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Found 5 papers, 1 papers with code
A Probabilistic Approach for Alignment with Human Comparisons
To bridge this gap, this paper studies the effective use of human comparisons to address limitations arising from noisy data and high-dimensional models.
Speed Up the Cold-Start Learning in Two-Sided Bandits with Many Arms
Next, we design two-phase bandit algorithms that first use subsampling and low-rank matrix estimation to obtain a substantially smaller targeted set of products and then apply a UCB procedure on the target products to find the best one.
Fatigue-aware Bandits for Dependent Click Models
Based on user's feedback, the platform learns the relevance of the underlying content as well as the discounting effect due to content fatigue.
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem
Motivated by the phenomenon that companies introduce new products to keep abreast with customers' rapidly changing tastes, we consider a novel online learning setting where a profit-maximizing seller needs to learn customers' preferences through offering recommendations, which may contain existing products and new products that are launched in the middle of a selling period.
Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue
Based on user feedback, the platform dynamically learns users' abandonment distribution and their valuations of messages to determine the length of the sequence and the order of the messages, while maximizing the cumulative payoff over a horizon of length T. We refer to this online learning task as the sequential choice bandit problem.
