Search Results for author: Meena Jagadeesan

Found 17 papers, 5 papers with code

Impact of Decentralized Learning on Player Utilities in Stackelberg Games

no code implementations29 Feb 2024 Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, Aleksandrs Slivkins

To better understand such cases, we examine the learning dynamics of the two-agent system and the implications for each agent's objective.

Chatbot Recommendation Systems

Feedback Loops With Language Models Drive In-Context Reward Hacking

1 code implementation9 Feb 2024 Alexander Pan, Erik Jones, Meena Jagadeesan, Jacob Steinhardt

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 agents.

Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms

no code implementations18 Jan 2024 Nicole Immorlica, Meena Jagadeesan, Brendan Lucier

To understand the total impact on the content landscape, we study a game between content creators competing on the basis of engagement metrics and analyze the equilibrium decisions about investment in quality and gaming.

Can Probabilistic Feedback Drive User Impacts in Online Platforms?

no code implementations10 Jan 2024 Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, Chara Podimata

A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare.

Recommendation Systems

Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition

1 code implementation NeurIPS 2023 Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, Nika Haghtalab

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

Incentivizing High-Quality Content in Online Recommender Systems

no code implementations13 Jun 2023 Xinyan Hu, Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt

For content recommender systems such as TikTok and YouTube, the platform's decision algorithm shapes the incentives of content producers, including how much effort the content producers invest in the quality of their content.

Recommendation Systems

Competition, Alignment, and Equilibria in Digital Marketplaces

no code implementations30 Aug 2022 Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab

Nonetheless, the data sharing assumptions impact what mechanism drives misalignment and also affect the specific form of misalignment (e. g. the quality of the best-case and worst-case market outcomes).

Supply-Side Equilibria in Recommender Systems

1 code implementation NeurIPS 2023 Meena Jagadeesan, Nikhil Garg, Jacob Steinhardt

Producers seek to create content that will be shown by the recommendation algorithm, which can impact both the diversity and quality of their content.

Recommendation Systems

Performative Power

no code implementations31 Mar 2022 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 change in a population of participants.

Regret Minimization with Performative Feedback

no code implementations1 Feb 2022 Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner

Our main contribution is an algorithm that achieves regret bounds scaling only with the complexity of the distribution shifts and not that of the reward function.

Learning Equilibria in Matching Markets from Bandit Feedback

no code implementations NeurIPS 2021 Meena Jagadeesan, Alexander Wei, Yixin Wang, Michael I. Jordan, Jacob Steinhardt

Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data.

Alternative Microfoundations for Strategic Classification

no code implementations24 Jun 2021 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 underlying classification.

Binary Classification Classification +2

Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm

1 code implementation24 Feb 2021 Meena Jagadeesan, Ilya Razenshteyn, Suriya Gunasekar

We provide a function space characterization of the inductive bias resulting from minimizing the $\ell_2$ norm of the weights in multi-channel convolutional neural networks with linear activations and empirically test our resulting hypothesis on ReLU networks trained using gradient descent.

Inductive Bias

Individual Fairness in Pipelines

no code implementations12 Apr 2020 Cynthia Dwork, Christina Ilvento, Meena Jagadeesan

It is well understood that a system built from individually fair components may not itself be individually fair.

Fairness General Classification

Individual Fairness in Advertising Auctions through Inverse Proportionality

no code implementations31 Mar 2020 Shuchi Chawla, Meena Jagadeesan

This value stability constraint is expressed as a function that maps the multiplicative distance between value vectors to the maximum allowable $\ell_{\infty}$ distance between the corresponding allocations.

Fairness

Multi-Category Fairness in Sponsored Search Auctions

no code implementations20 Jun 2019 Shuchi Chawla, Christina Ilvento, Meena Jagadeesan

Fairness in advertising is a topic of particular concern motivated by theoretical and empirical observations in both the computer science and economics literature.

Fairness

Understanding Sparse JL for Feature Hashing

1 code implementation NeurIPS 2019 Meena Jagadeesan

al (ICML '09) analyzes the accuracy of sparse JL with sparsity 1 on feature vectors with small $\ell_\infty$-to-$\ell_2$ norm ratio.

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