Betting mechanisms can yield provably more accurate and efficient estimates of real-world robot behavior than Monte Carlo sampling under specified conditions, with practical approximations demonstrated on synthetic data and a robotic manipulator task.
Universal portfolios.Mathematical finance, 1(1):1–29, 1991
2 Pith papers cite this work. Polarity classification is still indexing.
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COMPASS-Hedge is presented as the first parameter-free full-information anytime algorithm that simultaneously delivers minimax-optimal adversarial regret, instance-optimal stochastic regret, and Õ(1) regret to a baseline policy.
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Betting for Sim-to-Real Performance Evaluation
Betting mechanisms can yield provably more accurate and efficient estimates of real-world robot behavior than Monte Carlo sampling under specified conditions, with practical approximations demonstrated on synthetic data and a robotic manipulator task.
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Learning Safely Without Knowing the World:COMPASS-Hedge
COMPASS-Hedge is presented as the first parameter-free full-information anytime algorithm that simultaneously delivers minimax-optimal adversarial regret, instance-optimal stochastic regret, and Õ(1) regret to a baseline policy.