Beyond Classical Statistics: Optimality In Transfer Learning And Distributed Learning

During modern statistical learning practice, statisticians are dealing with increasingly huge, complicated and structured data sets. New opportunities can be found during the learning process with better structured data sets as well as powerful data analytic resources. Also, there are more and more challenges we need to address when dealing with large data sets, due to limitation of computation, communication resources or privacy concerns. Under decision-theoretical framework, statistical optimality should be reconsidered with new type of data or new constraints. Under the framework of minimax theory, this thesis aims to address the following four problems:1. The first part of this thesis aims to develop an optimality theory for transfer learning for nonparametric classification. An near optimal adaptive classifier is also established. 2. In the second part, we study distributed Gaussian mean estimation with known vari- ance under communication constraints. The exact distributed minimax rate of con- vergence is derived under three different communication protocols. 3. In the third part, we study distributed Gaussian mean estimation with unknown vari- ance under communication constraints. The results show that the amount of additional communication cost depends on the type of underlying communication protocol. 4. In the fourth part, we investigate the minimax optimality and communication cost of adaptation for distributed nonparametric function estimation under communication constraints

A Data Fusion Approach to Automated Decision Making in Intelligent Vehicles

The goal of an intelligent transportation system is to increase safety, convenience and efficiency in driving. Besides these obvious advantages, the integration of intelligent features and autonomous functionalities on vehicles will lead to major economic benefits from reduced fuel consumption to efficient exploitation of the road network. While giving this information to the driver can be useful, there is also the possibility of overloading the driver with too much information. Existing vehicles already have some mechanisms to take certain actions if the driver fails to act. Future vehicles will need more complex decision making modules which receive the raw data from all available sources, process this data and inform the driver about the existing or impending situations and suggest, or even take actions. Intelligent vehicles can take advantage of using different sources of data to provide more reliable and more accurate information about driving situations and build a safer driving environment. I have identified five general sources of data which is available for intelligent vehicles: the vehicle itself, cameras on the vehicle, communication between the vehicle and other vehicles, communications between vehicles and roadside units and the driver information. But facing this huge amount of data requires a decision making module to collect this data and provide the best reaction based on the situation. In this thesis, I present a data fusion approach for decision making in vehicles in which a decision making module collects data from the available sources of information and analyses this data and provides the driver with helpful information such as traffic congestion, emergency messages, etc. The proposed approach uses agents to collect the data and the agents cooperate using a black board method to provide the necessary data for the decision making system. The Decision making system benefits from this data and provides the intelligent vehicle applications with the best action(s) to be taken. Overall, the results show that using this data fusion approach for making decision in vehicles shows great potential for improving performance of vehicular systems by reducing travel time and wait time and providing more accurate information about the surrounding environment for vehicles. In addition, the safety of vehicles will increase since the vehicles will be informed about the hazard situations

Remote control and monitoring of power systems

Includes synopsis.Includes bibliographical references (leaves 87-93).Power systems are typically complex and can be affected by their environment in ways that cannot be completely predicted by their designers. It is thus imperative that monitoring is considered as part of the design of new power systems. Due to the associated costs of maintenance, repair, and downtime, monitoring these systems is particularly important when the installations are remote. Remote locations benefit greatly from renewable energy sources. As a result, this work focuses on a novel Hybrid Inverter system developed by Optimal Power Solutions Pty. Ltd. (OPS). This system uses renewable energy sources, grid power, and diesel generators together with a bi-directional inverter to supply a remote location with grid-quality power

A Graduatte Level Immersive-Simulattion Program for Teaching and Assessing Fundamental Skills in Entry Level Clinical Perfusionists.

Background: The clinical perfusionist is a member of the open-heart-surgery team and responsible for operating the life support equipment that replaces the function of the patient\u27s heart and lungs and arrests and restarts the patient\u27s heart in the course of a Cardiopulmonary Bypass (CPB) procedure. In the perfusionists scope of practice, the consequence of unskilled actions, inaccurate understanding or delayed decision making may result in significant patient morbidity or even death. Historically, perfusion students have learned and practiced their skills within a clinical preceptorship program in which an experienced clinician allows the novice student to operate the life support equipment under their direct supervision and consultation. While there is clinical evidence from numerous surgical specialties which establishes that learning curve associated errors have a negative effect on patient outcomes, this has not been researched for clinical perfusionists. Despite this evidence gap, the professions leaders have been instrumental in driving educational innovation and the development of medical simulation models that may reduce the patient\u27s exposure to learning curve associated morbidity by developing competence with high-risk clinical skills prior to patient contact. The purpose of this research is to develop, validate and apply novel medical simulation techniques and technologies to the preparation of entry level clinical perfusionists and demonstrate pre-clinical competence with the fundamental perfusion skills.Methods and Results: To inform the development of a skills curriculum we conducted two national surveys using online survey tools. Through these surveys we validated a list of fundamental skills, and the deconstructed sub-elements involved in the conduct of these skills. Additionally, we identified the typical ranges of physiologic and technical parameters that clinicians maintain during clinical procedures. With this foundational benchmark data we validated the performance of a simulated patient to establish that the patient surrogate generates data that is substantially similar to the physiologic and technical data that a perfusionist would manage during a live clinical procedure. This validated simulation technology was then incorporated into a high-fidelity simulation suite and applied to an innovative immersive curriculum which included hands on repetitive practice, live and video supported self, peer and expert observation and feedback as well as a battery of high-stakes assessments. The validity and fidelity of the simulated experience was established through analysis of over 800 opinions generated over 10 years by novice and expert perfusionists after performing simulated cases. Finally, the efficacy of the simulation curriculum was assessed by comparing our simulation trained students to a national pool of their peers from other schools and expert clinicians. Through this process we generated the first measurements of the typical learning curve for the fundamental skills of CPB, the first estimates of error rates for students navigating the learning curve and the first benchmark measures of competent performance in a simulated environment. This data establishes that students learning in traditional clinical training programs conduct three-fold more errors than experts and will have approximately 99 high-risk patient encounters prior to developing competence with fundamental skills. By comparison, simulation trained students demonstrated competence with fundamental skills that was similar to the experts with almost no high-risk patient encounters. Discussion: The implications to patient safety are clearly implied. These studies establish that there is a high level of agreement among clinicians regarding the skills that are necessary to operate perfusion equipment and that realistic simulation environments can be designed and applied to the development of student\u27s fundamental perfusion skills without exposing patients to the threat of students learning curve associated errors. This data may catalyze a larger national dialog regarding Entrustable Professional Activities for perfusionists and influence national accreditation standards for educational programs

