Investigation of Cumulative Retrospective Cost Adaptive Control for Missile Application

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83634/1/AIAA-2010-7577-578.pd

Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence

International audienceWe consider the problem of near-optimal arm identification in the fixed confidence setting of the infinitely armed bandit problem when nothing is known about the arm reservoir distribution. We (1) introduce a PAC-like framework within which to derive and cast results; (2) derive a sample complexity lower bound for near-optimal arm identification; (3) propose an algorithm that identifies a nearly-optimal arm with high probability and derive an upper bound on its sample complexity which is within a log factor of our lower bound; and (4) discuss whether our log^2(1/delta) dependence is inescapable for ``two-phase'' (select arms first, identify the best later) algorithms in the infinite setting. This work permits the application of bandit models to a broader class of problems where fewer assumptions hold

Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence

International audienceWe consider the problem of near-optimal arm identification in the fixed confidence setting of the infinitely armed bandit problem when nothing is known about the arm reservoir distribution. We (1) introduce a PAC-like framework within which to derive and cast results; (2) derive a sample complexity lower bound for near-optimal arm identification; (3) propose an algorithm that identifies a nearly-optimal arm with high probability and derive an upper bound on its sample complexity which is within a log factor of our lower bound; and (4) discuss whether our log^2(1/delta) dependence is inescapable for ``two-phase'' (select arms first, identify the best later) algorithms in the infinite setting. This work permits the application of bandit models to a broader class of problems where fewer assumptions hold

Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence

International audienceWe consider the problem of near-optimal arm identification in the fixed confidence setting of the infinitely armed bandit problem when nothing is known about the arm reservoir distribution. We (1) introduce a PAC-like framework within which to derive and cast results; (2) derive a sample complexity lower bound for near-optimal arm identification; (3) propose an algorithm that identifies a nearly-optimal arm with high probability and derive an upper bound on its sample complexity which is within a log factor of our lower bound; and (4) discuss whether our log^2(1/delta) dependence is inescapable for ``two-phase'' (select arms first, identify the best later) algorithms in the infinite setting. This work permits the application of bandit models to a broader class of problems where fewer assumptions hold

Time after Time: Longitudinal Trends in Nostalgic Listening

Nostalgia was once considered a medical disease but is now understood as a beneficial, identity-affirming emotion. Yet what induces feelings of nostalgia is not fully understood. Are nostalgic feelings prompted by changes in a person’s life, and do certain events prompt nostalgia across whole populations? In this paper, we analyze when people listen to nostalgic music. We use data from a large, cross-national survey to train a classifier to detect which tracks are nostalgic for individual listeners. We then analyze a comprehensive dataset of listening histories from a 5.5 year period. Despite it being a complex concept, we were able to predict nostalgic listening with relatively high precision. We compare our results across listeners from four countries to understand how consistent behavior toward nostalgic music is. We find people listen to nostalgic music more often as they age. We also find people tend to listen to nostalgic music consistently in their day-to-day lives. We do not find evidence that listening to personally nostalgic music increases in response to particular traditions, seasons, or events. However, we do find traditions and events can affect how much “back catalog” music people listen to. These trends are consistent across the national contexts we studied. Our results advance prior findings about nostalgia and the life course, and demonstrate a novel methodological consideration for studies of nostalgic listening