This thesis aims to contribute to the field of neural networks by improving upon the performance of a state-of-the-art regularization scheme called MixUp, and by contributing to the conceptual understanding of MixUp. MixUp is a data augmentation scheme in which pairs of training samples and their corresponding labels are mixed using linear coefficients. Without label mixing, MixUp becomes a more conventional scheme: input samples are moved but their original labels are retained. Because samples are preferentially moved in the direction of other classes we refer to this method as directional adversarial training, or DAT. We show that under two mild conditions, MixUp asymptotically convergences to a subset of DAT. We define untied MixUp (UMixUp), a superset of MixUp wherein training labels are mixed with different linear coefficients to those of their corresponding samples. We show that under the same mild conditions, untied MixUp converges to the entire class of DAT schemes. Motivated by the understanding that UMixUp is both a generalization of MixUp and a scheme possessing adversarial-training properties, we experiment with different datasets and loss functions to show that UMixUp provides improves performance over MixUp. In short, we present a novel interpretation of MixUp as belonging to a class highly analogous to adversarial training, and on this basis we introduce a simple generalization which outperforms MixUp.

Adversarial examples are among the most widespread attacks in adversarial machine learning. In this work, we define new targeted and non-targeted attacks that are computationally less expensive than standard adversarial attacks. Besides practical purposes in some scenarios, these attacks can improve our understanding of the robustness of machine learning models. Moreover, we introduce a new training scheme to improve the performance of pre-trained neural networks and defend against our attacks. We examine the differences between our method, standard training, and standard adversarial training on pre-trained models. We find that our method protects the networks better against our attacks. Furthermore, unlike usual adversarial training, which reduces standard accuracy when applied to previously trained networks, our method maintains and sometimes even improves standard accuracy.

For many real-world applications, labeled data can be costly to obtain. Semi-supervised learning methods make use of substantially available unlabeled data along with few labeled samples. Most of the latest work on semi-supervised learning for image classification show performance on standard machine learning datasets like MNIST, SVHN, etc. In this work, we propose a convolutional adversarial autoencoder architecture for real-world data. We demonstrate the application of this architecture for semi-supervised object recognition. We show that our approach can learn from limited labeled data and outperform fully-supervised CNN baseline method by about 4% on real-world datasets. We also achieve competitive performance on the MNIST dataset compared to state-of-the-art semi-supervised learning techniques. To spur research in this direction, we compiled two real-world datasets: Internet (WIS) dataset and Real-world (RW) dataset which consists of more than 20K labeled samples each, comprising of small household objects belonging to ten classes. We also show a possible application of this method for online learning in robotics. / I de flesta verklighetsbaserade tillämpningar kan det vara kostsamt att erhålla märkt data. Inlärningsmetoder som är semi-övervakade använder sig oftast i stor utsträckning av omärkt data med stöd av en liten mängd märkt data. Mycket av det senaste arbetet inom semiövervakade inlärningsmetoder för bildklassificering visar prestanda på standardiserad maskininlärning så som MNIST, SVHN, och så vidare. I det här arbetet föreslår vi en convolutional adversarial autoencoder arkitektur för verklighetsbaserad data. Vi demonstrerar tillämpningen av denna arkitektur för semi-övervakad objektidentifiering och visar att vårt tillvägagångssätt kan lära sig av ett begränsat antal märkt data. Därmed överträffar vi den fullt övervakade CNN-baslinjemetoden med ca. 4% på verklighetsbaserade datauppsättningar. Vi uppnår även konkurrenskraftig prestanda på MNIST datauppsättningen jämfört med moderna semi-övervakade inlärningsmetoder. För att stimulera forskningen i den här riktningen, samlade vi två verklighetsbaserade datauppsättningar: Internet (WIS) och Real-world (RW) datauppsättningar, som består av mer än 20 000 märkta prov vardera, som utgörs av små hushållsobjekt tillhörandes tio klasser. Vi visar också en möjlig tillämpning av den här metoden för online-inlärning i robotik.

