If customers respond differently to a campaign, it is worthwhile to find those customers who respond most positively and direct the campaign towards them. This can be done by using so called incremental response analysis where respondents from a campaign are compared with respondents from a control group. Customers with the highest increased response from the campaign will be selected and thus may increase the company’s return. Incremental response analysis is applied to the mobile operator Tres historical data. The thesis intends to investigate which method that best explain the incremental response, namely to find those customers who give the highest incremental response of Tres customers, and what characteristics that are important.The analysis is based on various classification methods such as logistic regression, Lassoregression and decision trees. RMSE which is the root mean square error of the deviation between observed and predicted incremental response, is used to measure the incremental response prediction error. The classification methods are evaluated by Hosmer-Lemeshow test and AUC (Area Under the Curve). Bayesian logistic regression is also used to examine the uncertainty in the parameter estimates.The Lasso regression performs best compared to the decision tree, the ordinary logistic regression and the Bayesian logistic regression seen to the predicted incremental response. Variables that significantly affect the incremental response according to Lasso regression are age and how long the customer had their subscription.

Given the "right" representation, learning is easy. This thesis studies representation learning and meta-learning, with a special focus on sparse representations. Meta-learning is fundamental to machine learning, and it translates to learning to learn itself. The presentation unfolds in two parts. In the first part, we establish learning theoretic results for learning sparse representations. The second part introduces new multi-task and meta-learning paradigms for representation learning. On the sparse representations front, our main pursuits are generalization error bounds to support a supervised dictionary learning model for Lasso-style sparse coding. Such predictive sparse coding algorithms have been applied with much success in the literature; even more common have been applications of unsupervised sparse coding followed by supervised linear hypothesis learning. We present two generalization error bounds for predictive sparse coding, handling the overcomplete setting (more original dimensions than learned features) and the infinite-dimensional setting. Our analysis led to a fundamental stability result for the Lasso that shows the stability of the solution vector to design matrix perturbations. We also introduce and analyze new multi-task models for (unsupervised) sparse coding and predictive sparse coding, allowing for one dictionary per task but with sharing between the tasks' dictionaries. The second part introduces new meta-learning paradigms to realize unprecedented types of learning guarantees for meta-learning. Specifically sought are guarantees on a meta-learner's performance on new tasks encountered in an environment of tasks. Nearly all previous work produced bounds on the expected risk, whereas we produce tail bounds on the risk, thereby providing performance guarantees on the risk for a single new task drawn from the environment. The new paradigms include minimax multi-task learning (minimax MTL) and sample variance penalized meta-learning (SVP-ML). Regarding minimax MTL, we provide a high probability learning guarantee on its performance on individual tasks encountered in the future, the first of its kind. We also present two continua of meta-learning formulations, each interpolating between classical multi-task learning and minimax multi-task learning. The idea of SVP-ML is to minimize the task average of the training tasks' empirical risks plus a penalty on their sample variance. Controlling this sample variance can potentially yield a faster rate of decrease for upper bounds on the expected risk of new tasks, while also yielding high probability guarantees on the meta-learner's average performance over a draw of new test tasks. An algorithm is presented for SVP-ML with feature selection representations, as well as a quite natural convex relaxation of the SVP-ML objective.

In this thesis, we introduce our lasso-type grasping scheme. This 3-D lasso-type grasping scheme, different from the previously performed grasping schemes which are either planar or fixed base, is the novelty of our approach where the snake robot grasps an object with any of its body links which are at close proximity to the object while undergoing its serpentine motion with the remaining links and dragging the grasped object. Since our snake robot has the pitch motion for every link, we can ensure that the links do not run into each other as they wrap around the object. A lasso-type power grasp is then possible for our 15-link snake robot as seen in the simulation results of this thesis. Furthermore we develop the kinematic and control models for lasso-type grasping and for dragging the grasped object to a desired state. This control model includes an adaptively changing feedback gain which prevents excessively large inputs to corrupt the serpentine locomotion control. According to our lasso-type grasping model, while the snake robot can grasp the object beginning with the any body link at close proximity of the object, the contact points can be controlled to generate the curvilinear grasping curve by using our lasso-type grasping procedure. For dragging the grasped object, we define a scheme which can determine the appropriate desired state to drag the grasped object to a desired position. The stability of the grasped object is important to resist the disturbance forces as well as the force closure grasping is important to counteract the disturbance force. To analyze the stability of the lasso-type grasping, we introduce a stability model of lasso-type grasping based on contact stiffness matrices that faces the snake to regrasp when gone unstable.

