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Found 8 papers, 0 papers with code
Adversarial Contextual Bandits Go Kernelized
We study a generalization of the problem of online learning in adversarial linear contextual bandits by incorporating loss functions that belong to a reproducing kernel Hilbert space, which allows for a more flexible modeling of complex decision-making scenarios.
Kernelized Reinforcement Learning with Order Optimal Regret Bounds
In particular, with highly non-smooth kernels (such as Neural Tangent kernel or some Mat\'ern kernels) the existing results lead to trivial (superlinear in the number of episodes) regret bounds.
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits
We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts.
Learning to maximize global influence from local observations
The goal of the decision maker is to select the sequence of agents in a way that the total number of influenced nodes in the network.
Online learning in MDPs with linear function approximation and bandit feedback.
We consider the problem of online learning in an episodic Markov decision process, where the reward function is allowed to change between episodes in an adversarial manner and the learner only observes the rewards associated with its actions.
Online learning in MDPs with linear function approximation and bandit feedback
We consider an online learning problem where the learner interacts with a Markov decision process in a sequence of episodes, where the reward function is allowed to change between episodes in an adversarial manner and the learner only gets to observe the rewards associated with its actions.
Efficient and Robust Algorithms for Adversarial Linear Contextual Bandits
We consider an adversarial variant of the classic $K$-armed linear contextual bandit problem where the sequence of loss functions associated with each arm are allowed to change without restriction over time.
Online Influence Maximization with Local Observations
We consider an online influence maximization problem in which a decision maker selects a node among a large number of possibilities and places a piece of information at the node.
