Hypernetworks Analysis of RoboCup Interactions

Robotic soccer simulations are controlled environments in which the rich variety of interactions among agents make them good candidates to be studied as complex adaptive systems. The challenge is to create an autonomous team of soccer agents that can adapt and improve its behaviour as it plays other teams. By analogy with chess, the movements of the soccer agents and the ball form ever-changing networks as players in one team form structures that give their team an advantage. For example, the Defender’s Dilemma involves relationships between an attacker with the ball, a team-mate and a defender. The defender must choose between tackling the player with the ball, or taking a position to intercept a pass to the other attacker. Since these structures involve more that two interacting entities it is necessary to go beyond networks to multidimensional hypernetworks. In this context, this thesis investigates (i) is it possible to identify patterns of play, that lead a team to obtain an advantage ?, (ii) is it possible to forecast with a good degree of accuracy if a certain game action or sequence of game actions is going to be successful, before it has been completed ?, and (iii) is it possible to make behavioural patterns emerge in the game without specifying the behavioural rules in detail ? To investigate these research questions we devised two methods to analyse the interactions between robotic players, one based on traditional programming and one based on Deep Learning. The first method identified thousands of Defender’s Dilemma configurations from RoboCup 2D simulator games and found a statistically significant association between winning and the creation of the defender’s dilemma by the attackers of the winning team. The second method showed that a feedforward Artificial Neural Network trained on thousands of games can take as input the current game configuration and forecast to a high degree of accuracy if the current action will end up in a goal or not. Finally, we designed our own fast and simple robotic soccer simulator for investigating Reinforcement Learning. This showed that Reinforcement Learning using Proximal Policy Optimization could train two agents in the task of scoring a goal, using only basic actions without using pre-built hand-programmed skills. These experiments provide evidence that it is possible: to identify advantageous patterns of play; to forecast if an action or sequence of actions will be successful; and to make behavioural patterns emerge in the game without specifying the behavioural rules in detail

Towards Deep Learning with Competing Generalisation Objectives

The unreasonable effectiveness of Deep Learning continues to deliver unprecedented Artificial Intelligence capabilities to billions of people. Growing datasets and technological advances keep extending the reach of expressive model architectures trained through efficient optimisations. Thus, deep learning approaches continue to provide increasingly proficient subroutines for, among others, computer vision and natural interaction through speech and text. Due to their scalable learning and inference priors, higher performance is often gained cost-effectively through largely automatic training. As a result, new and improved capabilities empower more people while the costs of access drop. The arising opportunities and challenges have profoundly influenced research. Quality attributes of scalable software became central desiderata of deep learning paradigms, including reusability, efficiency, robustness and safety. Ongoing research into continual, meta- and robust learning aims to maximise such scalability metrics in addition to multiple generalisation criteria, despite possible conflicts. A significant challenge is to satisfy competing criteria automatically and cost-effectively. In this thesis, we introduce a unifying perspective on learning with competing generalisation objectives and make three additional contributions. When autonomous learning through multi-criteria optimisation is impractical, it is reasonable to ask whether knowledge of appropriate trade-offs could make it simultaneously effective and efficient. Informed by explicit trade-offs of interest to particular applications, we developed and evaluated bespoke model architecture priors. We introduced a novel architecture for sim-to-real transfer of robotic control policies by learning progressively to generalise anew. Competing desiderata of continual learning were balanced through disjoint capacity and hierarchical reuse of previously learnt representations. A new state-of-the-art meta-learning approach is then proposed. We showed that meta-trained hypernetworks efficiently store and flexibly reuse knowledge for new generalisation criteria through few-shot gradient-based optimisation. Finally, we characterised empirical trade-offs between the many desiderata of adversarial robustness and demonstrated a novel defensive capability of implicit neural networks to hinder many attacks simultaneously

