Revenue management (RM) is the application of analytical methodologies and tools that
predict consumer behavior and optimize product availability and prices to maximize a firm's …

We initiate the study of episodic reinforcement learning under adversarial corruptions in both
the rewards and the transition probabilities of the underlying system extending recent results …

In digital online advertising, advertisers procure ad impressions simultaneously on multiple
platforms, or so-called channels, such as Google Ads, Meta Ads Manager, etc., each of …

We introduce data-driven decision-making algorithms that achieve state-of-the-art dynamic
regret bounds for a collection of nonstationary stochastic bandit settings. These settings …

Motivated by online decision-making in time-varying combinatorial environments, we study
the problem of transforming offline algorithms to their online counterparts. We focus on …

Many online platforms, ranging from online retail stores to social media platforms, employ
algorithms to optimize their offered assortment of items (eg, products and contents). These …

Today's online advertisers procure digital ad impressions through interacting with
autobidding platforms: advertisers convey high level procurement goals via setting levers …

Online retailing has seen steady growth over the last decade. According to the Digital
Commerce (formerly Internet Retailer) analysis of the US Commerce Department's year-end …

The proliferation of third-party platforms has led to the same product or service appearing
across multiple platforms. To facilitate consumers' purchase decisions, it is essential to rank …

This paper studies product ranking mechanisms of a monopolistic online platform in the
presence of social learning. The products' quality is initially unknown, but consumers can …