Distribution Learning Meets Graph Structure Sampling

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• Arnab Bhattacharya (University of Warwick)
• Tuesday 22 April 2025, 14:00-15:00
• Computer Laboratory, William Gates Building, FW26.

If you have a question about this talk, please contact Tom Gur.

This work establishes a novel link between the problem of PAC -learning high-dimensional graphical models and the task of (efficient) counting and sampling of graph structures, using an online learning framework. We observe that if we apply the exponentially weighted average (EWA) or randomized weighted majority (RWM) forecasters on a sequence of samples from a distribution P using the log loss function, the average regret incurred by the forecaster’s predictions can be used to bound the expected KL divergence between P and the predictions. Known regret bounds for EWA and RWM then yield new sample complexity bounds for learning Bayes nets. Moreover, these algorithms can be made computationally efficient for several interesting classes of Bayes nets. Specifically, we give a new sample-optimal and polynomial time learning algorithm with respect to trees of unknown structure and the first polynomial sample and time algorithm for learning with respect to Bayes nets over a given chordal skeleton.

Joint work with Sutanu Gayen (IIT Kanpur), Philips George John (NUS), Sayantan Sen (NUS), and N.V. Vinodchandran (U Nebraska Lincoln)

This talk is part of the Algorithms and Complexity Seminar series.

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