6 citations · 35 across the 26 of their papers we have counts for
12 papers · 1 filter
Strategic Feature Selection
Jivat Neet Kaur, Pratik Patil, Divya Shanmugam +6
When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The ty…
Breaking Algorithmic Collusion in Human-AI Ecosystems
Natalie Collina, Eshwar Ram Arunachaleswaran, Meena Jagadeesan
AI agents are increasingly deployed in ecosystems where they repeatedly interact not only with each other but also with humans. In this work, we study these human-AI ecosystems fro…
Safety vs. Performance: How Multi-Objective Learning Reduces Barriers to Market Entry
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt
Emerging marketplaces for large language models and other large-scale machine learning (ML) models appear to exhibit market concentration, which has raised concerns about whether t…
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
Sarah Dean, Evan Dong, Meena Jagadeesan +1
As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors. However, the design of…
Impact of Decentralized Learning on Player Utilities in Stackelberg Games
Kate Donahue, Nicole Immorlica, Meena Jagadeesan +2
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 su…
Feedback Loops With Language Models Drive In-Context Reward Hacking
Alexander Pan, Erik Jones, Meena Jagadeesan +1
Language models influence the external world: they query APIs that read and write to web pages, generate content that shapes human behavior, and run system commands as autonomous a…
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
Jessica Dai, Bailey Flanigan, Nika Haghtalab +2
A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare. In this work, we show that misalign…
Performative Power
Moritz Hardt, Meena Jagadeesan, Celestine Mendler-Dünner
We introduce the notion of performative power, which measures the ability of a firm operating an algorithmic system, such as a digital content recommendation platform, to cause cha…
Regret Minimization with Performative Feedback
Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner
In performative prediction, the deployment of a predictive model triggers a shift in the data distribution. As these shifts are typically unknown ahead of time, the learner needs t…
Learning Equilibria in Matching Markets from Bandit Feedback
Meena Jagadeesan, Alexander Wei, Yixin Wang +2
Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data. Classical notions of…
Alternative Microfoundations for Strategic Classification
Meena Jagadeesan, Celestine Mendler-Dünner, Moritz Hardt
When reasoning about strategic behavior in a machine learning context it is tempting to combine standard microfoundations of rational agents with the statistical decision theory un…
Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
Meena Jagadeesan, Ilya Razenshteyn, Suriya Gunasekar
We provide a function space characterization of the inductive bias resulting from minimizing the norm of the weights in multi-channel convolutional neural networks with li…