Computational Learning Theory
·
L.
Bisht, N. H. Bshouty, L. Khoury.
Learning with Error in Answers to Membership Queries.
·
N.
H. Bshouty, Polynomial time Prediction Strategy with almost
Optimal Mistake Probability.
·
N.
H. Bshouty and L. Burroughs. Maximizing
Agreements with One-Sided Error with Applications to Heuristical Learning.
Accepted to Journal of Machine Learning.
·
N.
H. Bshouty, A new
booster for the PAExact-learning model.
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N.
H. Bshouty, L. Borrough. Maximizing
Agreements and CoAgnostic Learning. The 13th International Conference on
Algorithmic Learning Theory, pp. 83-97, ALT 2002. Invited to Special Issue of Theoretical
Computer Science.
·
N.
H. Bshouty, E. Mossel, R. O'Donnel and R. A. Servedio. Learning DNF from
random walks. In Proceedings of the 44th Annual Symposium on Foundations of
Computer Science, (FOCS) 2003.
·
N.
H. Bshouty. The
monotone theory for the PAC-model. Information
and Computation. 186(1): 20-35 (2003).
·
N.
H. Bshouty, N. Eiron, E. Kushilevitz, PAC learning with
nasty noise. Special issue: Theoretical Computer Science, 288(2), 255-275
(2002). Lecture Notes in Artificial Intelligence Vol. 1720, pp. 206 - 218. ALT
99.
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N.
H. Bshouty, D. Gavinsky, PAC=PAE
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N.
H. Bshouty, N. Eiron. Learning
Monotone DNF from a Teacher that Almost does not Answer Membership Queries.
·
N.
H. Bshouty, V. Feldman. On
using Extended Statistical Queries to avoid Membership queries.
·
N.
H. Bshouty, J. C. Jackson and C. Tamon. Exploring
Learnability Between Exact and PAC, Proceedings of the 15th Annual Workshop
on Computational Learning Theory. (COLT 2002): 244-254.
·
N.
H. Bshouty and L. Burroughs (2002), On
the Proper Learning of Axis Parallel Concepts,
·
·
N.
H. Bshouty, Y. Mansour. Simple
learning algorithms for decision trees and multivariate polynomials.
·
N.
H. Bshouty, D. Gavinsky. On
Boosting with Optimal Poly-Bounded Distribution. Special Issue for COLT 01:
·
N.
H. Bshouty, A. Owshanko. Learning Regular Sets with an Incomplete Membership
Queries. 14th Annual Conference on Computational Learning Theory, COLT 2001 and
5th Eyropean Conference on Computational Learning Theory, EuroCOLT 2001. pp.
574-588, (July 2001).
·
A.
Beimel, F. Bergadano, N. H. Bshouty, E. Kushilevitz, S. Varricchio. Learning
functions represented as multiplicity automata. JACM 47(3): pp.
506-530. (2000). In Proceedings of the 37th Annual Symposium on Foundations of
Computer Science (FOCS 96), pp. 349-358.
·
N.
H. Bshouty, J. Jackson, and T. Tamon. More
Efficient PAC-learning of DNF with Membership Queries Under the Uniform
Distribution, Proceedings of the 12th Annual Workshop on Computational
Learning Theory, 1999. Journal of Computer and System Sciences.
·
N.
H. Bshouty and J. C. Jackson. Learning
DNF over the Uniform Distribution using a Quantum Example Oracle,
·
E.
Abboud, N. Agha, N. H. Bshouty, N. Radwan, F. Saleh. Learning
Threshold functions with small weights using membership queries, COLT 99,
pp. 318-322, (1999).
·
N.
H. Bshouty, P. W. Goldberg, S. A. Goldman, D. H. Mathias, E
·
N.
H. Bshouty, J. Jackson and T. Tamon. Uniform-Distribution
Attribute Noise Learnability, Proceedings of the 12th Annual
Workshop on Computational Learning Theory, 1999. COLT 99, pp. 75-80, (1999).
·
N.
H. Bshouty, D. K. Wilson. On learning in the presence of unspecified attribute
value, COLT 99, pp. 81-87. (1999).
·
Nader
H. Bshouty,
·
N.
H. Bshouty, D. Wilson, T. Tamon. On
the learnability of width 2 branching program. Information Processing Letters, 65, pp. 217-222, (1998). Proceeding of the
Ninth annual ACM workshop on Computational Learning Theory, COLT 96, pp.
224-227.
·
N.
H. Bshouty, C. Tamon, D.
·
N.
H. Bshouty, L. Hellerstein. Attribute-efficient
learning in query and mistake-bound models. Journal of Computing and
System Science, 56, 310-319, 1998. Proceeding of the Ninth annual ACM
workshop on Computational Learning Theory, COLT 96, pp. 235-243.
·
N.
H. Bshouty, D. Bshouty, On
interpolating arithmetic read-once formula with exponentiation. Journal
of Computer and System Science, pp. 112-124 (1998). Proceeding of the
Seventh annual ACM workshop on Computational Learning Theory, COLT 94, pp.
