Stochastics and Statistics Seminar

Abstract: Classical topic modeling approaches, such as Latent Dirichlet Allocation (LDA) and probabilistic Latent Semantic Indexing (pLSI), decompose a document-term matrix into a mixture of topics, offering a powerful tool for uncovering latent thematic structures from document corpora or compositional data at large. However, these methods generally assume document independence, overlooking potential relationships or additional structural information that could improve inference—especially in contexts with short documents or large vocabulary sizes. In this talk, we will consider two new structured approaches…

Abstract: The widespread adoption of AI and machine learning models in in society has brought increased attention to how model predictions impact decision processes in a variety of domains. I will describe tools that apply statistical decision theory and information economics to address pressing question at the human-AI interface. These include: how to evaluate when a decision-maker appropriately relies on model predictions, when a human or AI agent could better exploit available contextual information, and how to evaluate (and design)…

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Abstract: In the context of linear regression, we construct a data-driven convex loss function with respect to which empirical risk minimisation yields optimal asymptotic variance in the downstream estimation of the regression coefficients. Our semiparametric approach targets the best decreasing approximation of the derivative of the log-density of the noise distribution. At the population level, this fitting process is a nonparametric extension of score matching, corresponding to a log-concave projection of the noise distribution with respect to the Fisher divergence.…

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