Critical Infrastructures (CIs) such as water treatment plants, power grids and telecommunication networks are critical to the daily activities and well-being of our society. Disruption of such CIs would have catastrophic consequences for public safety and the national economy. Hence, these infrastructures have become major targets in the upsurge of cyberattacks. Defending against such attacks often depends on an arsenal of cyber-defence tools, including Machine Learning (ML)-based Anomaly Detection Systems (ADSs). These detection systems use ML models to learn the profile of the normal behaviour of a CI and classify deviations that go well beyond the normality profile as anomalies. However, ML methods are vulnerable to both adversarial and non-adversarial input perturbations. Adversarial perturbations are imperceptible noises added to the input data by an attacker to evade the classification mechanism. Non-adversarial perturbations can be a normal behaviour evolution as a result of changes in usage patterns or other characteristics and noisy data from normally degrading devices, generating a high rate of false positives. We first study the problem of ML-based ADSs being vulnerable to non-adversarial perturbations, which causes a high rate of false alarms. To address this problem, we propose an ADS called DAICS, based on a wide and deep learning model that is both adaptive to evolving normality and robust to noisy data normally emerging from the system. DAICS adapts the pre-trained model to new normality with a small number of data samples and a few gradient updates based on feedback from the operator on false alarms. The DAICS was evaluated on two datasets collected from real-world Industrial Control System (ICS) testbeds. The results show that the adaptation process is fast and that DAICS has an improved robustness compared to state-of-the-art approaches. We further investigated the problem of false-positive alarms in the ADSs. To address this problem, an extension of DAICS, called the SiFA framework, is proposed. The SiFA collects a buffer of historical false alarms and suppresses every new alarm that is similar to these false alarms. The proposed framework is evaluated using a dataset collected from a real-world ICS testbed. The evaluation results show that the SiFA can decrease the false alarm rate of DAICS by more than 80%. We also investigate the problem of ML-based network ADSs that are vulnerable to adversarial perturbations. In the case of network ADSs, attackers may use their knowledge of anomaly detection logic to generate malicious traffic that remains undetected. One way to solve this issue is to adopt adversarial training in which the training set is augmented with adversarially perturbed samples. This thesis presents an adversarial training approach called GADoT that leverages a Generative Adversarial Network (GAN) to generate adversarial samples for training. GADoT is validated in the scenario of an ADS detecting Distributed Denial of Service (DDoS) attacks, which have been witnessing an increase in volume and complexity. For a practical evaluation, the DDoS network traffic was perturbed to generate two datasets while fully preserving the semantics of the attack. The results show that adversaries can exploit their domain expertise to craft adversarial attacks without requiring knowledge of the underlying detection model. We then demonstrate that adversarial training using GADoT renders ML models more robust to adversarial perturbations. However, the evaluation of adversarial robustness is often susceptible to errors, leading to robustness overestimation. We investigate the problem of robustness overestimation in network ADSs and propose an adversarial attack called UPAS to evaluate the robustness of such ADSs. The UPAS attack perturbs the inter-arrival time between packets by injecting a random time delay before packets from the attacker. The attack is validated by perturbing malicious network traffic in a multi-attack dataset and used to evaluate the robustness of two robust ADSs, which are based on a denoising autoencoder and an adversarially trained ML model. The results demonstrate that the robustness of both ADSs is overestimated and that a standardised evaluation of robustness is needed.

In the field of wireless communication systems, the interest in machine learning has increased in recent years. Adversarial machine learning includes attack and defense methods on machine learning components. It is a topic that has been thoroughly studied in computer vision and natural language processing but not to the same extent in wireless communication. In this thesis, a Fast Gradient Sign Method (FGSM) attack on a neural receiver is studied. Furthermore, the thesis investigates whether it is possible to make a neural receiver robust against these attacks. The study is made using the python library Sionna, a library used for research on for example 5G, 6G and machine learning in wireless communication. The effect of a FGSM attack is evaluated and mitigated with different models within adversarial training. The training data of the models is either augmented with adversarial samples, or original samples are replaced with adversarial ones. Furthermore, the power distribution and range of the adversarial samples included in the training are varied. The thesis concludes that a FGSM attack decreases the performance of a neural receiver and needs less power than a barrage jamming attack to achieve the same performance loss. A neural receiver can be made more robust against a FGSM attack when the training data of the model is augmented with adversarial samples. The samples are concentrated on a specific attack power range and the power of the adversarial samples is normally distributed. A neural receiver is also proven to be more robust against a barrage jamming attack than conventional methods without defenses.

