Concentration of non-Lipschitz functions and applications
Random Structures & Algorithms - Probabilistic methods in combinatorial optimization
Random Structures & Algorithms - Probabilistic methods in combinatorial optimization
A Large Deviation Result on the Number of Small Subgraphs of a Random Graph
Combinatorics, Probability and Computing
Divide and conquer martingales and the number of triangles in a random graph
Random Structures & Algorithms
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Using Talagrand's concentration inequality on the discrete cube {0, 1}m we show that given a real-valued function Z(x) on {0, 1}m that satisfies certain monotonicity conditions one can control the deviations of Z(x) above its median by a local Lipschitz norm of Z at the point x. As one application, we obtain a deviation inequality for the number of k-cycles in a random graph.