World's Best Scientists 2026 revealed!

Overview

Leonard Pitt is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their academic profile does not currently list recent papers, frequent co-authors, publication venues, book publications, specific fields or subfields of study, or main topics of research.

There is no available information regarding awards won, which might suggest that data on recognitions has not been provided or documented at this time.

The absence of listed publications and research topics limits the detailed analysis of Leonard Pitt's specific contributions or areas of expertise within their broader academic context.

Further data would be necessary to offer a more comprehensive overview of their academic influence, research themes, and possible collaborations across institutions or disciplines.

Best Publications

  • Computational limitations on learning from examples

    Leonard Pitt;Leslie G. Valiant

  • On the learnability of Boolean formulae

    M. Kearns;M. Li;L. Pitt;L. Valiant

  • Inductive Inference, DFAs, and Computational Complexity

    Leonard Pitt

  • Learning Conjunctions of Horn Clauses

    Dana Angluin;Michael Frazier;Leonard Pitt

  • The minimum consistent DFA problem cannot be approximated within any polynomial

    Leonard Pitt;Manfred K. Warmuth

  • Prediction-preserving reducibility

    Leonard Pitt;Manfred K. Warmuth

  • On the necessity of Occam algorithms

    Raymond Board;Leonard Pitt

  • Recent Results on Boolean Concept Learning

    Michael Kearns;Ming Li;Leonard Pitt;Leslie G. Valiant

  • Sublinear time approximate clustering

    Nina Mishra;Dan Oblinger;Leonard Pitt

  • The minimum consistent DFA problem cannot be approximated within and polynomial

    L. Pitt;M. K. Warmuth

  • Probabilistic inductive inference

    L. Pitt

  • Probability and plurality for aggregations of learning machines

    Leonard Pitt;Carl H. Smith

  • A polynomial-time algorithm for learning k-variable pattern languages from examples

    Michael Kearns;Leonard Pitt

  • Reductions among prediction problems: on the difficulty of predicting automata

    L. Pitt;M.K. Warmuth

  • Learning from entailment: an application to propositional horn sentences

    Michael Frazier;Leonard Pitt

  • Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries

    Carlos Domingo;Nina Mishra;Leonard Pitt

  • A Characterization Of Probabilistic Inference

    L. Pitt

  • Exact learning of read-twice DNF formulas

    H. Aizenstein;L. Pitt

  • CLASSIC learning

    Michael Frazier;Leonard Pitt

  • The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial (abstract).

    Leonard Pitt;Manfred K. Warmuth

Frequent Co-Authors

Dana Angluin
Dana Angluin Yale University
Manfred K. Warmuth
Manfred K. Warmuth Google (United States)
Leslie G. Valiant
Leslie G. Valiant Harvard University
Michael Kearns
Michael Kearns University of Pennsylvania
Avrim Blum
Avrim Blum Toyota Technological Institute at Chicago
Dan Roth
Dan Roth University of Pennsylvania
Howard J. Aizenstein
Howard J. Aizenstein University of Pittsburgh
Eyal Kushilevitz
Eyal Kushilevitz Technion – Israel Institute of Technology
Dan Gusfield
Dan Gusfield University of California, Davis
Haym Hirsh
Haym Hirsh Cornell University

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