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An Introduction to Computational Learning Theory
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…
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Efficient noise-tolerant learning from statistical queries
TLDR
This paper formalizes a new but related model of learning from statistical queries, and demonstrates the generality of the statistical query model, showing that practically every class learnable in Valiant’s model and its variants can also be learned in the new model (and thus can be learning in the presence of noise).
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A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
TLDR
This paper presents a new algorithm that, given only a generative model (a natural and common type of simulator) for an arbitrary MDP, performs on-line, near-optimal planning with a per-state running time that has no dependence on the number of states.
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Cryptographic limitations on learning Boolean formulae and finite automata
TLDR
It is proved that a polynomial-time learning algorithm for Boolean formulae, deterministic finite automata or constant-depth threshold circuits would have dramatic consequences for cryptography and number theory and is applied to obtain strong intractability results for approximating a generalization of graph coloring.
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On the complexity of teaching
TLDR
This paper studies the complexity of teaching by considering a variant of the on-line learning model in which a helpful teacher selects the instances, and measures the teaching dimension by a combinatorial measure.
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Toward efficient agnostic learning
TLDR
An investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions is initiated, providing an initial outline of the possibilities for agnostic learning.
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Weakly learning DNF and characterizing statistical query learning using Fourier analysis
TLDR
It is proved that an algorithm due to Kushilevitz and Mansour can be used to weakly learn DNF using membership queries in polynomial time, with respect to the uniform distribution on the inputs, and it is obtained that DNF expressions and decision trees are not evenWeakly learnable with any unproven assumptions.
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Learning in the presence of malicious errors
TLDR
General methods for bounding the rate of error tolerable by any learning algorithm, efficient algorithms tolerating nontrivial rates of malicious errors, and equivalences between problems of learning with errors and standard combinatorial optimization problems are studied.
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Efficient Reinforcement Learning in Factored MDPs
We present a provably efficient and near-optimal algorithm for reinforcement learning in Markov decision processes (MDPs) whose transition model can be factored as a dynamic Bayesian network (DBN).…
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A general lower bound on the number of examples needed for learning
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