
Basic Principles of Clustering Methods
Clustering methods group a set of data points into a few coherent groups...
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Learning Networked Exponential Families with Network Lasso
The data arising in many important bigdata applications, ranging from s...
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Semisupervised Learning in NetworkStructured Data via Total Variation Minimization
We propose and analyze a method for semisupervised learning from partia...
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Localized Linear Regression in Networked Data
The network Lasso (nLasso) has been proposed recently as an efficient le...
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Classifying Partially Labeled Networked Data via Logistic Network Lasso
We apply the network Lasso to classify partially labeled data points whi...
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Analysis of Network Lasso For SemiSupervised Regression
We characterize the statistical properties of network Lasso for semisup...
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On the Duality between Network Flows and Network Lasso
The data arising in many application domains have an intrinsic network s...
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Classifying Process Instances Using Recurrent Neural Networks
Process Mining consists of techniques where logs created by operative sy...
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Clustering in Partially Labeled Stochastic Block Models via Total Variation Minimization
A main task in data analysis is to organize data points into coherent gr...
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On The Complexity of Sparse Label Propagation
This paper investigates the computational complexity of sparse label pro...
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Classifying Big Data over Networks via the Logistic Network Lasso
We apply network Lasso to solve binary classification (clustering) probl...
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Graph Signal Sampling via Reinforcement Learning
We formulate the problem of sampling and recovering clustered graph sign...
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Predicting Electricity Outages Caused by Convective Storms
We consider the problem of predicting power outages in an electrical pow...
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Recovery Conditions and Sampling Strategies for Network Lasso
The network Lasso is a recently proposed convex optimization method for ...
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A FixedPoint of View on Gradient Methods for Big Data
Interpreting gradient methods as fixedpoint iterations, we provide a de...
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The Network Nullspace Property for Compressed Sensing of Big Data over Networks
We adapt the nullspace property of compressed sensing for sparse vectors...
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Random Walk Sampling for Big Data over Networks
It has been shown recently that graph signals with small total variation...
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When is Network Lasso Accurate?
A main workhorse for statistical learning and signal processing using sp...
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On the Sample Complexity of Graphical Model Selection for NonStationary Processes
We formulate and analyze a graphical model selection method for inferrin...
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Learning conditional independence structure for highdimensional uncorrelated vector processes
We formulate and analyze a graphical model selection method for inferrin...
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Graphical LASSO Based Model Selection for Time Series
We propose a novel graphical model selection (GMS) scheme for highdimen...
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Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach
We propose a method for inferring the conditional independence graph (CI...
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Performance Limits of Dictionary Learning for Sparse Coding
We consider the problem of dictionary learning under the assumption that...
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Compressive Nonparametric Graphical Model Selection For Time Series
We propose a method for inferring the conditional indepen dence graph (...
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Structural Feature Selection for Event Logs
We consider the problem of classifying business process instances based ...
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Online Feature Ranking for Intrusion Detection Systems
Many current approaches to the design of intrusion detec tion systems a...
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A Gentle Introduction to Supervised Machine Learning
This tutorial is based on the lecture notes for the courses "Machine Lea...
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Shortterm prediction of Electricity Outages Caused by Convective Storms
Prediction of power outages caused by convective storms which are highly...
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Components of Machine Learning: Binding Bits and FLOPS
Many machine learning problems and methods are combinations of three com...
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Alexander Jung
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Assistant Professor of Computer Science at Aalto University