Society's newfound dependence on wireless transmission systems has driven demand for access to the electromagnetic (EM) spectrum to an all-time high. In particular, wireless applications related to the fifth generation (5G) of cellular technology along with statically allocated radar systems have contributed to the increasing scarcity of the sub 6 GHz frequency bands. As a result, development of Dynamic Spectrum Access (DSA) techniques for sharing these frequencies has become a critical research area for the greater wireless community. Since among incumbent systems, radars are the largest consumers of spectrum in the sub 6 GHz regime, and are being used increasingly for civilian applications such as traffic control, adaptive cruise control, and collision avoidance, the need for radars which can adaptively tune specific transmission parameters in an intelligent manner to promote coexistence with other systems has arisen. Thus, fully-aware, dynamic, cognitive radar has been proposed as target for radars to evolve towards. In this thesis, we extend current research thrusts towards cognitive radar to utilize Reinforcement Learning (RL) techniques which allow a radar system to learn desired behavior using information obtained from past transmissions. Since radar systems inherently interact with their electromagnetic environment, it is natural to view the use of reinforcement learning techniques as a straightforward extension to previous adaptive techniques. However, in designing learning algorithms for radar systems, we must carefully define goal-driven rewards, formalize the learning process, and consider an appropriate amount of environmental information. In this thesis, we apply well-established and emerging reinforcement learning approaches to meet the demands of modern radar coexistence problems. In particular, function estimation using deep neural networks is examined, as Deep RL presents a scalable learning framework which allows many environmental states to be considered in the decision-making process. We then show how these techniques can be used to improve traditional radar performance metrics, such as interference avoidance, spectral efficiency, and target detectibility with simulated and experimental results. We also compare the learning techniques to each other and naive approaches, such as fixed bandwidth radar and avoiding interference reactively. Finally, online learning strategies are considered which aim to balance the fundamental learning trade-off between exploration and exploitation. We show that online learning techniques can be used to select individual waveforms or applied as a high-level controller in a hierarchical learning scheme based on the biologically inspired concept of metacognition. The general use of RL techniques provides a robust framework for decision making under uncertainty that is more flexible than previously proposed cognitive radar strategies. Further, the wide array of RL models and algorithms allow the underlying structure to be applied to both small and large-scale radar scenarios. / Master of Science / Society's newfound dependence on wireless transmission systems has driven demand for control of the electromagnetic (EM) spectrum to an all-time high. In particular, federal spectrum auctions and the fifth generation of wireless technologies have contributed to the scarcity of frequency bands below 6GHz. These frequencies are widely used by both radar and communications systems due to favorable propagation characteristics. However, current radar systems typically occupy a fixed bandwidth and are tend to be poorly equipped to share their allocated spectrum with other users, which has become a necessity given the growth of wireless traffic. In this thesis, we study learning algorithms which enable a radar to optimize its electromagnetic pulses based on feedback from received signals. In particular, we are interested in reinforcement learning algorithms which allow a radar to learn optimal behavior based on rewards defined by a human. Using these algorithms, radar system designers can choose which metrics may be most important for a given radar application which can then be optimized for the given setting. However, scaling reinforcement learning to real-world problems such as radar optimization is often difficult due to the massive scope of the problem. Here we attempt to identify potential issues with implementation of each algorithm and narrow in on algorithms that are well-suited for real-time radar operation.

We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the $V_gamma$ dimension of a family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived.

Statistical learning (SL) is believed to be a mechanism that enables successful language acquisition. Language acquisition in turn is heavily influenced by environmental factors such as socioeconomic status (SES). However, it is unknown to what extent SL abilities interact with SES in affecting language outcomes. To examine this potential interaction, we measured event-related potentials (ERPs) in 38 children aged 7-12 while performing a visual SL task consisting of a sequence of stimuli that contained covert statistical probabilities that predicted a target stimulus. Hierarchical regression results indicated that SL ability moderated the relationship between SES (average of both caregiver’s education level) and language scores (grammar, and marginally with receptive vocabulary). For children with high SL ability, SES had a weaker effect on language compared to children with low SL ability, suggesting that having good SL abilities could help ameliorate the disadvantages associated with being raised in a family with lower SES.

In 1998, it was proved by Ben-David and Litman that a concept space has a sample compression scheme of size $d$ if and only if every finite subspace has a sample compression scheme of size $d$. In the compactness theorem, measurability of the hypotheses of the created sample compression scheme is not guaranteed; at the same time measurability of the hypotheses is a necessary condition for learnability. In this thesis we discuss when a sample compression scheme, created from compression schemes on finite subspaces via the compactness theorem, have measurable hypotheses. We show that if $X$ is a standard Borel space with a $d$-maximum and universally separable concept class $\m{C}$, then $(X,\CC)$ has a sample compression scheme of size $d$ with universally Borel measurable hypotheses. Additionally we introduce a new variant of compression scheme called a copy sample compression scheme.

