We study the classical problem of prediction with expert advice in the adversarial setting with a geometric stopping time. In 1965, Cover gave the optimal algorithm for the case of 2 experts. In this paper, we design the optimal algorithm, adversary and regret for the case of 3 experts. Further, we show that the optimal algorithm for 2 and 3 experts is a probability matching algorithm (analogous to Thompson sampling) against a particular randomized adversary.
Advances in sensor miniaturization, low-power computing, and battery life have enabled the first generation of mainstream wearable cameras. Millions of hours of videos have been captured by these devices, creating a record of our daily visual experiences at an unprecedented scale. This has created a major opportunity to develop new capabilities and products based on First Person Vision (FPV)--the automatic analysis of videos captured from wearable cameras. Meanwhile, vision technology is at a tipping point.
Today, computer systems need to cope with the explosive growth of data in the world. For instance, in financial technology companies, distributed processing systems are deployed to support graph analytics, and in data-center networks, monitoring systems are used to measure traffic statistics at high speed. Ideally, we expect the systems to meet service-level objectives (SLOs) using the least amount of resource.