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We study the problem of learning mixtures of $k$ Gaussians in $d$ dimensions.
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Ilias Diakonikolas, Daniel M Kane, and Alistair Stewart · 2017
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Jerry Li and Ludwig Schmidt · 2017
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Oded Regev and Aravindan Vijayaraghavan · 2017
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Hassan Ashtiani, Shai Ben-David, Nicholas Harvey, Christopher Liaw, Abbas Mehrabian, and Yaniv Plan · 2018
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Classical lower bounds from quantum upper bounds
Shalev Ben-David, Adam Bouland, Ankit Garg, and Robin Kothari · 2018
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Ahmed El Alaoui, Andrea Montanari, and Mark Sellke · 2023
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Linear convergence bounds for diffusion models via stochastic localization
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Error bounds for flow matching methods
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Improving image generation with better captions
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Convergence of score-based generative modeling for general data distributions
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Learning mixtures of gaussians using the DDPM objective
Kulin Shah, Sitan Chen, and Adam Klivans · 2023
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Yuling Yan, Kaizheng Wang, and Philippe Rigollet · 2023
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Fast parallel sampling under isoperimetry
Nima Anari, Sinho Chewi, and Thuy-Duong Vuong · 2024
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Khashayar Gatmiry, Jonathan Kelner, and Holden Lee · 2024
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Provable benefits of score matching
Chirag Pabbaraju, Dhruv Rohatgi, Anish Prasad Sevekari, Holden Lee, Ankur Moitra, and Andrej Risteski · 2024
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Optimal score estimation via empirical bayes smoothing
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