Open AccessBook
An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
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TLDR
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata is described.Abstract:
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata by experimentation appendix - some tools for probabilistic analysis.read more
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Journal Article
Breaking the Minsky-Papert Barrier for Constant-Depth Circuits.
TL;DR: The threshold degree of a Boolean function is defined in this article as the minimum degree of the real polynomial of the function that represents the Boolean function in the sign of the sign.
Proceedings Article
Learning cooperative games
TL;DR: A novel connection between PAC learnability and core stability is established: for games that are efficiently learnable, it is possible to find payoff divisions that are likely to be stable using a polynomial number of samples.
Journal Article
Constrained Counting and Sampling: Bridging the Gap Between Theory and Practice
TL;DR: A novel hashing-based algorithmic framework for constrained sampling and counting that combines the classical algorithmic technique of universal hashing with the dramatic progress made in combinatorial reasoning tools, in particular, SAT and SMT, over the past two decades is introduced.
Proceedings Article
Kernel methods for learning languages
TL;DR: In this article, the authors study the linear separability of automata and languages by examining the rich class of piecewise-testable languages and prove that all languages linearly separable under a regular finite cover embedding are regular.
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Repairing Decision-Making Programs Under Uncertainty
TL;DR: In this paper, the authors propose distribution-guided inductive synthesis, a repair technique that iteratively samples a finite set of inputs from a probability distribution defining the precondition, synthesizes a minimal repair to the program over the sampled inputs using an smt-based encoding, and verifies that the resulting program is correct and is semantically close to the original program.