A General Characterization of the Statistical Query Complexity. Feldman, V. In Kale, S. & Shamir, O., editors, volume 65, of Proceedings of Machine Learning Research, pages 785–830, Amsterdam, Netherlands, 07–10 Jul, 2017. PMLR. [Feldman17] Provides a characterization of SQ learning in terms of new "statistical dimension;" to be seen in view of the poly-factor equivalence between SQ learning and LDP-constrained/memory-constrained learnin (e.g., [KLNRS11,BDD98]). Includes applications to memory-limited streaming and communication-limited learning.
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Paper bibtex @inproceedings{Feldman17,
title = {A General Characterization of the Statistical Query Complexity},
author = {Vitaly Feldman},
pages = {785--830},
year = {2017},
editor = {Satyen Kale and Ohad Shamir},
volume = {65},
series = {Proceedings of Machine Learning Research},
address = {Amsterdam, Netherlands},
month = {07--10 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v65/feldman17c/feldman17c.pdf},
url = {http://proceedings.mlr.press/v65/feldman17c.html},
bibbase_note = {<div class="well well-small bibbase"><span class="bluecite">[Feldman17]</span> Provides a characterization of SQ learning in terms of new "statistical dimension;" to be seen in view of the poly-factor equivalence between SQ learning and LDP-constrained/memory-constrained learnin (e.g., [KLNRS11,BDD98]). Includes applications to memory-limited streaming and communication-limited learning.</div>}
}
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