311-317.
·
N.
H. Bshouty and R. Cleve. Parallel
interpolation of arithmetic read-once formula.
·
N.
H. Bshouty, S. A. Goldman, H. D. Mathias, S. Suri and H. Tamaki. Noise-Tolerant
Distribution-Free Learning of Geometric Concepts. JACM. 45(5), pp.
863-890 (1998). Proceedings of the 28th annual ACM Symposium on
Theory of Computing (STOC 96), pp. 151-160.
·
N.
H. Bshouty, T. Tamon., D.
·
N.
H. Bshouty, S. A. Goldman, H. D. Mathias, Noise-tolerant parallel learning of
geometric concepts. Information and
Computation, 146, pp. 89-110 (1998). Proceeding of the Eighth annual ACM
workshop on Computational Learning Theory, COLT 95, pp. 345-352.
·
N.
H. Bshouty, A
new composition theorem for learning algorithms Proceedings of the 30th
annual ACM Symposium on Theory of Computing, (STOC), 1998, pp. 583-589 (May
1998).
·
N.
H. Bshouty. Exact
learning of formulas in parallel. Machine Learning, 26, pp. 25-41
(1997). Proceedings of the 33rd Annual Symposium on Foundations of Computer
Science (FOCS 92), pp. 513-522.
·
S.
Ben-David, N. H. Bshouty, E. Kushilevitz. A Composition Theorem for Learning
Algorithms with Applications to Geometric Concept Classes Proceedings of the 29th
annual ACM Symposium on Theory of Computing (STOC), pp. 324-333, (May 1997).
·
F.
Bergadano, N. H. Bshouty, C. Tamon, S. Varricchio. On Learning Branching
Programs and Small Depth Circuits Euro-COLT 97. (March 1997).
·
N.
H. Bshouty, E. Kushilevitz. On Learning
Exclusive-Or of Terms. (1996).
·
N.
H. Bshouty. A
note on learning multivariate polynomials under the uniform distribution. Information
Processing Letters. 61 (6): pp. 303-309 (1997). Proceeding of the
Eighthannual ACM workshop on Computational Learning Theory,COLT 95, pp. 79-82.
·
N.
H. Bshouty, Simple
learning algorithms via divide and conquer. J. of Computational Comple
·
N.
H. Bshouty, S. A. Goldman, T. R. Hancock, S. Matar, Asking
questions to minimize errors. Journal of Computer and System Sciences.
52, pp. 268-286, (1996). Proceeding of the Si
·
N.
H. Bshouty, R. Cleve, R. Gavaldà, S. Kannan, C. Tamon, Oracles
and Queries That Are Sufficient for Exact Learning. Journal of Computer
and System Sciences, 52(3): pp. 421-433 (1996). Ps
file. Proceeding of the Seventh annual ACM workshop on Computational
Learning Theory, COLT 94, pp. 130-139.
·
N.
H. Bshouty and C. Tamon, On
the Fourier Spectrum of Monotone Functions. Journal of the Association for Computing Machinery.
(JACM), 43(4):747-770, 1996. Proceedings of the 27th
annual ACM Symposium on Theory of Computing (STOC 95), pp. 219-228, (May).
·
N.
H. Bshouty. A
Subexponential Exact Learning Algorithm for DNF Using Equivalence Queries. Information
Processing Letters, 59, pp. 37-40 (1996).
·
N.
H. Bshouty. Toward the learnability of DNF formulae. Proceedings of the 28th
annual ACM Symposium on Theory of Computing (STOC), pp. 131-140. (May 1996).
·
N.
H. Bshouty, Exact
learning of boolean functions via the monotone theory. Information and
Computation, 123, pp. 146-153, (1995).
·
N.
H. Bshouty, Z. Chen, S. E. Decatur, S. Homer, On the learnability of ZN-DNF
formulas, Proceeding of the Eighth annual ACM workshop on Computational
Learning Theory, COLT 95, pp. 198-205, (July 1995).
·
N.
H. Bshouty, T. Hancock, L. Hellerstein, Learning
boolean read-once formulas with arbitrary symmetric and constant fan-in gates.
Journal of Computer and System Science,
50, pp. 521-542, (1995). Proceeding of the fifth annual ACM workshop on
Computational Learning Theory, COLT 92, pp. 1-15.
·
N.
H. Bshouty. Exact
Learning Boolean Functions via the Monotone Theory, IEEE Symposium on
Foundations of Computer Science (FOCS 1993). Information and Computation,
123(1): 146-153,
·
N.
H. Bshouty, T. R. Hancock, L. Hellerstein. Learning
arithmetic read-once formulas.
·
N.
H. Bshouty, Z. Chen, S. Homer. On learning discretized geometric concepts.
Proceedings of the 35th Annual Symposium on Foundations of Computer Science
(FOCS 94), pp. 54-63, (November 1994).
