Adaptive Optimization Algorithms for Machine Learning

Machine learning assumes a pivotal role in our data-driven world. The increasing scale of models and datasets necessitates quick and reliable algorithms for model training. This dissertation investigates adaptivity in machine learning optimizers. The ensuing chapters are dedicated to various facets of adaptivity, including: 1. personalization and user-specific models via personalized loss, 2. provable post-training model adaptations via meta-learning, 3. learning unknown hyperparameters in real time via hyperparameter variance reduction, 4. fast O(1/k^2) global convergence of second-order methods via stepsized Newton method regardless of the initialization and choice basis, 5. fast and scalable second-order methods via low-dimensional updates. This thesis contributes novel insights, introduces new algorithms with improved convergence guarantees, and improves analyses of popular practical algorithms.Comment: Dissertation thesi

An E-Learning Investigation into Learning Style Adaptivity

Abstrac

A Conceptual Framework for Adapation

We present a white-box conceptual framework for adaptation. We called it CODA, for COntrol Data Adaptation, since it is based on the notion of control data. CODA promotes a neat separation between application and adaptation logic through a clear identification of the set of data that is relevant for the latter. The framework provides an original perspective from which we survey a representative set of approaches to adaptation ranging from programming languages and paradigms, to computational models and architectural solutions

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

A Conceptual Framework for Adapation

This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions

An E-Learning Investigation into Learning Style Adaptivity.

Abstrac

An E-Learning Investigation into Learning Style Adaptivity.

Abstrac

Un Framework Basé Bigraphes pour la Conception et l'Analyse des Systèmes Sensibles au Contexte

Today, modern technologies have become a part of our daily life. Whether to be informed, entertained, or even to communicate with friends, ubiquitous computing offers numerous opportunities. For this to become reality, computer systems must be able to observe the environment and to adapt their behaviour according to the users expectations and needs. This is called context-awareness. Indeed, the literature shows that context-awareness is the focal point of ubiquitous computing. However, due to heterogeneity and dynamicity of context information, taking it into account requires establishing a model to represent these information at a high-level of abstraction.In this thesis, we propose a model called BigCAS (Bigraphical Context-Aware System) that supports the design of context-aware systems. To achieve this goal, BigCAS is based on formal specifications, derived from bigraphical reactive systems, for modelling structural and behavioural aspects of context aware systems. It provides a clear separation between the context-aware part and the context-unaware part of these systems. Each part is modelled separately as a distinct bigraph, where the composition of these bigraphs models the general structure of the system, particularly, its components interactions and their side effects. Moreover, BigCAS considers not only structural aspects, but also the different reconfigurations involved in the behaviour of context aware systems.We also propose an extension to BigCAS, named BigCAS-FA (Bigraphical Context-Aware System - Formal Analysis), that provides formal verification of safety and liveness properties of context aware systems. Furthermore, BigCAS-FA provides contract-based strategies to guide the dynamic reconfiguration according to the context.To validate our proposals, we develop a prototype, BigCAS-Tool (Bigraphical Context Aware System - Tool), devoted to the specification and verification of context-aware systems. The proposed prototype is illustrated with a case study of a smart lighting system.Aujourd'hui, les nouvelles technologies font partie de notre vie quotidienne. Qu'il s'agisse de s'informer, de se divertir, ou même de communiquer avec ses amis, les possibilités qu'offre l'informatique ubiquitaire sont innombrables.Pour que ces possibilités puissent devenir une réalité, les systèmes informatiques doivent alors se doter d'une capacité d'observation de leur environnement et de s'adapter en fonction des attentes et des besoins des utilisateurs. C'est ce qu'on appelle la sensibilité au contexte. En effet, la littérature montre que la sensibilité au contexte est le point central de l'informatique ubiquitaire. Cependant, face à l'hétérogénéité et la dynamicité des informations de contexte, sa prise en compte nécessite la mise en place d'un modèle pour décrire ces informations à un haut niveau d'abstraction.Dans ce travail de thèse nous proposons, dans un premier temps, un modèle appelé BigCAS (Bigraphical Contexte-Aware System) qui permet la conception formelle des systèmes sensibles au contexte. Pour accomplir cet objectif, BigCAS repose sur des modèles formelles à base des systèmes réactifs bigraphiques permettant la modélisation des aspects structurels et comportementaux des systèmes sensibles au contexte. Il offre une séparation claire entre la partie sensible au contexte et la partie non-sensible au contexte de ces systèmes. Chacune de ces parties est modélisée séparément par un bigraphe distinct, où la composition de ceux-ci modélise la structure générale du système ainsi que les interactions et les effets de bord entre ses différents composants. Par ailleurs, BigCAS tient compte non seulement des aspects structurels, mais aussi des différentes reconfigurations intervenant dans le comportement des systèmes sensibles au contexte.Nous proposons également une extension du modèle BigCAS, appelée BigCAS-FA (Bigraphical Context-Aware System - Formal Analysis), qui permet la vérification formelle de propriétés de sûreté et de vivacité des systèmes sensibles au contexte. En outre, BigCAS-FA possède des stratégies à base de contrats qui consistent à guider la reconfiguration dynamique en fonction du contexte.Afin de valider nos propositions, nous développons le prototype BigCAS-Tool (Bigraphical Context Aware System - Tool) dédié à la spécification et la vérification des systèmes sensibles au contexte, et nous l'illustrons à travers une étude de cas d'un système d'éclairage intelligent

Adaptation based on learning style and knowledge level in e-learning systems

Although there have been numerous attempts to build and evaluate adaptive e-learning systems, they tend to be limited in scope, and suffer from a lack of carefully designed and controlled experimental evaluations of their effectiveness and usability. This thesis addresses these issues through the implementation of an adaptive e-learning system and its experimental validation. The design of an adaptive framework and the specific instantiation of its components into a configurable adaptive e-learning system are presented. The domain model of the system deals with computer security. The learner model incorporates the information perception dimension of the Felder-Silverman model of learning style and also knowledge level. The adaptation model generates personalised learning paths and offers adaptive guidance and recommendation. The thesis also provides an empirical evaluation through three controlled experiments to investigate the effect of different forms of adaptation. Rigorous experimental design, careful investigation and precise reporting of results are taken into account in all the three experiments. The findings indicate that matching the sequence of learning objects to the information perception learning style yields significantly better learning outcome and learner satisfaction than non-matching sequences. They also indicate that adaptation based on the combination of the information perception learning style and knowledge level yields significantly better learning outcome (both in the short- and long-term) and learner satisfaction than adaptation based on either of these learner characteristics alone; this combination is also marked by a significantly higher level of perceived usability compared to a non-adaptive version of the e-learning system