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ORCIDDSW 2018: Lausanne, Switzerland
- 2018 IEEE Data Science Workshop, DSW 2018, Lausanne, Switzerland, June 4-6, 2018. IEEE 2018, ISBN 978-1-5386-4410-2
Scalability in Data Sciences Analysis
- Bernhard Schmitzer
:
Multi-Scale Algorithms for Optimal Transport. 1-5 - Nicolai Meinshausen:
Causality from a Distributional Robustness Point of View. 6-10
Data Science Theory
- Christo Kurisummoottil Thomas, Dirk T. M. Slock:
SAVE - Space Alternating Variational Estimation for Sparse Bayesian Learning. 11-15 - Xinyue Shen, Yuchen Jiao, Yuantao Gu:
Subspace Data Visualization with Dissimilarity Based on Principal Angle. 16-20 - Zhixiong Yang, Waheed U. Bajwa:
BYRDIE: A Byzantine-Resilient Distributed Learning Algorithm. 21-25
Poster 1
- Stephen Kruzick, José M. F. Moura:
Spectral Statistics of Directed Networks with Random Link Model Transpose-Asymmetry. 26-30 - Yating Liu, Yuantao Gu:
A Novel Backbone Network Anomaly Detector via Clustering in Sketch Space. 31-35 - Sophie Mathieu, Rainer von Sachs, Véronique Delouille, Laure Lefevre:
Uncertainty Quantification in Sunspot Counts. 36-40 - Sylvain Le Corff, Alain Champagne, Maurice Charbit, Gilles Nozière, Eric Moulines:
Optimizing Thermal Comfort and Energy Consumption in a Large Building without Renovation Work. 41-45 - Chenxi Sun, Tongxin Li, Victor O. K. Li:
Robust and Consistent Clustering Recovery via SDP Approaches. 46-50 - Sergio Barbarossa, Stefania Sardellitti, Elena Ceci:
Learning from Signals Defined over Simplicial Complexes. 51-55 - Topi Halme, Visa Koivunen:
Distributed Nonparametric Inference Using a One-Sample Bootstrapped Anderson-Darling Test and P-Value Fusion. 56-60 - Addison W. Bohannon, Brian M. Sadler, Radu V. Balan:
Learning Flexible Representations of Stochastic Processes on Graphs. 61-65 - Timo Huuhtanen, Alexander Jung:
Predictive Maintenance of Photovoltaic Panels via Deep Learning. 66-70 - Elin Farnell, Henry Kvinge, Michael Kirby, Chris Peterson:
Endmember Extraction on the Grassmannian. 71-75 - Bhanukiran Vinzamuri, Kush R. Varshney:
False Discovery Rate Control with Concave Penalties Using Stability Selection. 76-80 - Praneeth Narayanamurthy, Namrata Vaswani:
Nearly Optimal Robust Subspace Tracking: A Unified Approach. 81-85 - Gen Li, Qinghua Liu, Yuantao Gu:
Restricted Isometry Property for Low-Dimensional Subspaces and its Application in Compressed Subspace Clustering. 86-90
Poster 2
- Keith Knight:
Subsampling Least Squares and Elemental Estimation. 91-94 - Michael Murray, Jared Tanner:
Deep CNN Sparse Coding Analysis: Towards Average Case. 95-99 - Armin Eftekhari, Jared Tanner, Andrew Thompson, Bogdan Toader, Hemant Tyagi:
Non-Negative Super-Resolution is Stable. 100-104 - Ran Xin, Chenguang Xi, Usman A. Khan:
Subgradient Projection Over Directed Graphs Using Surplus Consensus. 105-109 - Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov:
Vector Compression for Similarity Search Using Multi-Layer Sparse Ternary Codes. 110-114 - Yuchen Jiao, Xinyue Shen, Gen Li, Yuantao Gu:
Subspace Principal Angle Preserving Property of Gaussian Random Projection. 115-119 - Arya Farahi, Jonathan C. Stroud:
The Michigan Data Science Team: A Data Science Education Program with Significant Social Impact. 120-124 - Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav S. Sukhatme:
Profit Maximizing Logistic Regression Modeling for Credit Scoring. 125-129 - Kiwon Lee, Yong Hoon Lee, Changho Suh:
Alternating Autoencoders for Matrix Completion. 130-134 - Hashem Parvin, Parham Moradi, Shahrokh Esmaeili, Mahdi Jalili:
An Efficient Recommender System by Integrating Non-Negative Matrix Factorization with Trust and Distrust Relationships. 135-139 - Catherine Stamoulis:
Sparse Anomaly Representations in Very High-Dimensional Brain Signals. 140-144 - Roope Tervo, Joonas Karjalainen, Alexander Jung:
Predicting Electricity Outages Caused By Convective Storms. 145-149 - Victoria Stodden, Xiaomian Wu, Vanessa V. Sochat:
AIM: An Abstraction for Improving Machine Learning Prediction. 150-154 - Hoi-To Wai, Anna Scaglione, Baruch Barzel, Amir Leshem:
Network Inference from Complex Systems Steady States Observations: Theory and Methods. 155-159
Learning
- Aniket Anand Deshmukh, Emil Laftchiev:
Semi-Supervised Transfer Learning Using Marginal Predictors. 160-164 - Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis:
Semi-Blind Inference of Topologies and Signals over Graphs. 165-169 - Augusto Santos, Vincenzo Matta, Ali H. Sayed:
Divide-and-Conquer Tomography for Large-Scale Networks. 170-174 - Carla Tameling, Axel Munk:
Computational Strategies for Statistical Inference Based on Empirical Optimal Transport. 175-179 - Zachary Charles, Amin Jalali, Rebecca Willett:
Sparse Subspace Clustering with Missing and Corrupted Data. 180-184 - Bicheng Ying, Kun Yuan, Ali H. Sayed:
An Exponentially Convergent Algorithm for Learning Under Distributed Features. 185-189
Network Topology Interence
- Stefan Vlaski, Hermina Petric Maretic, Roula Nassif, Pascal Frossard, Ali H. Sayed:
Online Graph Learning from Sequential Data. 190-194 - Yanning Shen, Georgios B. Giannakis:
Online Identification of Directional Graph Topologies Capturing Dynamic and Nonlinear Dependencies. 195-199 - Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus:
Sparsest Network Support Estimation: A Submodular Approach. 200-204 - Keng-Shih Lu, Eduardo Pavez, Antonio Ortega:
On Learning Laplacians of Tree Structured Graphs. 205-209 - Rasoul Shafipour, Santiago Segarra, Antonio G. Marques, Gonzalo Mateos:
Directed Network Topology Inference via Graph Filter Identification. 210-214 - Yue Zhao, Jianshu Chen, H. Vincent Poor:
Learning to Infer Power Grid Topologies: Performance and Scalability. 215-219
CNNs for Graph Data
- Fernando Gama, Geert Leus, Antonio G. Marques, Alejandro Ribeiro:
Convolutional Neural Networks via Node-Varying Graph Filters. 220-224 - Federico Monti, Karl Otness, Michael M. Bronstein:
MOTIFNET: A Motif-Based Graph Convolutional Network for Directed Graphs. 225-228 - Alex Nowak, Soledad Villar, Afonso S. Bandeira, Joan Bruna:
Revised Note on Learning Quadratic Assignment with Graph Neural Networks. 229-233 - Carlos Eduardo Rosar Kós Lassance, Jean-Charles Vialatte, Vincent Gripon:
Matching Convolutional Neural Networks without Priors about Data. 234-238 - Jian Du, John Shi, Soummya Kar, José M. F. Moura:
On Graph Convolution for Graph CNNs. 239-243 - Mathias Niepert, Alberto García-Durán:
Towards a Spectrum of Graph Convolutional Networks. 244-248
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