·
N.
H. Bshouty, T. R. Hancock, L. Hellerstein, M. Karpinski. Read-once
threshold formulas, justifying assignments, and generic transformations. Computational
Comple
·
Algebraic and Computational Complexity
·
N.
H. Bshouty, On
the direct sum conjecture for the straight line model. Journal of Comple
·
N.
H. Bshouty. On
the additive complexity of 2x2 matrix multiplication. Information
processing letters, 56(6), pp. 329-336, (1995). Symposium on Applied
Computing (SAC 91), pp. 458-464.
·
N.
H. Bshouty. Multiplicative
complexity of direct sum of quadratic system. Linear Algebra and
Its Applications, 215, pp. 183-223 (1995).
·
N.
H. Bshouty, R. Cleve, W. Eberly, Size-depth
tradeoff for algebraic formulas.
·
N.
H. Bshouty, On the comple
·
A.
Averbuch, N. H. Bshouty, M. Kaminski, A
classification of bilinear algorithms for multiplying polynomials of small
degree over finite fields, Journal
of Algorithms, 13, pp. 577-588, (1992). Eleventh International Conference
of the Chilean Computer Science Society (SCCC), Computer Science Research and
Application, Edited by Ricardo Baeza-Yates and Udi Manber, Plenum Press, 91,
pp. 153-157.
·
N.
H. Bshouty, A
lower bound for the multiplication of polynomials modulo a polynomial,
Information processing letters, 41, pp. 321-326, (1992).
·
N.
H. Bshouty. Maximal Rank of mxnx (mn - k) Tensors, SIAM Journal on Computing, 19, (1990).
·
N.
H. Bshouty, M. Kaminski, Multiplication of polynomials over finite fields,
·
N.
H. Bshouty, On the e
·
N.
H. Bshouty, A
Lower Bound for Matrix Multiplication, In 29th Annual Symposium on
Foundations of Computer Science, pages 64-67,
·
M.
Kaminski, N. H. Bshouty, Multiplicative
complexity of polynomial multiplication over finite fields, Journal of ACM, 36, pp. 150-170, (1989). Proceedings of the
28th Annual Symposium on Foundations of Computer Science (FOCS 87), pp.
138-140.
·
M.
Kaminski, D. G. Kirkpatrick, N. H. Bshouty, Addition
requirements for matrix and transposed matrix products, Journal of
algorithms, 9, pp. 354-364, (1988).
·
M.
Kaminski and N. H. Bshouty. Multiplicative
complexity of polynomial multiplication over finite fields (extended
abstract). In 28th Annual Symposium on Foundations of Computer Science,
pages 138-140, Los Angeles, California, 12-14 October 1987. Journal of the ACM, 36(1):150-170, January 1989.
RAM Complexity
·
N.
H. Bshouty, Lower bounds for the comple
·
N.
H. Bshouty, Y. Mansour, B. Schieber, P. Tiwari. A
tight bound for approximating the square root. Information Processing
Letters, 63(4): pp. 211-213 (1997).
·
N.
H. Bshouty, On the complexity of functions for random access machine. Journal
of ACM, 40, pp. 211-223, (1993).
·
N.H.
Bshouty, Y. Mansour, B. Schieber and P. Tiwari, Fast exponentiation using the
truncation operation. Computational Complexity, 2 pp. 244--255
(1992).
·
N.
H. Bshouty, Lower bounds for algebraic computation trees, Advances in Computing
and Information-ICCI'91, Lecture Notes in Computer Science, Springer-Verlage
497, pp. 55-65, (May 1991).
Other Papers
·
N.H.
Bshouty, L. Higham and J. Warpechowska-Gruca. Meeting
times of random walks on graphs. Information Processing Letters,
69(5), pp. 259-265 (1999).
·
N.
H. Bshouty and L. Burroughs (1998) ``Massaging a Linear Programming Solution to
Give a 2-Approximation for a Generalization of the Vertex Cover Problem'', The
Proceedings of the Fifteenth Annual Symposium on the Theoretical Aspects of
Computer Science , pp 298-308.
·
N.
H. Bshouty, L. J. Burrough, Massaging a linear programming solution to give a
2-approximation for a generalization of the vertex cover problem, STAC, (1998).
·
D.
Bshouty, N. H. Bshouty, A note on prime
n-tuples. Rocky Mountain J. of Mathematics. 27, 3, (1997).
·
N. H.
Bshouty, Geoffrey Falk, Compression of dictionaries via extended front coding
and Huffman coding. Proceeding Forth International Conference Computing and
information, ICCI' 92, pp. 361-364, (May 1992).
·
N.
H. Bshouty, G. Seroussi, Generalization of the normal basis theorem.
·
Seroussi,
N. H. Bshouty, Vector
sets for exhaustive testing of logic circuits, IEEE transaction on
information theory, 34 , pp.
513-522, (1988). 23rd Allerton Conference on communication, control, and computing.