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Found 5 papers, 2 papers with code
Bayesian Design Principles for Frequentist Sequential Learning
We develop a general theory to optimize the frequentist regret for sequential learning problems, where efficient bandit and reinforcement learning algorithms can be derived from unified Bayesian principles.
Towards Problem-dependent Optimal Learning Rates
We study problem-dependent rates, i. e., generalization errors that scale tightly with the variance or the effective loss at the "best hypothesis."
Towards Optimal Problem Dependent Generalization Error Bounds in Statistical Learning Theory
We introduce a principled framework dubbed "uniform localized convergence," and characterize sharp problem-dependent rates for central statistical learning problems.
Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits
The principle of optimism in the face of uncertainty is one of the most widely used and successful ideas in multi-armed bandits and reinforcement learning.
Acceleration of Primal-Dual Methods by Preconditioning and Simple Subproblem Procedures
They reduce a difficult problem to simple subproblems, so they are easy to implement and have many applications.
Optimization and Control
