arXiv Machine Learning By Saumya Goyal, Barnab\'as P\'oczos

Sparse Priors for Efficient Distribution Learning

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arXiv:2609. 20883v1 Announce Type: new Abstract: Despite the widespread use and success of generative AI techniques today, theoretical guarantees on learning a distribution supported in $d$ dimensions from $n$ samples degrade as $O(n^{-1/\Theta(d)})$, though shown to be minimax optimal.

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arXiv Machine Learning
Jul 28

Learning Distributions from Multiple Data Providers

arXiv:2607. 24732v1 Announce Type: cross Abstract: Motivated by learning from heterogeneous and overlapping data providers, we study a stylized model of distribution learning from restricted conditional samples.

By Jon Kleinberg, Amin Saberi, Xizhi Tan, Grigoris Velegkas