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Travelled to:
1 × Denmark
1 × Finland
1 × Germany
1 × Iceland
1 × Israel
1 × Latvia
1 × Poland
1 × United Kingdom
2 × Canada
3 × Greece
8 × USA
Collaborated with:
P.M.Long I.Diakonikolas A.De R.O'Donnell A.Klivans L.Tan R.Rubinfeld A.Kalai H.K.Lee A.Wan C.Daskalakis A.R.Klivans M.O.Rabin C.Thorpe E.Mossel J.C.Jackson X.Chen E.Blais J.Håstad S.Chan X.Sun V.Feldman P.Gopalan A.Shpilka K.Wimmer K.Matulef V.Varadan S.Gilman M.Treshock D.Dachman-Soled T.Malkin H.Wee P.Harsha R.Meka P.Raghavendra
Talks about:
learn (7) function (6) polynomi (5) approxim (5) effici (5) monoton (4) test (4) nois (4) threshold (3) dimension (3)

Person: Rocco A. Servedio

DBLP DBLP: Servedio:Rocco_A=

Facilitated 1 volumes:

STOC 2015Ed

Contributed to:

STOC 20152015
ICALP (1) 20142014
STOC 20142014
ICALP (1) 20132013
ICML c3 20132013
ICALP (1) 20122012
STOC 20122012
ICML 20102010
STOC 20102010
ICALP (1) 20092009
ICALP (1) 20082008
ICML 20082008
STOC 20082008
LICS 20072007
ICML 20052005
STOC 20052005
STOC 20032003
STOC 20022002
ICALP 20012001
STOC 20012001
STOC 19991999

Wrote 27 papers:

STOC-2015-ChenDST #adaptation #query #testing
Boolean Function Monotonicity Testing Requires (Almost) n 1/2 Non-adaptive Queries (XC, AD, RAS, LYT), pp. 519–528.
ICALP-v1-2014-BlaisHST #approximate #on the
On DNF Approximators for Monotone Boolean Functions (EB, JH, RAS, LYT), pp. 235–246.
STOC-2014-ChanDSS #approximate #estimation #performance #polynomial
Efficient density estimation via piecewise polynomial approximation (SoC, ID, RAS, XS), pp. 604–613.
STOC-2014-DeS #approximate #performance #polynomial
Efficient deterministic approximate counting for low-degree polynomial threshold functions (AD, RAS), pp. 832–841.
ICALP-v1-2013-DeDS #algorithm #analysis #difference #fourier #geometry #robust
A Robust Khintchine Inequality, and Algorithms for Computing Optimal Constants in Fourier Analysis and High-Dimensional Geometry (AD, ID, RAS), pp. 376–387.
ICML-c3-2013-LongS #classification #consistency #multi
Consistency versus Realizable H-Consistency for Multiclass Classification (PML, RAS), pp. 801–809.
ICALP-v1-2012-DeDS #problem
The Inverse Shapley Value Problem (AD, ID, RAS), pp. 266–277.
STOC-2012-DaskalakisDS #learning
Learning poisson binomial distributions (CD, ID, RAS), pp. 709–728.
STOC-2012-DeDFS #approximate #parametricity #problem
Nearly optimal solutions for the chow parameters problem and low-weight approximation of halfspaces (AD, ID, VF, RAS), pp. 729–746.
ICML-2010-LongS #approximate #simulation #strict
Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate (PML, RAS), pp. 703–710.
STOC-2010-DiakonikolasHKMRST #bound #polynomial
Bounding the average sensitivity and noise sensitivity of polynomial threshold functions (ID, PH, AK, RM, PR, RAS, LYT), pp. 533–542.
ICALP-v1-2009-GopalanOSSW #fourier #testing
Testing Fourier Dimensionality and Sparsity (PG, RO, RAS, AS, KW), pp. 500–512.
ICALP-v1-2009-KlivansLS #learning
Learning Halfspaces with Malicious Noise (ARK, PML, RAS), pp. 609–621.
ICALP-A-2008-Dachman-SoledLMSWW #encryption #learning
Optimal Cryptographic Hardness of Learning Monotone Functions (DDS, HKL, TM, RAS, AW, HW), pp. 36–47.
ICALP-A-2008-DiakonikolasLMSW #testing
Efficiently Testing Sparse GF(2) Polynomials (ID, HKL, KM, RAS, AW), pp. 502–514.
ICML-2008-LongS #classification #random
Random classification noise defeats all convex potential boosters (PML, RAS), pp. 608–615.
STOC-2008-ODonnellS #parametricity #problem
The chow parameters problem (RO, RAS), pp. 517–526.
LICS-2007-RabinST #correctness #performance #proving
Highly Efficient Secrecy-Preserving Proofs of Correctness of Computations and Applications (MOR, RAS, CT), pp. 63–76.
ICML-2005-LongVGTS #integration
Unsupervised evidence integration (PML, VV, SG, MT, RAS), pp. 521–528.
STOC-2005-RubinfeldS #testing
Testing monotone high-dimensional distributions (RR, RAS), pp. 147–156.
Boosting in the presence of noise (AK, RAS), pp. 195–205.
STOC-2003-MosselOS #learning
Learning juntas (EM, RO, RAS), pp. 206–212.
STOC-2003-ODonnellS #bound #polynomial
New degree bounds for polynomial threshold functions (RO, RAS), pp. 325–334.
Learnability beyond AC0 (JCJ, AK, RAS), pp. 776–784.
ICALP-2001-Servedio #learning #quantum
Separating Quantum and Classical Learning (RAS), pp. 1065–1080.
STOC-2001-KlivansS01a #learning
Learning DNF in time 2Õ(n1/3) (AK, RAS), pp. 258–265.
STOC-1999-Servedio #complexity #learning
Computational Sample Complexity and Attribute-Efficient Learning (RAS), pp. 701–710.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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