Cluster-and-Conquer: When Randomness Meets Graph Locality

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The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Te ...

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

Human-machine interfaces (HMIs) can be used to decode a user's motor intention to control an external device. People that suffer from motor disabilities, such as spinal cord injury, can benefit from the uses of these interfaces. While many solutions can be ...

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the ...

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

The numerical solution of singular eigenvalue problems is complicated by the fact that small perturbations of the coefficients may have an arbitrarily bad effect on eigenvalue accuracy. However, it has been known for a long time that such perturbations are ...

Various forms of real-world data, such as social, financial, and biological networks, can be represented using graphs. An efficient method of analysing this type of data is to extract subgraph patterns, such as cliques, cycles, and motifs, from graphs. For ...

We present a loosely coupled scheme for the numerical simulation of the cardiac electrofluid-structure interaction problem, whose solution is typically computationally intensive due to the need to suitably treat the coupling of the different submodels. Our ...

In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

Graph neural networks (GNN) are very popular methods in machine learning and have been applied very successfully to the prediction of the properties of molecules and materials. First-order GNNs are well known to be incomplete, i.e. there exist graphs that ...

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Graph learning is often a necessary step in processing or representing structured data, when the underlying graph is not given explicitly. Graph learning is generally performed centrally with a full knowledge of the graph signals, namely the data that live ...

We address multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control pol ...

Federated learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or ...

Motivation: Finding biosynthetic pathways is essential for metabolic engineering of organisms to produce chemicals, biodegradation prediction of pollutants and drugs, and for the elucidation of bioproduction pathways of secondary metabolites. A key step in ...

Social Engagement is a novel business model transforming final users of a service from passive into active components. In this framework, people are contacted by a company and they are asked to perform tasks in exchange for a reward. This arises the compli ...

Graphs offer a simple yet meaningful representation of relationships between data. This representation is often used in machine learning algorithms in order to incorporate structural or geometric information about data. However, it can also be used in an i ...

Cluster-and-Conquer: When Randomness Meets Graph Locality

Chattez avec Graph Search

The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Human-machine interface for two-dimensional steering control with the auricular muscles

Distribution Inference Risks: Identifying and Mitigating Sources of Leakage

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Singular quadratic eigenvalue problems: linearization and weak condition numbers

Acceleration of graph pattern mining and applications to financial crime

A stable loosely-coupled scheme for cardiac electro-fluid-structure interaction

Fairness and Explainability in Clustering Problems

Robust Collaborative Learning with Linear Gradient Overhead

Incompleteness of graph neural networks for points clouds in three dimensions

Directionality-Aware Design of Embroidery Patterns

Distributed Graph Learning With Smooth Data Priors

Multi-robot task allocation for safe planning against stochastic hazard dynamics

FLEET: Online Federated Learning via Staleness Awareness and Performance Prediction

NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

A chance-constraint approach for optimizing Social Engagement-based services

On the transition times in a loss model of wireless networks

Representing graphs through data with learning and optimal transport

Preface-Special Issue on Misinformation on the Web