Les algorithmes d’apprentissage par renforcement conditionné par les buts apprennent à accomplir des tâches en interagissant avec leur environnement. Ce faisant, ils apprennent à propos du monde qui les entourent de façon graduelle et adaptive. Parmi d’autres raisons, c’est pourquoi cette branche de l’intelligence artificielle est une des avenues les plus promet- teuses pour le contrôle des robots généralistes de demain. Cependant, la sûreté de ces algo- rithmes de contrôle restent un champ de recherche actif. La majorité des algorithmes “d’ap- prentissage par renforcement sûr” tâchent d’assurer la sécurité de la politique de contrôle tant durant l’apprentissage que pendant le déploiement ou l’évaluation. Dans ce travail, nous proposons une stratégie complémentaire. Puisque la majorité des algorithmes de contrôle pour la robotique sont développés, entraî- nés, et testés en simulation pour éviter d’endommager les vrais robots, nous pouvons nous permettre d’agir de façon dangereuse dans l’environnement simulé. Nous démontrons qu’en donnant des buts dangereux à effectuer à l’algorithme d’apprentissage durant l’apprentissage, nous pouvons produire des populations de politiques de contrôle plus sûres au déploiement ou à l’évaluation qu’en sélectionnant les buts avec des techniques de l’état de l’art. Pour ce faire, nous introduisons un nouvel agent à l’entraînement de la politique de contrôle, le “Directeur”. Le rôle du Directeur est de sélectionner des buts qui sont assez difficiles pour aider la politique à apprendre à les résoudre sans être trop difficiles ou trop faciles. Pour aider le Directeur dans sa tâche, nous entraînons un réseau de neurones en ligne pour prédire sur quels buts la politique de contrôle échouera. Armé de ce “réseau de la peur” (nommé d’après la peur de la politique de contrôle), le Directeur parviens à sélectionner les buts de façon à ce que les politiques de contrôles finales sont plus sûres et plus performantes que les politiques entraînées à l’aide de méthodes de l’état de l’art, ou obtiennent des métriques semblables. De plus, les populations de politiques entraînées par le Directeur ont moins de variance dans leur comportement, et sont plus résistantes contre des attaques d’adversaires sur les buts qui leur sont issus. / By learning from experience, goal-conditioned reinforcement learning methods learn from their environments gradually and adaptively. Among other reasons, this makes them a promising direction for the generalist robots of the future. However, the safety of these goal- conditioned RL policies is still an active area of research. The majority of “Safe Reinforce- ment Learning” methods seek to enforce safety both during training and during deployment and/or evaluation. In this work, we propose a complementary strategy. Because the majority of control algorithms for robots are developed, trained, and tested in simulation to avoid damaging the real hardware, we can afford to let the policy act in unsafe ways in the simulated environment. We show that by tasking the learning algorithm with unsafe goals during its training, we can produce populations of final policies that are safer at evaluation or deployment than when trained with state-of-the-art goal-selection methods. To do so, we introduce a new agent to the training of the policy that we call the “Director”. The Director’s role is to select goals that are hard enough to aid the policy’s training, without being too hard or too easy. To help the Director in its task, we train a neural network online to predict which goals are unsafe for the current policy. Armed with this “fear network” (named after the policy’s own fear of violating its safety conditions), the Director is able to select training goals such that the final trained policies are safer and more performant than policies trained on state-of-the-art goal-selection methods (or just as safe/performant). Additionally, the populations of policies trained by the Director show decreased variance in their behaviour, along with increased resistance to adversarial attacks on the goals issued to them.

