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Optimal-Time Adaptive Strong Renaming, with Applications to Counting

Seth Gilbert, Dan Alistarh, Morteza Zadimoghaddam

We give two new randomized algorithms for tight renaming, both of which work against an adaptive adversary. The first uses repeated sampling over a sequence of arrays of decreasing size to assign unique names to each of

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processes with step complexity $ ...

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Efficient Learning from Comparisons

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Chattez avec Graph Search

Optimal-Time Adaptive Strong Renaming, with Applications to Counting

Choice set generation for iterated DTA simulations

Overcoming the limitations of traditional model-updating approaches

Neighbor discovery with reception status feedback to transmitters

Sorting signed permutations by inversions in O(nlogn) time

Ordered Sets in the Calculus of Data Structures

Evaluation of Control and Modulation Methods for Modular Multilevel Converters

A generic calibration framework for joint estimation of car following models using microscopic data

The Metropolized Partial Importance Sampling MCMC mixes slowly on minimal reversal rearrangement paths

Likelihood-Based Inference for Max-Stable Processes

Overcoming the limitations of traditional model-updating approaches

Optimal-Time Adaptive Strong Renaming, with Applications to Counting

Choice set generation for iterated DTA simulations

Neighbor discovery with reception status feedback to transmitters

Sorting signed permutations by inversions in O(nlogn) time

Ordered Sets in the Calculus of Data Structures

The Metropolized Partial Importance Sampling MCMC mixes slowly on minimal reversal rearrangement paths

Likelihood-Based Inference for Max-Stable Processes

Evaluation of Control and Modulation Methods for Modular Multilevel Converters

A generic calibration framework for joint estimation of car following models using microscopic data