TY - GEN
T1 - Testing probability distributions underlying aggregated data
AU - Canonne, Clément
AU - Rubinfeld, Ronitt
PY - 2014
Y1 - 2014
N2 - In this paper, we analyze and study a hybrid model for testing and learning probability distributions. Here, in addition to samples, the testing algorithm is provided with one of two different types of oracles to the unknown distribution D over [n]. More precisely, we consider both the dual and cumulative dual access models, in which the algorithm A can both sample from D and respectively, for any i ∈ [n], - query the probability mass D(i) (query access); or - get the total mass of {1,...,i}, i.e. Σj=1 i D(j) (cumulative access) In these two models, we bypass the strong lower bounds established in both of the previously studied sampling and query oracle settings for a number of problems, giving constant-query algorithms for most of them. Finally, we show that while the testing algorithms can be in most cases strictly more efficient, some tasks remain hard even with this additional power.
AB - In this paper, we analyze and study a hybrid model for testing and learning probability distributions. Here, in addition to samples, the testing algorithm is provided with one of two different types of oracles to the unknown distribution D over [n]. More precisely, we consider both the dual and cumulative dual access models, in which the algorithm A can both sample from D and respectively, for any i ∈ [n], - query the probability mass D(i) (query access); or - get the total mass of {1,...,i}, i.e. Σj=1 i D(j) (cumulative access) In these two models, we bypass the strong lower bounds established in both of the previously studied sampling and query oracle settings for a number of problems, giving constant-query algorithms for most of them. Finally, we show that while the testing algorithms can be in most cases strictly more efficient, some tasks remain hard even with this additional power.
UR - http://www.scopus.com/inward/record.url?scp=84904164512&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-43948-7_24
DO - 10.1007/978-3-662-43948-7_24
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AN - SCOPUS:84904164512
SN - 9783662439470
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 283
EP - 295
BT - Automata, Languages, and Programming - 41st International Colloquium, ICALP 2014, Proceedings
PB - Springer Verlag
T2 - 41st International Colloquium on Automata, Languages, and Programming, ICALP 2014
Y2 - 8 July 2014 through 11 July 2014
ER -