Deep learning is one of the hottest scientific topics at the moment. Deep convolutional networks can solve various complex tasks in the field of image processing. However, adversarial attacks have been shown to have the ability of fooling deep learning models. An adversarial attack is accomplished by applying specially designed perturbations on the input image of a deep learning model. The noises are almost visually indistinguishable to human eyes, but can fool classifiers into making wrong predictions. In this thesis, adversarial attacks and methods to improve deep learning ’models robustness against adversarial samples were studied. Five different adversarial attack algorithm were implemented. These attack algorithms included white-box attacks and black-box attacks, targeted attacks and non-targeted attacks, and image-specific attacks and universal attacks. The adversarial attacks generated adversarial examples that resulted in significant drop in classification accuracy. Adversarial training is one commonly used strategy to improve the robustness of deep learning models against adversarial examples. It is shown that adversarial training can provide an additional regularization benefit beyond that provided by using dropout. Adversarial training is performed by incorporating adversarial examples into the training process. Traditionally, during this process, cross-entropy loss is used as the loss function. In order to improve the robustness of deep learning models against adversarial examples, in this thesis we propose two new methods of adversarial training by applying the principle of Maximal Coding Rate Reduction. The Maximal Coding Rate Reduction loss function maximizes the coding rate difference between the whole data set and the sum of each individual class. We evaluated the performance of different adversarial training methods by comparing the clean accuracy, adversarial accuracy and local Lipschitzness. It was shown that adversarial training with Maximal Coding Rate Reduction loss function would yield a more robust network than the traditional adversarial training method. / Djupinlärning är ett av de hetaste vetenskapliga ämnena just nu. Djupa konvolutionella nätverk kan lösa olika komplexa uppgifter inom bildbehandling. Det har dock visat sig att motståndarattacker har förmågan att lura djupa inlärningsmodeller. En motståndarattack genomförs genom att man tillämpar särskilt utformade störningar på den ingående bilden för en djup inlärningsmodell. Störningarna är nästan visuellt omöjliga att särskilja för mänskliga ögon, men kan lura klassificerare att göra felaktiga förutsägelser. I den här avhandlingen studerades motståndarattacker och metoder för att förbättra djupinlärningsmodellers robusthet mot motståndarexempel. Fem olika algoritmer för motståndarattack implementerades. Dessa angreppsalgoritmer omfattade white-box-attacker och black-box-attacker, riktade attacker och icke-målinriktade attacker samt bildspecifika attacker och universella attacker. De negativa attackerna genererade motståndarexempel som ledde till en betydande minskning av klassificeringsnoggrannheten. Motståndsträning är en vanligt förekommande strategi för att förbättra djupinlärningsmodellernas robusthet mot motståndarexempel. Det visas att motståndsträning kan ge en ytterligare regulariseringsfördel utöver den som ges genom att använda dropout. Motståndsträning utförs genom att man införlivar motståndarexempel i träningsprocessen. Traditionellt används under denna process cross-entropy loss som förlustfunktion. För att förbättra djupinlärningsmodellernas robusthet mot motståndarexempel föreslår vi i den här avhandlingen två nya metoder för motståndsträning genom att tillämpa principen om maximal minskning av kodningshastigheten. Förlustfunktionen Maximal Coding Rate Reduction maximerar skillnaden i kodningshastighet mellan hela datamängden och summan av varje enskild klass. Vi utvärderade prestandan hos olika metoder för motståndsträning genom att jämföra ren noggrannhet, motstånds noggrannhet och lokal Lipschitzness. Det visades att motståndsträning med förlustfunktionen Maximal Coding Rate Reduction skulle ge ett mer robust nätverk än den traditionella motståndsträningsmetoden.

