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Found 6 papers, 1 papers with code
Unified Projection-Free Algorithms for Adversarial DR-Submodular Optimization
This paper introduces unified projection-free Frank-Wolfe type algorithms for adversarial continuous DR-submodular optimization, spanning scenarios such as full information and (semi-)bandit feedback, monotone and non-monotone functions, different constraints, and types of stochastic queries.
A Generalized Approach to Online Convex Optimization
We also show that any such algorithm that requires full-information feedback may be transformed to an algorithm with semi-bandit feedback with comparable regret bound.
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning
In this paper, we prove the first Bayesian regret bounds for Thompson Sampling in reinforcement learning in a multitude of settings.
A Unified Approach for Maximizing Continuous DR-submodular Functions
This paper presents a unified approach for maximizing continuous DR-submodular functions that encompasses a range of settings and oracle access types.
Stochastic Submodular Bandits with Delayed Composite Anonymous Bandit Feedback
This paper investigates the problem of combinatorial multiarmed bandits with stochastic submodular (in expectation) rewards and full-bandit delayed feedback, where the delayed feedback is assumed to be composite and anonymous.
Coagent Networks Revisited
Coagent networks formalize the concept of arbitrary networks of stochastic agents that collaborate to take actions in a reinforcement learning environment.
