Regularized learning is a fundamental technique in online optimization, machine learning,
and many other fields of computer science. A natural question that arises in this context is …

Rethinking search engines and recommendation systems: a game theoretic perspective
Page 1 66 COMMUNICATIONS OF THE ACM | DECEMBER 2019 | VOL. 62 | NO. 12 review …

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 …

Competition between traditional platforms is known to improve user utility by aligning the
platform's actions with user preferences. But to what extent is alignment exhibited in data …

The role of assortment planning and pricing in sha** sales and profits of retailers is well
documented and studied in monopolistic settings. However, such a role remains relatively …

In the well-studied Colonel Blotto game, players must divide a pool of troops among a set of
battlefields with the goal of winning a majority. Despite the importance of this game, only a …

However, weaker competition incentivizes better exploration algorithms and increases
welfare. We investigate two channels for weakening the competition: stochastic user choice …

In game theory, the price of anarchy framework studies efficiency loss in decentralized
environments. Optimization and decision theory, on the other hand, explore tradeoffs …

The security game is a basic model for resource allocation in adversarial environments.
Here there are two players, a defender and an attacker. The defender wants to allocate her …

Most modern systems strive to learn from interactions with users, and many engage in
exploration: making potentially suboptimal choices for the sake of acquiring new information …