Five experiments explored how we learn to read and recognise words with typical and atypical spelling-sound mappings and to generalize to novel words. In Experiment 1, adults learned to read pseudowords with typical or atypical pronunciations. There was some evidence that prior exposure to word meanings enhanced orthographic learning. However, interpretation was clouded by stimulus control problems that plague research using natural alphabets. In Experiment 2, an artificial orthography paradigm was developed to overcome these problems. Adults learned to read novel words written in novel symbols. Post-training, they could generalize, indicating extraction of individual symbol sounds. The frequency and predictability of symbol-sound mappings influenced learning and generalization, mirroring natural language findings. Experiment 3 found extended training to improve item recognition and generalization. In Experiment 4, pre-exposure to item sounds plus an object referent vs. item sounds provided equivalent benefit for orthographic learning. By the end of training, this was limited to items with low frequency unpredictable symbol-sound mappings. In Experiment 5, pre-exposure to novel definitions enhanced orthographic learning more than pre-exposure to item sounds, but by the end of training, both conditions were again equally beneficial.

Package party, implemented in the R system for statistical computing, provides basic classes and methods for recursive partitioning along with reference implementations for three recently-suggested tree-based learners: conditional inference trees and forests, and model-based recursive partitioning. / Series: Research Report Series / Department of Statistics and Mathematics

L'Apprentissage Automatique tire ses racines d'un large champ disciplinaire qui inclut l'Intelligence Artificielle, la Reconnaissance de Formes, les Statistiques ou l'Optimisation. Dès les origines de l'Apprentissage, les questions computationelles et les propriétés en généralisation ont toutes deux été identifiées comme centrales pour la discipline. Tandis que les premières concernent les questions de calculabilité ou de complexité (sur un plan fondamental) ou d'efficacité computationelle (d'un point de vue plus pratique) des systèmes d'apprentissage, les secondes visent a comprendre et caractériser comment les solutions qu'elles fournissent vont se comporter sur de nouvelles données non encore vues. Ces dernières années, l'émergence de jeux de données à grande échelle en Apprentissage Automatique a profondément remanié les principes de la Théorie de l'Apprentissage. En prenant en compte de potentielles contraintes sur le temps d'entraînement, il faut faire face à un compromis plus complexe que ceux qui sont classiquement traités par les Statistiques. Une conséquence directe tient en ce que la mise en place d'algorithmes efficaces (autant en théorie qu'en pratique) capables de tourner sur des jeux de données a grande échelle doivent impérativement prendre en compte les aspects statistiques et computationels de l'Apprentissage de façon conjointe. Cette thèse a pour but de mettre à jour, analyser et exploiter certaines des connections qui existent naturellement entre les aspects statistiques et computationels de l'Apprentissage. / Machine Learning is known to have its roots in a broad spectrum of fields including Artificial Intelligence, Pattern Recognition, Statistics or Optimisation. From the earliest stages of Machine Learning, both computational issues and generalisation properties have been identified as central to the field. While the former address the question of computability, complexity (from a fundamental perspective) or computational efficiency (on a more practical standpoint) of learning systems, the latter aim at understanding and characterising how well the solutions they provide perform on new, unseen data. Those last years, the emergence of large-scale datasets in Machine Learning has been deeply reshaping the principles of Learning Theory. Taking into account possible constraints on the training time, one has to deal with more complex trade-offs than the ones classically addressed by Statistics. As a direct consequence, designing new efficient algorithms (both in theory and practice), able to handle large-scale datasets, imposes to jointly deal with the statistical and computational aspects of Learning. The present thesis aims at unravelling, analysing and exploiting some of the connections that naturally exist between the statistical and computational aspects of Learning. More precisely, in a first part, we extend the stability analysis, which relates some algorithmic properties to the generalisation abilities of learning algorithms, to a novel (and fine-grain) performance measure, namely the confusion matrix. In a second part, we present a novel approach to learn a kernel-based regression function, that serves the learning task at hand and exploits the structure of

<p>This paper explores the effect of credit rating agency’s (CRA) reputation on the discretionary disclosures of corporate bond issuers. Academics, practitioners, and regulators disagree on the informational role played by major CRAs and the usefulness of credit ratings in influencing investors’ perception of the credit risk of bond issuers. Using management earnings forecasts as a measure of discretionary disclosure, I find that investors demand more (less) disclosure from bond issuers when the ratings become less (more) credible. In addition, using content analytics, I find that bond issuers disclose more qualitative information during periods of low CRA reputation to aid investors better assess credit risk. That the corporate managers alter their voluntary disclosure in response to CRA reputation shocks is consistent with credit ratings providing incremental information to investors and reducing adverse selection in lending markets. Overall, my findings suggest that managers rely on voluntary disclosure as a credible mechanism to reduce information asymmetry in bond markets.</p> / Dissertation