Esta tese trata de investigar as configurações das práticas equestres no estado do Rio Grande do Sul, no século XX. A proposição da pesquisa parte da noção de que as práticas tiveram um papel relevante para a história do Rio Grande do Sul, em particular nos campos da sociabilidade, lazer e preservação das culturas. No cenário sul-rio-grandense, a pesquisa dedica atenção ao desenvolvimento das carreiras de cancha reta, do turfe, do hipismo, do tiro de laço, do freio de ouro e da equoterapia. Tais práticas equestres foram conjecturadas por uma perspectiva socio-histórica, cuja análise foi guiada pela categoria ―configuração‖. Esta foi operacionalizada a partir da obra de Norbert Elias, conduzindo o estudo no sentido de compreender o processo de emergência, distinção e as relações de interdependência estabelecidas entre as práticas equestres no estado. A investigação assentou-se na análise de documentos escritos e impressos, os quais foram concebidos enquanto materiais e textos históricos, portadores de mensagens, sentidos e intuitos reservados à sua conjuntura. As fontes revelaram que o processo de desenvolvimento de configurações no cenário equestre sul-rio-grandense sublinhou uma reconstrução da variada e heterogênea rede de interdependências entre os domínios socializadores representados pelas corridas de cavalos, pelo hipismo, pela equoterapia e pelo tiro de laço. De igual forma, as transformações ocorridas no contexto destes domínios socializadores, ao longo do tempo, derivam das relações de interdependência entre os mesmos, nas esferas do trabalho, da cultura, do lazer, do esporte e da reabilitação. Por conseguinte, a noção de configuração entre práticas equestres pode auxiliar a compreender um campo mais amplo de interações e intercâmbios entre os esportes em geral enquanto domínios socializadores. / This thesis is to investigate the configurations of equestrian practices in the state of Rio Grande do Sul, in the twentieth century. The proposition of the research builds on the notion that the practices had a significant role in the history of Rio Grande do Sul, in particular in the fields of sociability, leisure and preservation of cultures. In the scenario of Rio Grande do Sul, the research devoted attention to the development of straight line horse races, horse racing, equestrianism, shot of lasso with horse and equine-assisted therapy. Such equestrian practices were conjectured by a sociohistorical perspective, whose analysis was guided by the category "configuration". This was operationalized from Norbert Elias work, leading the study in order to understand the process of emergence, distinction and interdependence of relations between the equestrian sports practices in the state. The research was based on the analysis of written and printed documents, which are designed as historical materials and texts, carrying messages, meanings and intentions reserved to their conjuncture. The sources revealed that a configuration development process in Rio Grande do Sul‘s equestrian scene emphasized a reconstruction of the varied and heterogeneous network of independencies between socializing areas represented by the horse racing, equestrian sports, equine-assisted therapy and shot of lasso with horse. Similarly, the changes occurred in the context of these socializing areas, over time, derived from the interrelationship between them, in the spheres of work, culture, leisure, sport and rehabilitation. Therefore, the notion of a configuration between equestrian practices can help understanding a new broader field of interactions and exchanges between the sports in general as socializing areas.

Esta tese trata de investigar as configurações das práticas equestres no estado do Rio Grande do Sul, no século XX. A proposição da pesquisa parte da noção de que as práticas tiveram um papel relevante para a história do Rio Grande do Sul, em particular nos campos da sociabilidade, lazer e preservação das culturas. No cenário sul-rio-grandense, a pesquisa dedica atenção ao desenvolvimento das carreiras de cancha reta, do turfe, do hipismo, do tiro de laço, do freio de ouro e da equoterapia. Tais práticas equestres foram conjecturadas por uma perspectiva socio-histórica, cuja análise foi guiada pela categoria ―configuração‖. Esta foi operacionalizada a partir da obra de Norbert Elias, conduzindo o estudo no sentido de compreender o processo de emergência, distinção e as relações de interdependência estabelecidas entre as práticas equestres no estado. A investigação assentou-se na análise de documentos escritos e impressos, os quais foram concebidos enquanto materiais e textos históricos, portadores de mensagens, sentidos e intuitos reservados à sua conjuntura. As fontes revelaram que o processo de desenvolvimento de configurações no cenário equestre sul-rio-grandense sublinhou uma reconstrução da variada e heterogênea rede de interdependências entre os domínios socializadores representados pelas corridas de cavalos, pelo hipismo, pela equoterapia e pelo tiro de laço. De igual forma, as transformações ocorridas no contexto destes domínios socializadores, ao longo do tempo, derivam das relações de interdependência entre os mesmos, nas esferas do trabalho, da cultura, do lazer, do esporte e da reabilitação. Por conseguinte, a noção de configuração entre práticas equestres pode auxiliar a compreender um campo mais amplo de interações e intercâmbios entre os esportes em geral enquanto domínios socializadores. / This thesis is to investigate the configurations of equestrian practices in the state of Rio Grande do Sul, in the twentieth century. The proposition of the research builds on the notion that the practices had a significant role in the history of Rio Grande do Sul, in particular in the fields of sociability, leisure and preservation of cultures. In the scenario of Rio Grande do Sul, the research devoted attention to the development of straight line horse races, horse racing, equestrianism, shot of lasso with horse and equine-assisted therapy. Such equestrian practices were conjectured by a sociohistorical perspective, whose analysis was guided by the category "configuration". This was operationalized from Norbert Elias work, leading the study in order to understand the process of emergence, distinction and interdependence of relations between the equestrian sports practices in the state. The research was based on the analysis of written and printed documents, which are designed as historical materials and texts, carrying messages, meanings and intentions reserved to their conjuncture. The sources revealed that a configuration development process in Rio Grande do Sul‘s equestrian scene emphasized a reconstruction of the varied and heterogeneous network of independencies between socializing areas represented by the horse racing, equestrian sports, equine-assisted therapy and shot of lasso with horse. Similarly, the changes occurred in the context of these socializing areas, over time, derived from the interrelationship between them, in the spheres of work, culture, leisure, sport and rehabilitation. Therefore, the notion of a configuration between equestrian practices can help understanding a new broader field of interactions and exchanges between the sports in general as socializing areas.

