High-dimensional longitudinal data is increasingly used in a wide range of scientific studies. To properly account for dependence between longitudinal observations, statistical methods for high-dimensional linear mixe...
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Sparse linear regression methods for high-dimensional data commonly assume that residuals have constant variance, which can be violated in practice. For example, Aphasia Quotient (AQ) is a critical measure of language...
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The principle of maximum entropy is a well-established technique for choosing a distribution that matches available information while minimizing bias. It finds broad use across scientific disciplines and in machine le...
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Simultaneously estimating the kinematic state and extent of extended targets is a nonlinear and high-dimensional problem. While the extended Kalman filter (EKF) is widely employed to achieve this goal, it may not be s...
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Understanding causal relationships in multivariate time series is essential for predicting and controlling dynamic systems in fields like economics, neuroscience, and climate science. However, existing causal discover...
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In this work, we investigate the margin-maximization bias exhibited by gradient-based algorithms in classifying linearly separable data. We present an in-depth analysis of the specific properties of the velocity field...
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Bayesian optimal design is a well-established approach to planning experiments. A distribution for the responses, i.e. a statistical model, is assumed which is dependent on unknown parameters. A utility function is th...
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Prophet inequalities are a central object of study in optimal stopping theory. A gambler is sent values in an online fashion, sampled from an instance of independent distributions, in an adversarial, random or selecte...
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State estimation of dynamical systems from noisy observations is a fundamental task in many applications. It is commonly addressed using the linear Kalman filter (KF), whose performance can significantly degrade in th...
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maximization of mutual information between the model’s input and output is formally related to "decisiveness" and "fairness" of the softmax predictions [1], motivating these unsupervised entropy-b...
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