Beyond Flatland : exploring graphs in many dimensions

Societies, technologies, economies, ecosystems, organisms, . . . Our world is composed of complex networks—systems with many elements that interact in nontrivial ways. Graphs are natural models of these systems, and scientists have made tremendous progress in developing tools for their analysis. However, research has long focused on relatively simple graph representations and problem specifications, often discarding valuable real-world information in the process. In recent years, the limitations of this approach have become increasingly apparent, but we are just starting to comprehend how more intricate data representations and problem formulations might benefit our understanding of relational phenomena. Against this background, our thesis sets out to explore graphs in five dimensions: descriptivity, multiplicity, complexity, expressivity, and responsibility. Leveraging tools from graph theory, information theory, probability theory, geometry, and topology, we develop methods to (1) descriptively compare individual graphs, (2) characterize similarities and differences between groups of multiple graphs, (3) critically assess the complexity of relational data representations and their associated scientific culture, (4) extract expressive features from and for hypergraphs, and (5) responsibly mitigate the risks induced by graph-structured content recommendations. Thus, our thesis is naturally situated at the intersection of graph mining, graph learning, and network analysis.Gesellschaften, Technologien, Volkswirtschaften, Ökosysteme, Organismen, . . . Unsere Welt besteht aus komplexen Netzwerken—Systemen mit vielen Elementen, die auf nichttriviale Weise interagieren. Graphen sind natürliche Modelle dieser Systeme, und die Wissenschaft hat bei der Entwicklung von Methoden zu ihrer Analyse große Fortschritte gemacht. Allerdings hat sich die Forschung lange auf relativ einfache Graphrepräsentationen und Problemspezifikationen beschränkt, oft unter Vernachlässigung wertvoller Informationen aus der realen Welt. In den vergangenen Jahren sind die Grenzen dieser Herangehensweise zunehmend deutlich geworden, aber wir beginnen gerade erst zu erfassen, wie unser Verständnis relationaler Phänomene von intrikateren Datenrepräsentationen und Problemstellungen profitieren kann. Vor diesem Hintergrund erkundet unsere Dissertation Graphen in fünf Dimensionen: Deskriptivität, Multiplizität, Komplexität, Expressivität, und Verantwortung. Mithilfe von Graphentheorie, Informationstheorie, Wahrscheinlichkeitstheorie, Geometrie und Topologie entwickeln wir Methoden, welche (1) einzelne Graphen deskriptiv vergleichen, (2) Gemeinsamkeiten und Unterschiede zwischen Gruppen multipler Graphen charakterisieren, (3) die Komplexität relationaler Datenrepräsentationen und der mit ihnen verbundenen Wissenschaftskultur kritisch beleuchten, (4) expressive Merkmale von und für Hypergraphen extrahieren, und (5) verantwortungsvoll den Risiken begegnen, welche die Graphstruktur von Inhaltsempfehlungen mit sich bringt. Damit liegt unsere Dissertation naturgemäß an der Schnittstelle zwischen Graph Mining, Graph Learning und Netzwerkanalyse

