TCS

Following yesterday’s post (here), let’s delve deeper into Stackelberg Games and the key points of the paper, particularly the addition of context to the problem setting. Regret Minimization in Stackelberg Games with Side Information Keegan Harris, Zhiwei Steven Wu, Maria-Florina Balcan (2024) | paper’s arxiv link recap of the mode: A Stackelberg Security Game is a structured competitive setting involving a defender and an attacker. The defender commits to a strategy $ \mathbf p \in \mathbb{R}^n $ over $ n $ targets, and the attacker selects a target....

Of course, the general purpose of an academic presentation is multifaceted (see an older post about it), as discussed here. Nevertheless, I’ve once heard someone say that the key purpose of a talk at a conference is to make your audience interested in reading your work after the talk ends. I attended the RAIN seminar yesterday at Y2E2, Stanford, where Nina Balcan presented one of her latest works. Personally, I have a general interest in research that involves complex human behaviors....

I had the very fortune to listen to Nina Balcan giving a talk on one of her latest work, Online learning in Stackelberg Security Games: ABSTRACT In a Stackelberg Security Game, a defender commits to a randomized deployment of security resources, and an attacker best responds by attacking a target that maximizes their utility. While algorithms for computing an optimal strategy for the defender to commit to have been used in several real-world applications, deployed applications require knowledge about the utility function of the potential attacker....

A memoir of Market Mechanism Design course’s final presentation report, based on: Algorithmic Classification and Strategic Effort Jon Kleinberg and Manish Raghavan | ACM SIGecom Exchanges, Vol. 18, No. 2, November 2020, Pages 53–60 motivation: difference in modelling strategic behavior and objectives–between econ/CS perspectives The principal-agent and strategic machine learning literatures appear to share a common goal: how should one structure a decision-making rule to account for the strategic actions of decision subjects?...

speaker: Guillermo Gallego | Prof., The Chinese University of Hong Kong-Shenzhen. TALK ABSTRACT: To study the limits of personalization, we introduce the notion of a clairvoyant firm that can read the mind of consumers and sell them the highest revenue product that they are willing to buy. We show how to compute the expected revenue of the clairvoyant firm for a class of rational discrete choice models, and develop prophet-type inequalities that provide performance guarantees for the expected revenue of the traditional assortment optimization firm (a TAOP firm) relative to the clairvoyant firm, and therefore to any effort to personalize assortments....

This week, Chenhao Zhang from Northwestern University visited ITCS and gave a talk on Regulation of Algorithmic Collusion, based on his ongoing collaboration with Prof. Jason Hartline. Here’s a background of the topic, summary of the talk and their work (hopefully), and some discussion afterwards. Regulation of Algorithmic Collusion ABSTRACT Consider sellers in a competitive market that use algorithms to adapt their prices from data that they collect. In such a context it is plausible that algorithms could arrive at prices that are higher than the competitive prices and this may benefit sellers at the expense of consumers (i....