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When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.CCC.2017.18
URN: urn:nbn:de:0030-drops-75327
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/7532/
Oliveira, Igor C. Carboni ;
Santhanam, Rahul
Conspiracies Between Learning Algorithms, Circuit Lower Bounds, and Pseudorandomness
Abstract
We prove several results giving new and stronger connections between learning theory, circuit complexity and pseudorandomness. Let C be any typical class of Boolean circuits, and C[s(n)] denote n-variable C-circuits of size <= s(n). We show:
Learning Speedups: If C[s(n)] admits a randomized weak learning algorithm under the uniform distribution with membership queries that runs in time 2^n/n^{\omega(1)}, then for every k >= 1 and epsilon > 0 the class C[n^k] can be learned to high accuracy in time O(2^{n^epsilon}). There is epsilon > 0 such that C[2^{n^{epsilon}}] can be learned in time 2^n/n^{omega(1)} if and only if C[poly(n)] can be learned in time 2^{(log(n))^{O(1)}}.
Equivalences between Learning Models: We use learning speedups to obtain equivalences between various randomized learning and compression models, including sub-exponential time learning with membership queries, sub-exponential time learning with membership and equivalence queries, probabilistic function compression and probabilistic average-case function compression.
A Dichotomy between Learnability and Pseudorandomness: In the non-uniform setting, there is non-trivial learning for C[poly(n)] if and only if there are no exponentially secure pseudorandom functions computable in C[poly(n)].
Lower Bounds from Nontrivial Learning: If for each k >= 1, (depth-d)-C[n^k] admits a randomized weak learning algorithm with membership queries under the uniform distribution that runs in time 2^n/n^{\omega(1)}, then for each k >= 1, BPE is not contained in (depth-d)-C[n^k]. If for some epsilon > 0 there are P-natural proofs useful against C[2^{n^{epsilon}}], then ZPEXP is not contained in C[poly(n)].
Karp-Lipton Theorems for Probabilistic Classes: If there is a k > 0 such that BPE is contained in i.o.Circuit[n^k], then BPEXP is contained in i.o.EXP/O(log(n)). If ZPEXP is contained in i.o.Circuit[2^{n/3}], then ZPEXP is contained in i.o.ESUBEXP.
Hardness Results for MCSP: All functions in non-uniform NC^1 reduce to the Minimum Circuit Size Problem via truth-table reductions computable by TC^0 circuits. In particular, if MCSP is in TC^0 then NC^1 = TC^0.
BibTeX - Entry
@InProceedings{oliveira_et_al:LIPIcs:2017:7532,
author = {Igor C. Carboni Oliveira and Rahul Santhanam},
title = {{Conspiracies Between Learning Algorithms, Circuit Lower Bounds, and Pseudorandomness}},
booktitle = {32nd Computational Complexity Conference (CCC 2017)},
pages = {18:1--18:49},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-040-8},
ISSN = {1868-8969},
year = {2017},
volume = {79},
editor = {Ryan O'Donnell},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2017/7532},
URN = {urn:nbn:de:0030-drops-75327},
doi = {10.4230/LIPIcs.CCC.2017.18},
annote = {Keywords: boolean circuits, learning theory, pseudorandomness}
}
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Keywords: |
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boolean circuits, learning theory, pseudorandomness |
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Collection: |
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32nd Computational Complexity Conference (CCC 2017) |
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Issue Date: |
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2017 |
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Date of publication: |
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01.08.2017 |
