factors for Modeling Functional Brain Connectivity. 57-63 - Qin Lu, Vassilis N. Ioannidis, Georgios B. Giannakis:
Semi-Supervised Tracking of Dynamic Processes Over Switching Graphs. 64-68 - Brandon Oselio, Alfred O. Hero III, Amir Sadeghian, Silvio Savarese:
Time-Varying Interaction Estimation Using Ensemble Methods. 69-75 - Tianyi Yao, Genevera I. Allen:
Clustered Gaussian Graphical Model Via Symmetric Convex Clustering. 76-82 - Yanning Shen, Geert Leus:
Scalable Learning with Privacy Over Graphs. 83-87
Anomaly Detection
- Gayathri R. Prabhu, Srikrishna Bhashyam, Aditya Gopalan, Rajesh Sundaresan:
Learning to Detect an Anomalous Target with Observations from an Exponential Family. 88-92 - Karl Pazdernik, Bryan Stanfill, Lisa Bramer, Kellie J. MacPhee:
Simultaneous Multivariate Outlier and Trend Detection. 93-99 - Patrick Schlachter, Yiwen Liao, Bin Yang:
Deep One-Class Classification Using Intra-Class Splitting. 100-104 - Topi Halme, Martin Gölz, Visa Koivunen:
Bayesian Multiple Hypothesis Testing For Distributed Detection In Sensor Networks. 105-109
Learning, Modeling and Inference with Data I
- Michael Krikheli, Amir Leshem:
Lfinite Sample Bounds on the Performance of Weighted Linear Least Squares in Sub-Gaussian Correlated Noise. 110-114 - Mario Coutino, Geert Leus:
Asynchronous Distributed Edge-Variant Graph Filters. 115-119 - Lenin Arango-Castillo, Glen Takahara:
Long-Range Dependence Parameter Estimation For Mixed Spectra Gaussian Processes. 120-124 - Carlos Eduardo Rosar Kós Lassance, Vincent Gripon, Jian Tang, Antonio Ortega:
Structural Robustness for Deep Learning Architectures. 125-129 - Panagiotis A. Traganitis:
Blind Ensemble Classification of Sequential Data. 130-134 - Sara Mourad, Haris Vikalo, Ahmed H. Tewfik:
Online selective training for faster neural network learning. 135-139
Graph Signal Processing
- Rasoul Shafipour, Abolfazl Hashemi, Gonzalo Mateos, Haris Vikalo:
Online Topology Inference from Streaming Stationary Graph Signals. 140-144 - Daniel B. Burkhardt, Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy:
Vertex-Frequency Clustering. 145-149 - Alejandro Parada-Mayorga, Daniel L. Lau, Jhony H. Giraldo, Gonzalo R. Arce:
Sampling of Graph Signals with Blue Noise Dithering. 150-154 - Dingyu Wang, John Lipor, Gautam Dasarathy:
Distance-Penalized Active Learning via Markov Decision Processes. 155-159 - Myriam Bontonou, Carlos Eduardo Rosar Kós Lassance, Ghouthi Boukli Hacene, Vincent Gripon, Jian Tang, Antonio Ortega:
Introducing Graph Smoothness Loss for Training Deep Learning Architectures. 160-164 - Dominique Guillot, Alejandro Parada-Mayorga, Sebastian M. Cioaba, Gonzalo R. Arce:
Optimal Sampling Sets in Cographs. 165-169
Learning, Modeling and Inference with Data II
- Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems. 170-174 - Seung-Jun Kim, Rami Mowakeaa:
Kernel-Based Efficient Lifelong Learning Algorithm. 175-179 - Rebecca Chen, Lav R. Varshney:
Non-Negative Matrix Factorization of Clustered Data with Missing Values. 180-184 - Sina Miran, Jonathan Z. Simon, Michael C. Fu, Steven I. Marcus, Behtash Babadi:
Estimation of State-Space Models with Gaussian Mixture Process Noise. 185-189
Applications in Social Media and Recommendation Systems
- Imara Nazar, Daphney-Stavroula Zois, Mengfan Yao:
A Hierarchical Approach for Timely Cyberbullying Detection. 190-195 - Tien Huu Do, Xiao Luo, Duc Minh Nguyen, Nikos Deligiannis:
Rumour Detection Via News Propagation Dynamics and User Representation Learning. 196-200 - Davis Gilton, Greg Ongie, Rebecca Willett:
Learning to Regularize Using Neumann Networks. 201-207 - Sarod Yatawatta, Lukas De Clercq, Hanno Spreeuw, Faruk Diblen:
A Stochastic LBFGS Algorithm for Radio Interferometric Calibration. 208-212