Machine learning has seen a rapid progress the last couple of decades, with more and more powerful neural network models continuously being presented. These neural networks require large amounts of data to train them. Labelled data is especially in great demand, but due to the time consuming and costly nature of data labelling, there exists a scarcity for labelled data, whereas there usually is an abundance of unlabelled data. In some cases, data from a certain distribution, or domain, is labelled, whereas the data we actually want to optimise our model on is unlabelled and from another domain. This falls under the umbrella of domain adaptation and the purpose of this thesis is to train a network using domain-adversarial training on eye image datasets consisting of a labelled source domain and an unlabelled target domain, with the goal of performing well on target data, i.e., overcoming the domain gap. This was done on two different datasets: a proprietary dataset from Tobii with real images and the public U2Eyes dataset with synthetic data. When comparing domain-adversarial training to a baseline model trained conventionally on source data and a oracle model trained conventionally on target data, the proposed DAT-ResNet model outperformed the baseline on both datasets. For the Tobii dataset, DAT-ResNet improved the Huber loss by 22.9% and the Intersection over Union (IoU) by 7.6%, and for the U2Eyes dataset, DAT-ResNet improved the Huber loss by 67.4% and the IoU by 37.6%. Furthermore, the IoU measures were extended to also include the portion of predicted ellipsis with no intersection with the corresponding ground truth ellipsis – referred to as zero-IoUs. By this metric, the proposed model improves the percentage of zero-IoUs by 34.9% on the Tobii dataset and by 90.7% on the U2Eyes dataset. / Maskininlärning har sett en snabb utveckling de senaste decennierna med mer och mer kraftfulla neurala nätverk-modeller presenterades kontinuerligt. Dessa neurala nätverk kräver stora mängder data för att tränas. Data med etiketter är det framförallt stor efterfrågan på, men på grund av det är tidskrävande och kostsamt att etikettera data så finns det en brist på sådan data medan det ofta finns ett överflöd av data utan etiketter. I vissa fall så är data från en viss fördelning, eller domän, etiketterad, medan datan som vi faktiskt vill optimera vår modell efter saknar etiketter och är från en annan domän. Det här faller under området domänadaptering och målet med det här arbetet är att träna ett nätverk genom att använda domänmoststående träning på dataset med ögonbilder som har en källdomän med etiketter och en måldomän utan etiketter, där målet är att prestera bra på data från måldomänen, i.e., att lösa ett domänadapteringsproblem. Det här gjordes på två olika dataset: ett dataset som ägs av Tobii med riktiga ögonbilder och det offentliga datasetet U2Eyes med syntetiska bilder. När domänadapteringsmodellen jämförs med en basmodell tränad konventionellt på källdata och en orakelmodell tränad konventionellt på måldata, så utklassar den presenterade DAT-ResNet-modellen basmodellen på båda dataseten. På Tobii-datasetet så förbättrade DAT-ResNet förlusten med 22.9% och Intersection over Union (IoU):n med 7.6%, och på U2Eyes-datasetet, förbättrade DAT-ResNet förlusten med 67.4% och IoU:n med 37.6%. Dessutom så utökades IoU-måtten till att också innefatta andelen av förutspådda ellipser utan något överlapp med tillhörande grundsanningsellipser – refererat till som noll-IoU:er. Enligt detta mått så förbättrar den föreslagna modellen noll-IoU:erna med 34.9% på Tobii-datasetet och 90.7% på U2Eyes-datasetet.

