The performance of anytime algorithms can be improved by simultaneously solving several instances of algorithm-problem pairs. These pairs may include different instances of a problem (such as starting from a different initial state), different algorithms (if several alternatives exist), or several runs of the same algorithm (for non-deterministic algorithms). In this paper we present a methodology for designing an optimal scheduling policy based on the statistical characteristics of the algorithms involved. We formally analyze the case where the processes share resources (a single-processor model), and provide an algorithm for optimal scheduling. We analyze, theoretically and empirically, the behavior of our scheduling algorithm for various distribution types.
Efficient and robust algorithms for decentralized estimation in networks are essential to many distributed systems. Whereas distributed estimation of sample mean statistics has been the subject of a good deal of attention, computation of U-statistics, relying on more expensive averaging over pairs of observations, is a less investigated area. Yet, such data functionals are essential to describe global properties of a statistical population, with important examples including Area Under the Curve, empirical variance, Gini mean difference and within-cluster point scatter. This paper proposes new synchronous and asynchronous randomized gossip algorithms which simultaneously propagate data across the network and maintain local estimates of the U-statistic of interest. We establish convergence rate bounds of O(1 / t) and O(log t / t) for the synchronous and asynchronous cases respectively, where t is the number of iterations, with explicit data and network dependent terms.
We live in a world run by algorithms, computer programs that make decisions or solve problems for us. In this riveting, funny talk, Kevin Slavin shows how modern algorithms determine stock prices, espionage tactics, even the movies you watch. But, he asks: If we depend on complex algorithms to manage our daily decisions -- when do we start to lose control?
I am wondering if there is any research out their about an kNN classifier with a optimized algorithm where a function is trained upon the training data set that maps a point to a value of k. Then, when the algorithm needs to classify a new point, it first looks for the nearest point in this trained function to find what value k it should use. Any thoughts or links to research like this?
This is the second'I, Lawyer' podcast Artificial Lawyer/TromansConsulting has done with Sweden's leading legal tech writer, Fredrik Svärd, who runs the super-informative, Legaltech.se In this approximately 30 minutes chat we knock around a few subjects, such as where legal AI as an industry has got to; how the use of algorithms does not always mean there is any AI involved; why AI may be the answer to removing bias rather than the cause of it, and much, much more. We also give a special shout out to Lexpo, which is now just around the corner and will take place in Amsterdam 8 9 May 2017. Many thanks to Fred for organising and producing the podcast, which is below on Soundcloud.