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Stochastics and Statistics Seminar

Winners with Confidence: Discrete Argmin Inference with an Application to Model Selection

Jing Lei, Carnegie Mellon University
E18-304

Abstract:  We study the problem of finding the index of the minimum value of a vector from noisy observations. This problem is relevant in population/policy comparison, discrete maximum likelihood, and model selection. By integrating concepts and tools from cross-validation and differential privacy, we develop a test statistic that is asymptotically normal even in high-dimensional settings, and allows for arbitrarily many ties in the population mean vector. The key technical ingredient is a central limit theorem for globally dependent data characterized…

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Other Events

Deep Learning Methods for Public Health Prediction

Alexander Rodríguez, University of Michigan
E18-304

Abstract: Epidemic prediction is an essential tool for public health decision-making and strategic planning. Despite its importance, our ability to model the spread of epidemics remains limited, largely due to the complexity of social and pathogen dynamics. With the increasing availability of real-time multimodal data and advances in deep learning, a new opportunity has emerged to capture and exploit previously unobservable facets of the spatiotemporal dynamics of epidemics. Toward realizing the potential of AI in public health, my work addresses multiple challenges in this domain,…

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