
Basic Principles of Clustering Methods
Clustering methods group a set of data points into a few coherent groups...
11/18/2019 ∙ by Alexander Jung, et al. ∙ 108 ∙ shareread it

Learning Networked Exponential Families with Network Lasso
The data arising in many important bigdata applications, ranging from s...
05/22/2019 ∙ by Alexander Jung, et al. ∙ 22 ∙ shareread it

Semisupervised Learning in NetworkStructured Data via Total Variation Minimization
We propose and analyze a method for semisupervised learning from partia...
01/28/2019 ∙ by Alexander Jung, et al. ∙ 18 ∙ shareread it

Localized Linear Regression in Networked Data
The network Lasso (nLasso) has been proposed recently as an efficient le...
03/26/2019 ∙ by Alexander Jung, et al. ∙ 14 ∙ shareread it

Classifying Partially Labeled Networked Data via Logistic Network Lasso
We apply the network Lasso to classify partially labeled data points whi...
03/26/2019 ∙ by Nguyen Tran, et al. ∙ 14 ∙ shareread it

Analysis of Network Lasso For SemiSupervised Regression
We characterize the statistical properties of network Lasso for semisup...
08/22/2018 ∙ by Alexander Jung, et al. ∙ 12 ∙ shareread it

On the Duality between Network Flows and Network Lasso
The data arising in many application domains have an intrinsic network s...
10/04/2019 ∙ by Alexander Jung, et al. ∙ 12 ∙ shareread it

Classifying Process Instances Using Recurrent Neural Networks
Process Mining consists of techniques where logs created by operative sy...
09/16/2018 ∙ by Markku Hinkka, et al. ∙ 6 ∙ shareread it

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...
11/03/2019 ∙ by Alexander Jung, et al. ∙ 6 ∙ shareread it

On The Complexity of Sparse Label Propagation
This paper investigates the computational complexity of sparse label pro...
04/25/2018 ∙ by Alexander Jung, et al. ∙ 4 ∙ shareread it

Classifying Big Data over Networks via the Logistic Network Lasso
We apply network Lasso to solve binary classification (clustering) probl...
05/07/2018 ∙ by Henrik Ambos, et al. ∙ 4 ∙ shareread it

Graph Signal Sampling via Reinforcement Learning
We formulate the problem of sampling and recovering clustered graph sign...
05/15/2018 ∙ by Oleksii Abramenko, et al. ∙ 2 ∙ shareread it

Predicting Electricity Outages Caused by Convective Storms
We consider the problem of predicting power outages in an electrical pow...
05/21/2018 ∙ by Roope Tervo, et al. ∙ 2 ∙ shareread it

Recovery Conditions and Sampling Strategies for Network Lasso
The network Lasso is a recently proposed convex optimization method for ...
09/03/2017 ∙ by Alexandru Mara, et al. ∙ 0 ∙ shareread it

A FixedPoint of View on Gradient Methods for Big Data
Interpreting gradient methods as fixedpoint iterations, we provide a de...
06/29/2017 ∙ by Alexander Jung, et al. ∙ 0 ∙ shareread it

The Network Nullspace Property for Compressed Sensing of Big Data over Networks
We adapt the nullspace property of compressed sensing for sparse vectors...
05/11/2017 ∙ by Alexander Jung, et al. ∙ 0 ∙ shareread it

Random Walk Sampling for Big Data over Networks
It has been shown recently that graph signals with small total variation...
04/16/2017 ∙ by Saeed Basirian, et al. ∙ 0 ∙ shareread it

When is Network Lasso Accurate?
A main workhorse for statistical learning and signal processing using sp...
04/07/2017 ∙ by Alexander Jung, et al. ∙ 0 ∙ shareread it

On the Sample Complexity of Graphical Model Selection for NonStationary Processes
We formulate and analyze a graphical model selection method for inferrin...
01/17/2017 ∙ by Nguyen Tran Quang, et al. ∙ 0 ∙ shareread it

Learning conditional independence structure for highdimensional uncorrelated vector processes
We formulate and analyze a graphical model selection method for inferrin...
09/13/2016 ∙ by Nguyen Tran Quang, et al. ∙ 0 ∙ shareread it

Graphical LASSO Based Model Selection for Time Series
We propose a novel graphical model selection (GMS) scheme for highdimen...
10/05/2014 ∙ by Alexander Jung, et al. ∙ 0 ∙ shareread it

Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach
We propose a method for inferring the conditional independence graph (CI...
04/04/2014 ∙ by Alexander Jung, et al. ∙ 0 ∙ shareread it

Performance Limits of Dictionary Learning for Sparse Coding
We consider the problem of dictionary learning under the assumption that...
02/17/2014 ∙ by Alexander Jung, et al. ∙ 0 ∙ shareread it

Compressive Nonparametric Graphical Model Selection For Time Series
We propose a method for inferring the conditional indepen dence graph (...
11/13/2013 ∙ by Alexander Jung, et al. ∙ 0 ∙ shareread it

Structural Feature Selection for Event Logs
We consider the problem of classifying business process instances based ...
10/08/2017 ∙ by Markku Hinkka, et al. ∙ 0 ∙ shareread it

Online Feature Ranking for Intrusion Detection Systems
Many current approaches to the design of intrusion detec tion systems a...
03/01/2018 ∙ by Buse Gul Atli, et al. ∙ 0 ∙ shareread it

A Gentle Introduction to Supervised Machine Learning
This tutorial is based on the lecture notes for the courses "Machine Lea...
05/14/2018 ∙ by Alexander Jung, et al. ∙ 0 ∙ shareread it

Shortterm prediction of Electricity Outages Caused by Convective Storms
Prediction of power outages caused by convective storms which are highly...
07/01/2019 ∙ by Roope Tervo, et al. ∙ 0 ∙ shareread it

Components of Machine Learning: Binding Bits and FLOPS
Many machine learning problems and methods are combinations of three com...
10/25/2019 ∙ by Alexander Jung, et al. ∙ 0 ∙ shareread it
Alexander Jung
is this you? claim profile
Assistant Professor of Computer Science at Aalto University