Social Learning Under Inferential Attacks

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We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

In-network devices around the world monitor and tamper with connections for many reasons, including intrusion prevention, combating spam or phishing, and country-level censorship. Connection tampering seeks to block access to specific domain names or keywo ...

Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and req ...

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

This thesis presents work at the junction of statistics and climate science. We first provide methodology for use by climate scientists when performing fast event attribution using extreme value theory, and then describe two interdisciplinary projects in c ...

Minimax-fair machine learning minimizes the error for the worst-off group. However, empirical evidence suggests that when sophisticated models are trained with standard empirical risk minimization (ERM), they often have the same performance on the worst-of ...

Rare events include many of the most interesting transformation processes in condensed matter, from phase transitions to biomolecular conformational changes to chemical reactions. Access to the corresponding mechanisms, free-energy landscapes and kinetic r ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

This thesis focuses on two selected learning problems: 1) statistical inference on graphs models, and, 2) gradient descent on neural networks, with the common objective of defining and analysing the measures that characterize the fundamental limits. In th ...

Artificial Neural Networks (ANN) are habitually trained via the back-propagation (BP) algorithm. This approach has been extremely successful: Current models like GPT-3 have O(10 11 ) parameters, are trained on O(10 11 ) words and produce awe-inspiring resu ...

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Information theory has allowed us to determine the fundamental limit of various communication and algorithmic problems, e.g., the channel coding problem, the compression problem, and the hypothesis testing problem. In this work, we revisit the assumptions ...

Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neural re ...

Incidents where water networks are contaminated with microorganisms or pollutants can result in a large number of infected or ill persons, and it is therefore important to quickly detect, localize and estimate the spread and source of the contamination. In ...

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applicati ...

Laser powder bed fusion of metals (PBF-LB/M) is a pivotal additive manufacturing technique that enables the production of intricate components. In addition to enabling the production of complex shapes, it allows for a high degree of freedom in manipulating ...

Headwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts and the flowing drainage density (DD) varies over time. Monitoring the DD dynamics is essential to understand the processes controlling i ...

Social Learning Under Inferential Attacks

Chattez avec Graph Search

Equilibria in Network Constrained Energy Markets

Probing the latent hierarchical structure of data via diffusion models

Global, Passive Detection of Connection Tampering

Self-supervised and few-shot learning for robust bioaerosol monitoring

Discovering Influencers in Opinion Formation Over Social Graphs

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics

Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

When do Minimax-fair Learning and Empirical Risk Minimization Coincide?

Inferring free-energy barriers and kinetic rates from molecular dynamics via underdamped Langevin models

Social Opinion Formation and Decision Making Under Communication Trends

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Artificial Neural Network Training on an Optical Processor via Direct Feedback Alignment

Boosting likelihood learning with event reweighting

On Speed and Advantage : Results in Information Velocity and Monitoring Problems

Learnable latent embeddings for joint behavioural and neural analysis

Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information

Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

Exploring crystallographic texture manipulation in stainless steels via laser powder bed fusion: insights from neutron diffraction and machine learning

Short-term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements