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Found 4 papers, 1 papers with code
Semi-parametric dynamic contextual pricing
Motivated by the application of real-time pricing in e-commerce platforms, we consider the problem of revenue-maximization in a setting where the seller can leverage contextual information describing the customer's history and the product's type to predict her valuation of the product.
Optimal Testing in the Experiment-rich Regime
Motivated by the widespread adoption of large-scale A/B testing in industry, we propose a new experimentation framework for the setting where potential experiments are abundant (i. e., many hypotheses are available to test), and observations are costly; we refer to this as the experiment-rich regime.
Bandit Learning with Positive Externalities
In other words, arrivals exhibit positive externalities.
Adaptive Matching for Expert Systems with Uncertain Task Types
To address this challenge, we develop a model of a task-expert matching system where a task is matched to an expert using not only the prior information about the task but also the feedback obtained from the past matches.
