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High-Dimensional Statistics

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arxiv 2310.19244 v1 pith:AL6YDUB5 submitted 2023-10-30 math.ST stat.TH

Philippe Rigollet , Jan-Christian Hütter This is my paper
classification math.STstat.TH
keywords notesstatisticsbuildcoursedimensionalhighhigh-dimensionalhopefully
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These lecture notes were written for the course 18.657, High Dimensional Statistics at MIT. They build on a set of notes that was prepared at Princeton University in 2013-14 that was modified (and hopefully improved) over the years.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. Variance-Reduced Q-Learning over Static and Time-Varying Networks

    cs.LG 2026-07 conditional novelty 7.0 of 10

    VRDQ achieves the optimal collaborative error rate 1/√(NT) for decentralized tabular Q-learning while requiring only O(log²(NT)) communication per agent, on both static and time-varying networks.

  2. Joint Planning and Operations of Wind Power under Decision-dependent Uncertainty

    math.OC 2025-08 unverdicted novelty 6.0 of 10

    A two-stage distributionally robust wind farm planning model with a decision-dependent Wasserstein ambiguity set, reformulated as a mixed-integer second-order cone program with an accelerated constraint generation solver.

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