As the scale of machine learning models increases, trends such as scaling laws anticipate
consistent downstream improvements in predictive accuracy. However, these trends take the …

Recommendation systems are dynamic economic systems that balance the needs of
multiple stakeholders. A recent line of work studies incentives from the content providers' …

Real-world systems often involve some pool of users choosing between a set of services.
With the increase in popularity of online learning algorithms, these services can now self …

Emerging marketplaces for large language models and other large-scale machine learning
(ML) models appear to exhibit market concentration, which has raised concerns about …

Organizations typically train large models individually. This is costly and time-consuming,
particularly for large-scale foundation models. Such vertical production is known to be …

In this paper, we propose a new algorithm for addressing the problem of matching markets
with complementary preferences, where agents' preferences are unknown a priori and must …

ChatGPT has established Generative AI (GenAI) as a significant technological
advancement. However, GenAI's intricate relationship with competing platforms and its …

In digital markets comprised of many competing services, each user chooses between
multiple service providers according to their preferences, and the chosen service makes use …

When deployed in the world, a learning agent such as a recommender system or a chatbot
often repeatedly interacts with another learning agent (such as a user) over time. In many …

We focus on reducing communication overhead in multiplayer games, where frequently
exchanging strategies between players is not feasible and players have noisy or outdated …