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Statistics and Data Science Seminar Andrej Risteski (Princeton University)

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Statistics and Data Science Seminar Yury Polyanskiy (MIT)

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Statistics and Data Science Seminar Amir Dembo (Stanford University)

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Statistics and Data Science Seminar Jelani Nelson (Harvard University)

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New provable techniques for learning and inference in probabilistic graphical models

Andrej Risteski (Princeton University)

Abstract: A common theme in machine learning is succinct modeling of distributions over large domains. Probabilistic graphical models are one of the most expressive frameworks for doing this. The two major tasks involving graphical models are learning and inference. Learning is the task of calculating the "best fit" model parameters from raw data, while inference…

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Sample complexity of population recovery

Yury Polyanskiy (MIT)

Abstract: In this talk we will first consider a general question of estimating linear functional of the distribution based on the noisy samples from it. We discover that the (two-point) LeCam lower bound is in fact achievable by optimizing bias-variance tradeoff of an empirical-mean type of estimator. Next, we apply this general framework to the specific…

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Walking within growing domains: recurrence versus transience

Amir Dembo (Stanford University)
4-163

Abstract: When is simple random walk on growing in time d-dimensional domains recurrent? For domain growth which is independent of the walk, we review recent progress and related universality conjectures about a sharp recurrence versus transience criterion in terms of the growth rate. We compare this with the question of recurrence/transience for time varying conductance…

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Optimal lower bounds for universal relation, and for samplers and finding duplicates in streams

Jelani Nelson (Harvard University)
E18-304

Abstract: Consider the following problem: we monitor a sequence of edgeinsertions and deletions in a graph on n vertices, so there are N = (n choose 2) possible edges (e.g. monitoring a stream of friend accepts/removals on Facebook). At any point someone may say "query()", at which point must output a random edge that exists in the graph…

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