• Publications
  • Influence
A bridging model for parallel computation
TLDR
The bulk-synchronous parallel (BSP) model is introduced as a candidate for this role, and results quantifying its efficiency both in implementing high-level language features and algorithms, as well as in being implemented in hardware.
A theory of the learnable
  • L. Valiant
  • Computer Science
    STOC '84
  • 5 November 1984
TLDR
This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.
A theory of the learnable
TLDR
This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.
The Complexity of Computing the Permanent
  • L. Valiant
  • Mathematics, Computer Science
    Theor. Comput. Sci.
  • 1979
The Complexity of Enumeration and Reliability Problems
  • L. Valiant
  • Mathematics, Computer Science
    SIAM J. Comput.
  • 1 August 1979
TLDR
For a large number of natural counting problems for which there was no previous indication of intractability, that they belong to the class of computationally eqivalent counting problems that are at least as difficult as the NP-complete problems.
Completeness classes in algebra
  • L. Valiant
  • Mathematics, Philosophy
    STOC
  • 30 April 1979
TLDR
The aim of this paper is to demonstrate that for both algebraic and combinatorial problems this phenomenon exists in a form that is purely algebraic in both of the respects (A) and (B).
NP is as easy as detecting unique solutions
TLDR
It is shown that the problems of distinguishing between instances of SAT having zero or one solution, or finding solutions to instances of SOTA having unique solutions, are as hard as SAT itself.
Universal schemes for parallel communication
TLDR
This paper shows that there exists an N-processor computer that can simulate arbitrary N- processor parallel computations with only a factor of O(log N) loss of runtime efficiency, and isolates a combinatorial problem that lies at the heart of this question.
Computational limitations on learning from examples
TLDR
It is shown for various classes of concept representations that these cannot be learned feasibly in a distribution-free sense unless R = NP, and relationships between learning of heuristics and finding approximate solutions to NP-hard optimization problems are given.
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