Submitted by Delson Barros (delsonbarros2@gmail.com) on 2018-05-30T00:14:41Z No. of bitstreams: 1 delson_final.pdf: 846097 bytes, checksum: 752b58fefaf3d19312e764af6cbac574 (MD5) / Rejected by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br), reason: Prezado, bom dia. o seu documento foi rejeitado por não estar no padrão FGV. Favor realizar uma nova submissão. atenciosamente Gilson on 2018-06-05T13:14:50Z (GMT) / Submitted by Delson Barros (delsonbarros2@gmail.com) on 2018-06-06T02:16:32Z No. of bitstreams: 1 delson_dissertacao.pdf: 852210 bytes, checksum: 75c1cbc72dcf7007f64365447409fc76 (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2018-07-06T13:52:28Z (GMT) No. of bitstreams: 1 delson_dissertacao.pdf: 852210 bytes, checksum: 75c1cbc72dcf7007f64365447409fc76 (MD5) / Made available in DSpace on 2018-07-16T19:53:51Z (GM en T). No. of bitstreams: 1 delson_dissertacao.pdf: 852210 bytes, checksum: 75c1cbc72dcf7007f64365447409fc76 (MD5) Previous issue date: 2018-04-20 / A despeito da elegância teórica do fator estocástico de desconto, sua especificação empírica é demasiado ampla. Utilizando fatores construídos como excessos de retornos que usufruem das propriedades de componentes principais, o presente estudo propõe metodologias de estimação do fator estocástico de desconto com representação esparsa e que sofrem pouca elevação no erro de apreçamento na presença de um grande cross-section. O trabalho aplica uma combinação das técnicas de análise de componentes principais e regularização Lasso que concilia duas propriedades desejáveis: (i) representação esparsa e economicamente significativa, e (ii) baixo erro de apreçamento fora da amostra. Este resultado vem em conjunto com uma deterioração moderada do erro de apreçamento dentro da amostra.

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Maximizing hotel's revenue is a hard and complicated task. A lot of aspects have to be taken into consideration during this procedure. One major variable is the competitors. Identifying hotel's competitors and using their behavior could be a crucial advantage for maximizing hotel's revenue. For this reason, in this thesis the effect of competitors' pricing in the demand forecasting is being studied. Five hotels with approximately 13 months of historical data are available. During the study two GLM models were tested, Poisson and Negative Binomial regression. As a baseline, the models were modeled only with time related features. Competitors' pricing features were added to the baseline models in order to test the effect of the competition. The feature extraction procedure was implemented by the author of the thesis. A LASSO regression was used for feature selection. The effect of competition is tested in terms of the performance of the models and their forecasting behavior. Moreover, it is of great interest to test, whether any of the features or class of features proposed in this study dominates among others. In order for the goals of the project to be accomplished, the evaluation procedure was split in two parts. In the first one, all models were trained into approximately 12 months of historical data. The goodness of fit was tested by a likelihood ratio test and the forecasting behavior for the remaining one month was commented. For the second part of evaluation, a cross validation forecasting error was computed for two different forecasting windows (7 days and 30 days). The evaluation metrics were RMSE, sMAPE and CMAE. The results showed that adding competitors' features improved the goodness of fit of the models. The cross validation forecasting error results differ among hotels. For some of them, adding competitors' features led to an important decrease of the error metrics. For some others, the decrease was not so high, but it still occurred. On average of all hotels, the three metrics were decreased for both 7 and 30 days of forecasting window. For the forecasting behavior, the models with competitors' features were able to capture peaks in specific days, something that the models without competitors could not. The results also showed, that none of the features or group of features proposed in this thesis dominates among others. Consequently, adding competitors' features in the models appeared to have a positive effect to the demand forecasting by improving the performance of the models.

