Tao Lin
  • Bio
  • News
  • Research
  • Publications
  • Talks
  • Experience
  • Recent & Upcoming Talks
    • Example Talk
  • Publications
    • A Unified Approach to Submodular Maximization Under Noise
    • Generalized Principal-Agent Problem with a Learning Agent
    • Information Design with Unknown Prior
    • User-Creator Feature Polarization in Recommender Systems with Dual Influence
    • Bias Detection via Signaling
    • Multi-Sender Persuasion: A Computational Perspective
    • Learning Thresholds with Latent Values and Censored Feedback
    • Sample Complexity of Forecast Aggregation
    • From Monopoly to Competition: Optimal Contests Prevail
    • Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions
    • How Many Representatives Do We Need? The Optimal Size of a Congress Voting on Binary Issues
    • Learning Utilities and Equilibria in Non-Truthful Auctions
    • A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
    • Private Data Manipulation in Optimal Sponsored Search Auction
  • Recent & Upcoming Talks
    • Learning to Coordinate Bidders in Non-Truthful Auctions
    • How to Avoid Polarization in Recommender Systems with Dual Influence?
    • Bayesian Persuasion with a Learning Agent
    • Generalized Principal-Agent Problem with a Learning Agent
    • Private Data Manipulation in Sponsored Search Auctions
    • Sample Complexity of Forecast Aggregation
    • Persuading a Behavioral Agent: Approximately Best Responding and Learning
    • Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions
    • Robustness of Empirical Revenue Maximization in Auction Learning
  • Working Papers
    • Learning to Play Multi-Follower Bayesian Stackelberg Games
    • Information Design With Large Language Models
    • WOMAC: A Mechanism For Prediction Competitions
    • Explainable Information Design
    • Learning to Coordinate Bidders in Non-Truthful Auctions
    • Learning a Game by Paying the Agents
  • Journal Publications
    • From Monopoly to Competition: When Do Optimal Contests Prevail?
  • Projects
  • Blog
    • 🎉 Easily create your own simple yet highly customizable blog
    • 🧠 Sharpen your thinking with a second brain
    • 📈 Communicate your results effectively with the best data visualizations
    • 👩🏼‍🏫 Teach academic courses
    • ✅ Manage your projects
  • Example_projects
    • Pandas
    • PyTorch
    • scikit-learn
  • Experience
  • Teaching
    • Learn JavaScript
    • Learn Python
  • Notes
    • How Does Independence Help Generalization? Sample Complexity of ERM on Product Distributions
    • On Clearing Prices in Matching Markets: A Simple Characterization without Duality
  • Example_publications
    • An example preprint / working paper
    • An example journal article
    • An example conference paper

Learning to Coordinate Bidders in Non-Truthful Auctions

Oct 27, 2025·
Tao Lin
Tao Lin
· 0 min read
Abstract
Presentation for our working paper “Learning to Coordinate Bidders in Non-Truthful Auctions”.
Event
2025 INFORMS Annual Meeting
Location

Atlanta

Georgia, USA

Last updated on Oct 27, 2025
Tao Lin
Authors
Tao Lin
PhD in Computer Science
How to Avoid Polarization in Recommender Systems with Dual Influence? Nov 15, 2024 →

© 2025. This work is licensed under CC BY NC ND 4.0

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.