The elements of integration and Lebesgue measure

By R. Bartle. 1995

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Citations in the corpus

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (2016, background): “See the work of (Bartle, 2014, Theorem 5.6).”
On the equivalence between graph isomorphism testing and function approximation with GNNs (2019, methodology): “For more background on sigma-algebras and measurability, we refer the interested readers to standard texts on measure theory, such as [3].”
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) (2005, unknown): “Bartlett et al. [2003] have characterized the loss functions that lead to universal consistency.”
Minimum-Landing-Error Powered-Descent Guidance for Mars Landing Using Convex Optimization (2010, background): “on 0; tf means that the condition is valid on a set of points in the interval, with an exception of a set of points with measure zero [25]:”
The Mirrlees Approach to Mechanism Design with Renegotiation (with Applications to Hold‐up and Risk Sharing) (2002, background): “Moore (1990), Demski and Sappington (1991), Hermalin and Katz (1991), Edlin and Reichelstein (1996), Segal (1999), Che and Hausch (1999), and Edlin and Hermalin (2000).”
Esaim: Control, Optimisation and Calculus of Variations Spatially-distributed Coverage Optimization and Control with Limited-range Interactions (unknown): “almost everywhere and because this last function is measurable and its integral over the bounded set Ω(x0) is finite by hypothesis, the Lebesgue Dominated Convergence Theorem [3] implies that”
A Powered Descent Guidance Algorithm for Mars Pinpoint Landing (2005, background): “) on [0, tf ] means that the condition is valid on a set of points in the interval with an exception of a set of points with zero measure [1].”