On the privacy risks of machine learning models

Machine learning (ML) has made huge progress in the last decade and has been applied to a wide range of critical applications. However, driven by the increasing adoption of machine learning models, the significance of privacy risks has become more crucial than ever. These risks can be classified into two categories depending on the role played by ML models: one in which the models themselves are vulnerable to leaking sensitive information, and the other in which the models are abused to violate privacy. In this dissertation, we investigate the privacy risks of machine learning models from two perspectives, i.e., the vulnerability of ML models and the abuse of ML models. To study the vulnerability of ML models to privacy risks, we conduct two studies on one of the most severe privacy attacks against ML models, namely the membership inference attack (MIA). Firstly, we explore membership leakage in label-only exposure of ML models. We present the first label-only membership inference attack and reveal that membership leakage is more severe than previously shown. Secondly, we perform the first privacy analysis of multi-exit networks through the lens of membership leakage. We leverage existing attack methodologies to quantify the vulnerability of multi-exit networks to membership inference attacks and propose a hybrid attack that exploits the exit information to improve the attack performance. From the perspective of abusing ML models to violate privacy, we focus on deepfake face manipulation that can create visual misinformation. We propose the first defense system \system against GAN-based face manipulation by jeopardizing the process of GAN inversion, which is an essential step for subsequent face manipulation. All findings contribute to the community's insight into the privacy risks of machine learning models. We appeal to the community's consideration of the in-depth investigation of privacy risks, like ours, against the rapidly-evolving machine learning techniques.Das maschinelle Lernen (ML) hat in den letzten zehn Jahren enorme Fortschritte gemacht und wurde für eine breite Palette wichtiger Anwendungen eingesetzt. Durch den zunehmenden Einsatz von Modellen des maschinellen Lernens ist die Bedeutung von Datenschutzrisiken jedoch wichtiger denn je geworden. Diese Risiken können je nach der Rolle, die ML-Modelle spielen, in zwei Kategorien eingeteilt werden: in eine, in der die Modelle selbst anfällig für das Durchsickern sensibler Informationen sind, und in die andere, in der die Modelle zur Verletzung der Privatsphäre missbraucht werden. In dieser Dissertation untersuchen wir die Datenschutzrisiken von Modellen des maschinellen Lernens aus zwei Blickwinkeln, nämlich der Anfälligkeit von ML-Modellen und dem Missbrauch von ML-Modellen. Um die Anfälligkeit von ML-Modellen für Datenschutzrisiken zu untersuchen, führen wir zwei Studien zu einem der schwerwiegendsten Angriffe auf den Datenschutz von ML-Modellen durch, nämlich dem Angriff auf die Mitgliedschaft (membership inference attack, MIA). Erstens erforschen wir das Durchsickern von Mitgliedschaften in ML-Modellen, die sich nur auf Labels beziehen. Wir präsentieren den ersten "label-only membership inference"-Angriff und stellen fest, dass das "membership leakage" schwerwiegender ist als bisher gezeigt. Zweitens führen wir die erste Analyse der Privatsphäre von Netzwerken mit mehreren Ausgängen durch die Linse des Mitgliedschaftsverlustes durch. Wir nutzen bestehende Angriffsmethoden, um die Anfälligkeit von Multi-Exit-Netzwerken für Membership-Inference-Angriffe zu quantifizieren und schlagen einen hybriden Angriff vor, der die Exit-Informationen ausnutzt, um die Angriffsleistung zu verbessern. Unter dem Gesichtspunkt des Missbrauchs von ML-Modellen zur Verletzung der Privatsphäre konzentrieren wir uns auf die Manipulation von Gesichtern, die visuelle Fehlinformationen erzeugen können. Wir schlagen das erste Abwehrsystem \system gegen GAN-basierte Gesichtsmanipulationen vor, indem wir den Prozess der GAN-Inversion gefährden, der ein wesentlicher Schritt für die anschließende Gesichtsmanipulation ist. Alle Ergebnisse tragen dazu bei, dass die Community einen Einblick in die Datenschutzrisiken von maschinellen Lernmodellen erhält. Wir appellieren an die Gemeinschaft, eine eingehende Untersuchung der Risiken für die Privatsphäre, wie die unsere, im Hinblick auf die sich schnell entwickelnden Techniken des maschinellen Lernens in Betracht zu ziehen

Characterising and modeling the co-evolution of transportation networks and territories

The identification of structuring effects of transportation infrastructure on territorial dynamics remains an open research problem. This issue is one of the aspects of approaches on complexity of territorial dynamics, within which territories and networks would be co-evolving. The aim of this thesis is to challenge this view on interactions between networks and territories, both at the conceptual and empirical level, by integrating them in simulation models of territorial systems.Comment: Doctoral dissertation (2017), Universit\'e Paris 7 Denis Diderot. Translated from French. Several papers compose this PhD thesis; overlap with: arXiv:{1605.08888, 1608.00840, 1608.05266, 1612.08504, 1706.07467, 1706.09244, 1708.06743, 1709.08684, 1712.00805, 1803.11457, 1804.09416, 1804.09430, 1805.05195, 1808.07282, 1809.00861, 1811.04270, 1812.01473, 1812.06008, 1908.02034, 2012.13367, 2102.13501, 2106.11996

Automatic machine learning:methods, systems, challenges

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844