Search Results for author:
Found 42 papers, 7 papers with code
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations
Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature.
Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization
AI systems increasingly shape critical decisions across personal and societal domains.
Personalized Federated Learning via Active Sampling
The available local datasets might fail to provide sufficient statistical power to train high-dimensional models (such as deep neural networks) effectively.
Inertia emulation contribution of Frades 2 variable speed pump-turbine to power network stability
This paper is addressing the quantification and the comparison of pumped storage power plants, PSPP, contribution to synchronous inertia and synthetic inertia when fixed speed and variable speed motor-generators technologies are considered, respectively.
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization
This paper proposes a proximal variant of the alternating direction method of multipliers (ADMM) for distributed optimization.
Rethinking Drone-Based Search and Rescue with Aerial Person Detection
This final processing stage used in the AIR detector significantly improves its performance and usability in the face of real-world aerial SAR missions.
Clustered Federated Learning via Generalized Total Variation Minimization
Our main analytic contribution is an upper bound on the deviation between the local model parameters learnt by our algorithm and an oracle-based clustered federated learning method.
Containing Future Epidemics with Trustworthy Federated Systems for Ubiquitous Warning and Response
In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.
Distributed, Parallel, and Cluster Computing
Local Graph Clustering with Network Lasso
We study the statistical and computational properties of a network Lasso method for local graph clustering.
An Information-Theoretic Approach to Personalized Explainable Machine Learning
Recommender systems decide which jobs, movies, or other user profiles might be interesting to us.
Basic Principles of Clustering Methods
Clustering methods group a set of data points into a few coherent groups or clusters of similar data points.
Clustering in Partially Labeled Stochastic Block Models via Total Variation Minimization
In such a partially labeled stochastic block model, clustering amounts to estimating the cluster assignments of the remaining data points.
Components of Machine Learning: Binding Bits and FLOPS
Many machine learning problems and methods are combinations of three components: data, hypothesis space and loss function.
On the Duality between Network Flows and Network Lasso
Many applications generate data with an intrinsic network structure such as time series data, image data or social network data.
Short-term prediction of Electricity Outages Caused by Convective Storms
We propose a new machine learning approach to predict the damage caused by storms.
Learning Networked Exponential Families with Network Lasso
We propose networked exponential families to jointly leverage the information in the topology as well as the attributes (features) of networked data points.
Classifying Partially Labeled Networked Data via Logistic Network Lasso
We apply the network Lasso to classify partially labeled data points which are characterized by high-dimensional feature vectors.
Localized Linear Regression in Networked Data
The network Lasso (nLasso) has been proposed recently as an efficient learning algorithm for massive networked data sets (big data over networks).
Semi-supervised Learning in Network-Structured Data via Total Variation Minimization
This lends naturally to learning the labels by total variation (TV) minimization, which we solve by applying a recently proposed primal-dual method for non-smooth convex optimization.
Classifying Process Instances Using Recurrent Neural Networks
Recurrent neural networks and its subclasses, such as Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), have been demonstrated to be able to learn relevant temporal features for subsequent classification tasks.
Predicting Electricity Outages Caused by Convective Storms
We consider the problem of predicting power outages in an electrical power grid due to hazards produced by convective storms.
Graph Signal Sampling via Reinforcement Learning
We formulate the problem of sampling and recovering clustered graph signal as a multi-armed bandit (MAB) problem.
Machine Learning: Basic Principles
This tutorial is based on the lecture notes for, and the plentiful student feedback received from, the courses "Machine Learning: Basic Principles" and "Artificial Intelligence", which I have co-taught since 2015 at Aalto University.
The Logistic Network Lasso
We apply the network Lasso to solve binary classification and clustering problems for network-structured data.
On The Complexity of Sparse Label Propagation
This paper investigates the computational complexity of sparse label propagation which has been proposed recently for processing network structured data.
Online Feature Ranking for Intrusion Detection Systems
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion.
When is Network Lasso Accurate: The Vector Case
A recently proposed learning algorithm for massive network-structured data sets (big data over networks) is the network Lasso (nLasso), which extends the well- known Lasso estimator from sparse models to network-structured datasets.
Structural Feature Selection for Event Logs
The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis.
Recovery Conditions and Sampling Strategies for Network Lasso
By generalizing the concept of the compatibility condition put forward by van de Geer and Buehlmann as a powerful tool for the analysis of plain Lasso, we derive a sufficient condition, i. e., the network compatibility condition, on the underlying network topology such that network Lasso accurately learns a clustered underlying graph signal.
A Fixed-Point of View on Gradient Methods for Big Data
In particular, we will show how gradient descent can be accelerated by a fixed-point preserving transformation of an operator associated with the objective function.
The Network Nullspace Property for Compressed Sensing of Big Data over Networks
The network nullspace property couples the cluster structure of the underlying network-structure with the geometry of the sampling set.
Random Walk Sampling for Big Data over Networks
Numerical experiments demonstrate the effectiveness of this approach for graph signals obtained from a synthetic random graph model as well as a real-world dataset.
When is Network Lasso Accurate?
By leveraging concepts of compressed sensing, we address this gap and derive precise conditions on the underlying network topology and sampling set which guarantee the network Lasso for a particular loss function to deliver an accurate estimate of the entire underlying graph signal.
On the Sample Complexity of Graphical Model Selection for Non-Stationary Processes
We characterize the sample size required for accurate graphical model selection from non-stationary samples.
Semi-Supervised Learning via Sparse Label Propagation
This learning algorithm allows for a highly scalable implementation as message passing over the underlying data graph.
Scalable Semi-Supervised Learning over Networks using Nonsmooth Convex Optimization
We propose a scalable method for semi-supervised (transductive) learning from massive network-structured datasets.
Learning conditional independence structure for high-dimensional uncorrelated vector processes
We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation.
On the Minimax Risk of Dictionary Learning
The main conceptual contribution of this paper is the adaption of the information-theoretic approach to minimax estimation for the DL problem in order to derive lower bounds on the worst case MSE of any DL scheme.
Graphical LASSO Based Model Selection for Time Series
We propose a novel graphical model selection (GMS) scheme for high-dimensional stationary time series or discrete time process.
Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach
A theoretical performance analysis provides conditions which guarantee that the probability of the proposed inference method to deliver a wrong CIG is below a prescribed value.
Performance Limits of Dictionary Learning for Sparse Coding
We consider the problem of dictionary learning under the assumption that the observed signals can be represented as sparse linear combinations of the columns of a single large dictionary matrix.
Compressive Nonparametric Graphical Model Selection For Time Series
We propose a method for inferring the conditional indepen- dence graph (CIG) of a high-dimensional discrete-time Gaus- sian vector random process from finite-length observations.
