We present Self-Play Preference Optimization (SPO), an algorithm for reinforcement
learning from human feedback. Our approach is minimalist in that it does not require training …

We study the $ K $-armed contextual dueling bandit problem, a sequential decision making
setting in which the learner uses contextual information to make two decisions, but only …

We study the problem of $ K $-armed dueling bandit for both stochastic and adversarial
environments, where the goal of the learner is to aggregate information through relative …

Dueling bandits is a prominent framework for decision-making involving preferential
feedback, a valuable feature that fits various applications involving human interaction, such …

We consider the problem of reward maximization in the dueling bandit setup along with
constraints on resource consumption. As in the classic dueling bandits, at each round the …

Dueling bandits are widely used to model preferential feedback prevalent in many
applications such as recommendation systems and ranking. In this paper, we study the …

Learning from human feedback plays an important role in aligning generative models, such
as large language models (LLM). However, the effectiveness of this approach can be …

We study the problem of non-stationary dueling bandits and provide the first adaptive
dynamic regret algorithm for this problem. The only two existing attempts in this line of work …

We address the problem of Internal Regret in adversarial Sleeping Bandits and the
relationship between different notions of sleeping regrets in multi-armed bandits. We …

We study the problem of dynamic regret minimization in $ K $-armed Dueling Bandits under
non-stationary or time-varying preferences. This is an online learning setup where the agent …