Parmi tous les jeux de société joués par les humains au cours de l’histoire, le jeu de go était considéré comme l’un des plus difficiles à maîtriser par un programme informatique [Van Den Herik et al., 2002]; Jusqu’à ce que ce ne soit plus le cas [Silveret al., 2016]. Cette percée révolutionnaire [Müller, 2002, Van Den Herik et al., 2002] fût le fruit d’une combinaison sophistiquée de Recherche arborescente Monte-Carlo et de techniques d’apprentissage automatique pour évaluer les positions du jeu, mettant en lumière le grand potentiel de l’apprentissage automatique pour résoudre des jeux. L’apprentissage antagoniste, un cas particulier de l’optimisation multiobjective, est un outil de plus en plus utile dans l’apprentissage automatique. Par exemple, les jeux à deux joueurs et à somme nulle sont importants dans le domain des réseaux génératifs antagonistes [Goodfellow et al., 2014] ainsi que pour maîtriser des jeux comme le Go ou le Poker en s’entraînant contre lui-même [Silver et al., 2017, Brown andSandholm, 2017]. Un résultat classique de la théorie des jeux indique que les jeux convexes-concaves ont toujours un équilibre [Neumann, 1928]. Étonnamment, les praticiens en apprentissage automatique entrainent avec succès une seule paire de réseaux de neurones dont l’objectif est un problème de minimax non-convexe et non-concave alors que pour une telle fonction de gain, l’existence d’un équilibre de Nash n’est pas garantie en général. Ce travail est une tentative d'établir une solide base théorique pour l’apprentissage dans les jeux. La première contribution explore le théorème minimax pour une classe particulière de jeux non-convexes et non-concaves qui englobe les réseaux génératifs antagonistes. Cette classe correspond à un ensemble de jeux à deux joueurs et a somme nulle joués avec des réseaux de neurones. Les deuxième et troisième contributions étudient l’optimisation des problèmes minimax, et plus généralement, les inégalités variationnelles dans le cadre de l’apprentissage automatique. Bien que la méthode standard de descente de gradient ne parvienne pas à converger vers l’équilibre de Nash de jeux convexes-concaves simples, il existe des moyens d’utiliser des gradients pour obtenir des méthodes qui convergent. Nous étudierons plusieurs techniques telles que l’extrapolation, la moyenne et la quantité de mouvement à paramètre négatif. La quatrième contribution fournit une étude empirique du comportement pratique des réseaux génératifs antagonistes. Dans les deuxième et troisième contributions, nous diagnostiquons que la méthode du gradient échoue lorsque le champ de vecteur du jeu est fortement rotatif. Cependant, une telle situation peut décrire un pire des cas qui ne se produit pas dans la pratique. Nous fournissons de nouveaux outils de visualisation afin d’évaluer si nous pouvons détecter des rotations dans comportement pratique des réseaux génératifs antagonistes. / Among all the historical board games played by humans, the game of go was considered one of the most difficult to master by a computer program [Van Den Heriket al., 2002]; Until it was not [Silver et al., 2016]. This odds-breaking break-through [Müller, 2002, Van Den Herik et al., 2002] came from a sophisticated combination of Monte Carlo tree search and machine learning techniques to evaluate positions, shedding light upon the high potential of machine learning to solve games. Adversarial training, a special case of multiobjective optimization, is an increasingly useful tool in machine learning. For example, two-player zero-sum games are important for generative modeling (GANs) [Goodfellow et al., 2014] and mastering games like Go or Poker via self-play [Silver et al., 2017, Brown and Sandholm,2017]. A classic result in Game Theory states that convex-concave games always have an equilibrium [Neumann, 1928]. Surprisingly, machine learning practitioners successfully train a single pair of neural networks whose objective is a nonconvex-nonconcave minimax problem while for such a payoff function, the existence of a Nash equilibrium is not guaranteed in general. This work is an attempt to put learning in games on a firm theoretical foundation. The first contribution explores minimax theorems for a particular class of nonconvex-nonconcave games that encompasses generative adversarial networks. The proposed result is an approximate minimax theorem for two-player zero-sum games played with neural networks, including WGAN, StarCrat II, and Blotto game. Our findings rely on the fact that despite being nonconcave-nonconvex with respect to the neural networks parameters, the payoff of these games are concave-convex with respect to the actual functions (or distributions) parametrized by these neural networks. The second and third contributions study the optimization of minimax problems, and more generally, variational inequalities in the context of machine learning. While the standard gradient descent-ascent method fails to converge to the Nash equilibrium of simple convex-concave games, there exist ways to use gradients to obtain methods that converge. We investigate several techniques such as extrapolation, averaging and negative momentum. We explore these techniques experimentally by proposing a state-of-the-art (at the time of publication) optimizer for GANs called ExtraAdam. We also prove new convergence results for Extrapolation from the past, originally proposed by Popov [1980], as well as for gradient method with negative momentum. The fourth contribution provides an empirical study of the practical landscape of GANs. In the second and third contributions, we diagnose that the gradient method breaks when the game’s vector field is highly rotational. However, such a situation may describe a worst-case that does not occur in practice. We provide new visualization tools in order to exhibit rotations in practical GAN landscapes. In this contribution, we show empirically that the training of GANs exhibits significant rotations around Local Stable Stationary Points (LSSP), and we provide empirical evidence that GAN training converges to a stable stationary point, which is a saddle point for the generator loss, not a minimum, while still achieving excellent performance.

