BibSLEIGH — Rocco_A

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: Servedio:Rocco_A=

Facilitated 1 volumes:

Contributed to:

2015

2014

2013

2012

2010

2009

2008

2007

2005

2003

2002

2001

1999

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.
STOC-2003-KalaiS: 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.
STOC-2002-JacksonKS: 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.