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08/31/2023
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization
This paper proposes a proximal variant of the alternating direction meth...
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12/13/2021
FlexOS: Towards Flexible OS Isolation
At design time, modern operating systems are locked in a specific safety...
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11/17/2021
Rethinking Drone-Based Search and Rescue with Aerial Person Detection
The visual inspection of aerial drone footage is an integral part of lan...
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05/26/2021
Networked Federated Multi-Task Learning
Many important application domains generate distributed collections of h...
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04/26/2021
Unikraft: Fast, Specialized Unikernels the Easy Way
Unikernels are famous for providing excellent performance in terms of bo...
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10/26/2020
Containing Future Epidemics with Trustworthy Federated Systems for Ubiquitous Warning and Response
In this paper, we propose a global digital platform to avoid and combat ...
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04/25/2020
Local Graph Clustering with Network Lasso
We study the statistical and computational properties of a network Lasso...
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03/01/2020
An Information-Theoretic Approach to Explainable Machine Learning
A key obstacle to the successful deployment of machine learning (ML) met...
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11/18/2019
Basic Principles of Clustering Methods
Clustering methods group a set of data points into a few coherent groups...
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11/03/2019
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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10/25/2019
Components of Machine Learning: Binding Bits and FLOPS
Many machine learning problems and methods are combinations of three com...
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10/04/2019
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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07/01/2019
Short-term prediction of Electricity Outages Caused by Convective Storms
Prediction of power outages caused by convective storms which are highly...
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05/22/2019
Learning Networked Exponential Families with Network Lasso
The data arising in many important big-data applications, ranging from s...
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03/26/2019
Localized Linear Regression in Networked Data
The network Lasso (nLasso) has been proposed recently as an efficient le...
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03/26/2019
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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01/28/2019
Semi-supervised Learning in Network-Structured Data via Total Variation Minimization
We propose and analyze a method for semi-supervised learning from partia...
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09/16/2018
Classifying Process Instances Using Recurrent Neural Networks
Process Mining consists of techniques where logs created by operative sy...
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08/22/2018
Analysis of Network Lasso For Semi-Supervised Regression
We characterize the statistical properties of network Lasso for semi-sup...
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05/21/2018
Predicting Electricity Outages Caused by Convective Storms
We consider the problem of predicting power outages in an electrical pow...
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05/15/2018
Graph Signal Sampling via Reinforcement Learning
We formulate the problem of sampling and recovering clustered graph sign...
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05/14/2018
A Gentle Introduction to Supervised Machine Learning
This tutorial is based on the lecture notes for the courses "Machine Lea...
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05/07/2018
Classifying Big Data over Networks via the Logistic Network Lasso
We apply network Lasso to solve binary classification (clustering) probl...
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04/25/2018
On The Complexity of Sparse Label Propagation
This paper investigates the computational complexity of sparse label pro...
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03/01/2018
Online Feature Ranking for Intrusion Detection Systems
Many current approaches to the design of intrusion detec- tion systems a...
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10/08/2017
Structural Feature Selection for Event Logs
We consider the problem of classifying business process instances based ...
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09/03/2017
Recovery Conditions and Sampling Strategies for Network Lasso
The network Lasso is a recently proposed convex optimization method for ...
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06/29/2017
A Fixed-Point of View on Gradient Methods for Big Data
Interpreting gradient methods as fixed-point iterations, we provide a de...
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05/11/2017
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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04/16/2017
Random Walk Sampling for Big Data over Networks
It has been shown recently that graph signals with small total variation...
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04/07/2017
When is Network Lasso Accurate?
A main workhorse for statistical learning and signal processing using sp...
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01/17/2017
On the Sample Complexity of Graphical Model Selection for Non-Stationary Processes
We formulate and analyze a graphical model selection method for inferrin...
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09/13/2016
Learning conditional independence structure for high-dimensional uncorrelated vector processes
We formulate and analyze a graphical model selection method for inferrin...
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10/05/2014
Graphical LASSO Based Model Selection for Time Series
We propose a novel graphical model selection (GMS) scheme for high-dimen...
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04/04/2014
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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02/17/2014
Performance Limits of Dictionary Learning for Sparse Coding
We consider the problem of dictionary learning under the assumption that...
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11/13/2013