Applications in Biology and Medicine
- Charles R. Hatt, Sundaresh Ram, Craig J. Galbán:
A Convolutional Neural Network Approach to Automated Lung Bounding Box Estimation from Computed Tomography Scans. 213-216 - Jiankun Wang, Shahrokh Valaee:
Enhancing Medical Imaging Semantic Segmentation Using the Digital Annealer. 217-221 - Shih-Gu Huang, Moo K. Chung, Ian C. Carroll, H. Hill Goldsmith:
Dynamic Functional Connectivity Using Heat Kernel. 222-226 - Giancarlo A. Antonucci, Simon Vary, David Humphreys, Robert A. Lamb, Jonathan Piper, Jared Tanner:
Multispectral Snapshot Demosaicing Via Non-Convex Matrix Completion. 227-231 - Sean Mobilia, Birsen Sirkeci-Mergen, Joshua Deal, Thomas C. Rich, Silas J. Leavesley:
Classification of Hyperspectral Colon Cancer Images Using Convolutional Neural Networks. 232-236
Topics in Data Science
- Michael Weylandt:
Splitting Methods For Convex Bi-Clustering And Co-Clustering. 237-242 - Huozhi Zhou, Ashish Jagmohan, Lav R. Varshney:
Generalized Jordan Center: A Source Localization Heuristic For Noisy And Incomplete Observations. 243-247 - Liam Madden, Stephen Becker, Emiliano Dall'Anese:
Online Sparse Subspace Clustering. 248-252 - Robiul Hossain Md. Rafi, Ali Cafer Gürbüz:
Data Driven Measurement Matrix Learning for Sparse Reconstruction. 253-257
Data Analytics for Power Systems
- Liang Zhang, Gang Wang, Georgios B. Giannakis:
Distribution System State Estimation Via Data-Driven and Physics-Aware Deep Neural Networks. 258-262 - Cristian Genes, Iñaki Esnaola, Samir M. Perlaza, Daniel Coca:
Recovery of Missing Data in Correlated Smart Grid Datasets. 263-269 - Jiaming Li, Yue Zhao, Young-Hwan Lee, Seung-Jun Kim:
Learning to Infer Voltage Stability Margin Using Transfer Learning. 270-274 - Raksha Ramakrishna, Anna Scaglione:
On Modeling Voltage Phasor Measurements as Graph Signals. 275-279 - Dorcas Ofori-Boateng, Asim Kumer Dey, Yulia R. Gel, Binghui Li, Jie Zhang, H. Vincent Poor:
Assessing the Resilience of the Texas Power Grid Network. 280-284 - Dario Bauso, Toru Namerikawa:
Data-Driven Mean-Field Game Approximation for a Population of Electric Vehicles. 285-289
Omics Data Processing and Analysis Tensor Methods
Tensor Methods
- Magda Amiridi, Nikos Kargas, Nicholas D. Sidiropoulos:
Statistical Learning Using Hierarchical Modeling of Probability Tensors. 290-294 - Kejun Huang, Zhuoran Yang, Zhaoran Wang, Mingyi Hong:
Learning Partially Observable Markov Decision Processes Using Coupled Canonical Polyadic Decomposition. 295-299 - Shahana Ibrahim, Xiao Fu:
Stochastic Optimization for Coupled Tensor Decomposition with Applications in Statistical Learning. 300-304 - Ali Koochakzadeh, Piya Pal:
Canonical Polyadic (CP) Decomposition of Structured Semi-Symmetric Fourth-Order Tensors. 305-309 - Nico Vervliet, Michiel Vandecappelle, Martijn Boussé, Rob Zink, Lieven De Lathauwer:
Recent Numerical and Conceptual Advances for Tensor Decompositions - A Preview of Tensorlab 4.0. 310-314
Statistical Inference in Complex Network Data
Nonconvex Optimization for Data Science
- Songtao Lu, Xinwei Zhang, Haoran Sun, Mingyi Hong:
GNSD: a Gradient-Tracking Based Nonconvex Stochastic Algorithm for Decentralized Optimization. 315-321 - Xuechao He, Qing Ling, Tianyi Chen:
Byzantine-Robust Stochastic Gradient Descent for Distributed Low-Rank Matrix Completion. 322-326 - Babak Barazandeh, Meisam Razaviyayn, Maziar Sanjabi:
Training Generative Networks Using Random Discriminators. 327-332 - Man-Wai Un, Mingjie Shao, Wing-Kin Ma, P. C. Ching:
Deep Mimo Detection Using ADMM Unfolding. 333-337 - Aria Ameri, Arindam Bose, Mojtaba Soltanalian:
Comprehensive Personalized Ranking Using One-Bit Comparison Data. 338-342
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