2,858 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    On the Generation of Realistic and Robust Counterfactual Explanations for Algorithmic Recourse

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    This recent widespread deployment of machine learning algorithms presents many new challenges. Machine learning algorithms are usually opaque and can be particularly difficult to interpret. When humans are involved, algorithmic and automated decisions can negatively impact people’s lives. Therefore, end users would like to be insured against potential harm. One popular way to achieve this is to provide end users access to algorithmic recourse, which gives end users negatively affected by algorithmic decisions the opportunity to reverse unfavorable decisions, e.g., from a loan denial to a loan acceptance. In this thesis, we design recourse algorithms to meet various end user needs. First, we propose methods for the generation of realistic recourses. We use generative models to suggest recourses likely to occur under the data distribution. To this end, we shift the recourse action from the input space to the generative model’s latent space, allowing to generate counterfactuals that lie in regions with data support. Second, we observe that small changes applied to the recourses prescribed to end users likely invalidate the suggested recourse after being nosily implemented in practice. Motivated by this observation, we design methods for the generation of robust recourses and for assessing the robustness of recourse algorithms to data deletion requests. Third, the lack of a commonly used code-base for counterfactual explanation and algorithmic recourse algorithms and the vast array of evaluation measures in literature make it difficult to compare the per formance of different algorithms. To solve this problem, we provide an open source benchmarking library that streamlines the evaluation process and can be used for benchmarking, rapidly developing new methods, and setting up new experiments. In summary, our work contributes to a more reliable interaction of end users and machine learned models by covering fundamental aspects of the recourse process and suggests new solutions towards generating realistic and robust counterfactual explanations for algorithmic recourse

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Adaptive Sparse Gaussian Process

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    Adaptive learning is necessary for non-stationary environments where the learning machine needs to forget past data distribution. Efficient algorithms require a compact model update to not grow in computational burden with the incoming data and with the lowest possible computational cost for online parameter updating. Existing solutions only partially cover these needs. Here, we propose the first adaptive sparse Gaussian Process (GP) able to address all these issues. We first reformulate a variational sparse GP algorithm to make it adaptive through a forgetting factor. Next, to make the model inference as simple as possible, we propose updating a single inducing point of the sparse GP model together with the remaining model parameters every time a new sample arrives. As a result, the algorithm presents a fast convergence of the inference process, which allows an efficient model update (with a single inference iteration) even in highly non-stationary environments. Experimental results demonstrate the capabilities of the proposed algorithm and its good performance in modeling the predictive posterior in mean and confidence interval estimation compared to state-of-the-art approaches

    Essays on intergenerational inequalities: Earnings, marital sorting and health

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    This thesis aims to study the intergenerational transmission of earnings and health inequalities in developing and developed countries. The first chapter studies the intergenerational mobility of earnings in Mexico, using ESRU Survey on Social Mobility in Mexico 2011 (ESRU-EMOVI 2011). I utilise the Two-Sample Two-Stage Least Squares approach to estimate the intergenerational elasticity of earnings and the rank-rank coefficient at the national, urban and regional levels, accounting for the attenuation and life-cycle biases suffered by the estimators. The key results show less mobility than previous studies suggested. On average, 70.9%70.9\% of the relative difference in fathers' earnings is transmitted to their children. Moreover, a 10 percentile point increase in the father's earnings rank is associated with a 3.153.15 percentile point increase in the son's earnings rank. At the regional level, strong intergenerational persistence is found in the South, the poorest region of Mexico, whilst the North, the wealthiest region, presents the highest intergenerational earnings mobility. Using Mexican data from ESRU-EMOVI 2011, the second chapter studies intergenerational earnings mobility, focusing on the role of sex, marital status and marital sorting. I examine the implications of using family earnings rather than individual earnings to assess differences in intergenerational earnings mobility for daughters and sons. I find that the intergenerational persistence of earnings is higher for single daughters and married sons than for their counterparts. Additionally, married daughters present higher intergenerational earnings mobility than married sons for individual and family earnings. The sex differences in economic mobility are considerably more significant for combined earnings than individual earnings, suggesting that marital sorting is more critical for daughters. The final chapter studies the intergenerational transmission of health in the UK. Using the 1970 British Cohort Study (BCS70), I find that having a mother with comorbidity of physical and mental health problems during the offspring's early childhood or adolescence significantly increases the chance of offspring having mental health problems in early adulthood and comorbidity of physical and mental health problems during early and mid adulthood. Furthermore, if the mother presents poor mental health during early childhood, it is more likely that the offspring suffers from mental health problems in early adulthood, whilst if these problems arise during the offspring's adolescence, the likelihood of the offspring having poor mental health persists from early to mid adulthood
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