Aprendizagem de coordenação em sistemas multi-agente

The ability for an agent to coordinate with others within a system is a valuable property in multi-agent systems. Agents either cooperate as a team to accomplish a common goal, or adapt to opponents to complete different goals without being exploited. Research has shown that learning multi-agent coordination is significantly more complex than learning policies in singleagent environments, and requires a variety of techniques to deal with the properties of a system where agents learn concurrently. This thesis aims to determine how can machine learning be used to achieve coordination within a multi-agent system. It asks what techniques can be used to tackle the increased complexity of such systems and their credit assignment challenges, how to achieve coordination, and how to use communication to improve the behavior of a team. Many algorithms for competitive environments are tabular-based, preventing their use with high-dimension or continuous state-spaces, and may be biased against specific equilibrium strategies. This thesis proposes multiple deep learning extensions for competitive environments, allowing algorithms to reach equilibrium strategies in complex and partially-observable environments, relying only on local information. A tabular algorithm is also extended with a new update rule that eliminates its bias against deterministic strategies. Current state-of-the-art approaches for cooperative environments rely on deep learning to handle the environment’s complexity and benefit from a centralized learning phase. Solutions that incorporate communication between agents often prevent agents from being executed in a distributed manner. This thesis proposes a multi-agent algorithm where agents learn communication protocols to compensate for local partial-observability, and remain independently executed. A centralized learning phase can incorporate additional environment information to increase the robustness and speed with which a team converges to successful policies. The algorithm outperforms current state-of-the-art approaches in a wide variety of multi-agent environments. A permutation invariant network architecture is also proposed to increase the scalability of the algorithm to large team sizes. Further research is needed to identify how can the techniques proposed in this thesis, for cooperative and competitive environments, be used in unison for mixed environments, and whether they are adequate for general artificial intelligence.A capacidade de um agente se coordenar com outros num sistema é uma propriedade valiosa em sistemas multi-agente. Agentes cooperam como uma equipa para cumprir um objetivo comum, ou adaptam-se aos oponentes de forma a completar objetivos egoístas sem serem explorados. Investigação demonstra que aprender coordenação multi-agente é significativamente mais complexo que aprender estratégias em ambientes com um único agente, e requer uma variedade de técnicas para lidar com um ambiente onde agentes aprendem simultaneamente. Esta tese procura determinar como aprendizagem automática pode ser usada para encontrar coordenação em sistemas multi-agente. O documento questiona que técnicas podem ser usadas para enfrentar a superior complexidade destes sistemas e o seu desafio de atribuição de crédito, como aprender coordenação, e como usar comunicação para melhorar o comportamento duma equipa. Múltiplos algoritmos para ambientes competitivos são tabulares, o que impede o seu uso com espaços de estado de alta-dimensão ou contínuos, e podem ter tendências contra estratégias de equilíbrio específicas. Esta tese propõe múltiplas extensões de aprendizagem profunda para ambientes competitivos, permitindo a algoritmos atingir estratégias de equilíbrio em ambientes complexos e parcialmente-observáveis, com base em apenas informação local. Um algoritmo tabular é também extendido com um novo critério de atualização que elimina a sua tendência contra estratégias determinísticas. Atuais soluções de estado-da-arte para ambientes cooperativos têm base em aprendizagem profunda para lidar com a complexidade do ambiente, e beneficiam duma fase de aprendizagem centralizada. Soluções que incorporam comunicação entre agentes frequentemente impedem os próprios de ser executados de forma distribuída. Esta tese propõe um algoritmo multi-agente onde os agentes aprendem protocolos de comunicação para compensarem por observabilidade parcial local, e continuam a ser executados de forma distribuída. Uma fase de aprendizagem centralizada pode incorporar informação adicional sobre ambiente para aumentar a robustez e velocidade com que uma equipa converge para estratégias bem-sucedidas. O algoritmo ultrapassa abordagens estado-da-arte atuais numa grande variedade de ambientes multi-agente. Uma arquitetura de rede invariante a permutações é também proposta para aumentar a escalabilidade do algoritmo para grandes equipas. Mais pesquisa é necessária para identificar como as técnicas propostas nesta tese, para ambientes cooperativos e competitivos, podem ser usadas em conjunto para ambientes mistos, e averiguar se são adequadas a inteligência artificial geral.Apoio financeiro da FCT e do FSE no âmbito do III Quadro Comunitário de ApoioPrograma Doutoral em Informátic