L’apprentissage automatique repose pour un bon nombre de problèmes sur la minimisation d’une fonction de coût, pour ce faire il tire parti de la vaste littérature sur l’optimisation qui fournit des algorithmes et des garanties de convergences pour ce type de problèmes. Cependant récemment plusieurs modèles d’apprentissage automatique qui ne peuvent pas être formulé comme la minimisation d’un coût unique ont été propose, à la place ils nécessitent de définir un jeu entre plusieurs joueurs qui ont chaque leur propre objectif. Un de ces modèles sont les réseaux antagonistes génératifs (GANs). Ce modèle génératif formule un jeu entre deux réseaux de neurones, un générateur et un discriminateur, en essayant de tromper le discriminateur qui essaye de distinguer les vraies images des fausses, le générateur et le discriminateur s’améliore résultant en un équilibre de Nash, ou les images produites par le générateur sont indistinguable des vraies images. Malgré leur succès les GANs restent difficiles à entrainer à cause de la nature antagoniste du jeu, nécessitant de choisir les bons hyperparamètres et résultant souvent en une dynamique d’entrainement instable. Plusieurs techniques de régularisations ont été propose afin de stabiliser l’entrainement, dans cette thèse nous abordons ces instabilités sous l’angle d’un problème d’optimisation. Nous commençons par combler le fossé entre la littérature d’optimisation et les GANs, pour ce faire nous formulons GANs comme un problème d’inéquation variationnelle, et proposons de la littérature sur le sujet pour proposer des algorithmes qui convergent plus rapidement. Afin de mieux comprendre quels sont les défis de l’optimisation des jeux, nous proposons plusieurs outils afin d’analyser le paysage d’optimisation des GANs. En utilisant ces outils, nous montrons que des composantes rotationnelles sont présentes dans le voisinage des équilibres, nous observons également que les GANs convergent rarement vers un équilibre de Nash mais converge plutôt vers des équilibres stables locaux (LSSP). Inspirer par le succès des GANs nous proposons pour finir, une nouvelle famille de jeux que nous appelons adversarial example games qui consiste à entrainer simultanément un générateur et un critique, le générateur cherchant à perturber les exemples afin d’induire en erreur le critique, le critique cherchant à être robuste aux perturbations. Nous montrons qu’à l’équilibre de ce jeu, le générateur est capable de générer des perturbations qui transfèrent à toute une famille de modèles. / Many machine learning (ML) problems can be formulated as minimization problems, with a large optimization literature that provides algorithms and guarantees to solve this type of problems. However, recently some ML problems have been proposed that cannot be formulated as minimization problems but instead require to define a game between several players where each player has a different objective. A successful application of such games in ML are generative adversarial networks (GANs), where generative modeling is formulated as a game between a generator and a discriminator, where the goal of the generator is to fool the discriminator, while the discriminator tries to distinguish between fake and real samples. However due to the adversarial nature of the game, GANs are notoriously hard to train, requiring careful fine-tuning of the hyper-parameters and leading to unstable training. While regularization techniques have been proposed to stabilize training, we propose in this thesis to look at these instabilities from an optimization perspective. We start by bridging the gap between the machine learning and optimization literature by casting GANs as an instance of the Variational Inequality Problem (VIP), and leverage the large literature on VIP to derive more efficient and stable algorithms to train GANs. To better understand what are the challenges of training GANs, we then propose tools to study the optimization landscape of GANs. Using these tools we show that GANs do suffer from rotation around their equilibrium, and that they do not converge to Nash-Equilibria. Finally inspired by the success of GANs to generate images, we propose a new type of games called Adversarial Example Games that are able to generate adversarial examples that transfer across different models and architectures.