Climate change has emerged as one of the most critical issues of the 21st century. It stands to impact communities across the globe, forcing individuals and governments alike to adapt to a new environment. While it is critical for governments and organizations to make strides to change business as usual, individuals also have the ability to make an impact. The goal of this thesis is to study the beliefs that shape climate-related attitudes and the factors that drive the adoption of sustainable practices and technologies using a foundation in statistical learning. Previous research has studied the factors that influence both climate-related attitude and adoption, but comparatively little has been done to leverage recent advances in statistical learning and computing ability to advance our understanding of these topics. As increasingly large amounts of relevant data become available, it will be pivotal not only to use these emerging sources to derive novel insights on climate change, but to develop and improve statistical frameworks designed with climate change in mind. This thesis presents two novel applications of statistical learning to climate change, one of which includes a more general framework that can easily be extended beyond the field of climate change. Specifically, the work consists of two studies: (1) a robust integration of social media activity with climate survey data to relate climate-talk to climate-thought and (2) the development and validation of a statistical learning model to predict renewable energy installations using social, environmental, and economic predictors. The analysis presented in this thesis supports decision makers by providing new insights on the factors that drive climate attitude and adoption.

Os modelos de análise e decisão de concessão de crédito buscam associar o perfil do tomador de crédito à probabilidade do não pagamento de obrigações contraídas, identificando assim o risco associado ao tomador e auxiliando a firma a decidir pela aprovação ou negação da solicitação de crédito. Atualmente este campo de pesquisa tem ganhado importância no cenário nacional - pela intensificação da atividade de crédito no país com grande participação dos bancos públicos neste processo - e internacional - pelo aumento das preocupações com potenciais danos à economia derivados de eventos de default. Tal quadro fez com que fossem construídos e adaptados diversos modelos e métodos à análise de risco de crédito tanto para consumidores como para empresas. Estes modelos são testados e comparados com base na acurácia de previsão ou de métricas de otimização estatística. Este é um procedimento que pode não se mostrar eficiente do ponto de vista financeiro, ao mesmo tempo em que dificulta a interpretação e tomada de decisão por parte da firma quanto a qual o melhor modelo, gerando uma lacuna pelo desprendimento observado entre a decisão de qual o modelo a ser adotado e o objetivo financeiro da empresa. Tendo em vista que o desempenho financeiro é um dos principais indicadores de qualquer procedimento gerencial, o presente estudo objetivou preencher a esta lacuna analisando o desempenho financeiro de carteiras de crédito formadas por técnicas de aprendizagem estatística utilizadas atualmente na classificação e análise de risco de crédito em pesquisas nacionais e internacionais. A pesquisa selecionou as técnicas: análise discriminante, regressão logística, redes bayesianas Naïve Bayes, kdB-1, kdB-2, SVC e SVM e aplicou tais técnicas junto à base de dados German Credit Data Set. Os resultados foram analisados e comparados inicialmente em termos de acurácia e custos por erro de classificação. Adicionalmente a pesquisa propôs o emprego de quatro métricas financeiras (RFC, PLR, RAROC e IS), encontrando variações quanto aos resultados produzidos por cada técnica. Estes resultados sugerem variações quanto a sequência de eficiência e consequentemente de emprego das técnicas, demonstrando a importância da consideração destas métricas para a análise e decisão de seleção de modelos de classificação ótimos. / Analysis and decision credit concession models search for relating the borrower\'s credit profile to the nonpayment probability of their obligations, identifying risks related to borrower and helping decision firm to approve or deny the credit request. Currently this search field has increased in Brazilian scenario - by credit activity intensification into the country with a large public banks sharing - and in the international scenario - by growing concerns about economy potential damages resulting from default events. This position leads the construction and adaptation of several models and methods by credit risk analysis from both consumers and companies. These models have been tested and compared based on prediction of accuracy or other statistical optimization metrics. This proceed is eventually not effective when analyzed by a financial standpoint, in the same time that affects the understanding and decision of the enterprise about the best model, creating a gap in the decision model choice and the firm financial goals. Given that the financial performance is a foremost indicator of any management procedure, this study aimed to address this gap by the financial performance analysis of loan portfolios formed by statistical learning techniques currently used in the classification and credit risk analysis in national and international researches. The selected techniques (discriminant analysis, logistic regression, Bayesian networks Naïve Bayes , 1 - KDB , KDB - 2 , SVC and SVM) were applied to the German Credit Data Set and their results were initially analyzed and compared in terms of accuracy and misclassification costs. Regardless of these metrics the research has proposed to use four financial metrics (RFC, PLR, RAROC and IS), finding variations in the results of each statistical learning techniques. These results suggest variations in the sequence of efficiency and, ultimately, techniques choice, demonstrating the importance of considering these metrics for analysis and selection of decision models of optimal classification.