Cette thèse, organisée en deux parties indépendantes, a pour objet la sémantique distributionnelle et la sélection de variables. Dans la première partie, nous introduisons une nouvelle méthode pour l'apprentissage de représentations de mots à partir de grandes quantités de texte brut. Cette méthode repose sur un modèle probabiliste de la phrase, utilisant modèle de Markov caché et arbre de dépendance. Nous présentons un algorithme efficace pour réaliser l'inférence et l'apprentissage dans un tel modèle, fondé sur l'algorithme EM en ligne et la propagation de message approchée. Nous évaluons les modèles obtenus sur des taches intrinsèques, telles que prédire des jugements de similarité humains ou catégoriser des mots et deux taches extrinsèques~: la reconnaissance d'entités nommées et l'étiquetage en supersens. Dans la seconde partie, nous introduisons, dans le contexte des modèles linéaires, une nouvelle pénalité pour la sélection de variables en présence de prédicteurs fortement corrélés. Cette pénalité, appelée trace Lasso, utilise la norm trace des prédicteurs sélectionnés, qui est une relaxation convexe de leur rang, comme critère de complexité. Le trace Lasso interpole les normes $\ell_1$ et $\ell_2$. En particulier, lorsque tous les prédicteurs sont orthogonaux, il est égal à la norme $\ell_1$, tandis que lorsque tous les prédicteurs sont égaux, il est égal à la norme $\ell_2$. Nous proposons deux algorithmes pour calculer la solution du problème de régression aux moindres carrés regularisé par le trace Lasso et réalisons des expériences sur des données synthétiques. / This thesis, which is organized in two independent parts, presents work on distributional semantics and on variable selection. In the first part, we introduce a new method for learning good word representations using large quantities of unlabeled sentences. The method is based on a probabilistic model of sentence, using a hidden Markov model and a syntactic dependency tree. The latent variables, which correspond to the nodes of the dependency tree, aim at capturing the meanings of the words. We develop an efficient algorithm to perform inference and learning in those models, based on online EM and approximate message passing. We then evaluate our models on intrinsic tasks such as predicting human similarity judgements or word categorization, and on two extrinsic tasks: named entity recognition and supersense tagging. In the second part, we introduce, in the context of linear models, a new penalty function to perform variable selection in the case of highly correlated predictors. This penalty, called the trace Lasso, uses the trace norm of the selected predictors, which is a convex surrogate of their rank, as the criterion of model complexity. The trace Lasso interpolates between the $\ell_1$-norm and $\ell_2$-norm. In particular, it is equal to the $\ell_1$-norm if all predictors are orthogonal and to the $\ell_2$-norm if all predictors are equal. We propose two algorithms to compute the solution of least-squares regression regularized by the trace Lasso, and perform experiments on synthetic datasets to illustrate the behavior of the trace Lasso.

Air pollution and meteorological conditions impact COVID-19 mortality rates. This research studied Quito, Ecuador, using Granger causality tests and regression models to investigate the relationship between pollutants, meteorological variables, human mobility, and excess deaths. Results suggested that Mobility as defined by Google Mobility Index, Facebook Isolation Index, in addition to Nitrogen Dioxide, and Sulphur Dioxide significantly impact excess deaths, while Carbon Monoxide and Relative Humidity have mixed results. Measures to reduce Carbon Monoxide emissions and increase humidity levels may mitigate the impact of air pollution on COVID-19 mortality rates. Further research is needed to investigate the impact of pollutants on COVID-19 transmission in other locations. Healthcare decision-makers must monitor and mitigate the impact of pollutants, promote healthy air quality policies, and encourage physical activity in safe environments. They must also consider meteorological conditions and implement measures such as increased ventilation and air conditioning to reduce exposure. Additionally, they must consider human mobility and reduce it to slow the spread of the diseases. Decisionmakers must monitor and track excess deaths during the pandemic to understand the impact of pollutants, meteorological conditions, and human mobility on human health. Public education is critical to raising awareness of air quality and its impact on health. Encouraging individuals to reduce their exposure to pollutants and meteorological conditions can play a critical role in mitigating the impact of air pollution on respiratory health during the pandemic.