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Tao Lin

PhD student in Computer Science

Harvard University

Biography

I am a third-year PhD student in Computer Science at Harvard University, where I am very fortunate to be advised by Prof. Yiling Chen. My research interest lies in the intersection between economics and machine learning. I have been working on “mechanism design + machine learning” problems since my undergraduate study at Peking University, working with Prof. Xiaotie Deng. Recently, I started to look into information design problems like information aggregation and Bayesian persuasion, also from a machine learning perspective.

Interests

  • Mechanism Design
  • Information Design
  • Machine Learning

Education

  • BSc in EECS, 2020

    Peking University

Working Papers

Yiling Chen, Tao Lin (2023). Persuading a Behavioral Agent: Approximately Best Responding and Learning. Working Paper.

PDF arXiv

Yiling Chen, Tao Lin (2022). Sample Complexity of Forecast Aggregation. Working Paper.

PDF arXiv

Publications

Xiaotie Deng, Yotam Gafni, Ron Lavi, Tao Lin, Hongyi Ling (2023). From Monopoly to Competition: Optimal Contests Prevail. AAAI.

PDF arXiv

Xiaotie Deng, Xinyan Hu, Tao Lin, Weiqiang Zheng (2022). Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions. The Web Conference (WWW).

PDF DOI arXiv slides 15-min video 2-min video by Xinyan

Manon Revel, Tao Lin, Daniel Halpern (2022). How Many Representatives Do We Need? The Optimal Size of an Epistemic Congress. AAAI.

DOI arXiv video

Xiaotie Deng, Ron Lavi, Tao Lin, Qi Qi, Wenwei Wang, Xiang Yan (2020). A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling. NeurIPS.

PDF arXiv slides publisher site

Hu Fu, Tao Lin (2020). Learning Utilities and Equilibria in Non-Truthful Auctions. NeurIPS.

PDF arXiv publisher site

Xiaotie Deng, Tao Lin, Tao Xiao (2020). Private Data Manipulation in Optimal Sponsored Search Auction. The Web Conference (WWW).

PDF DOI

Notes (not intended to publish)

Tao Lin (2022). How Does Independence Help Generalization? Sample Complexity of ERM on Product Distributions. Notes.

PDF arXiv

Xiaoming Li, Tao Lin (2019). On Clearing Prices in Matching Markets: A Simple Characterization without Duality. Notes.

PDF arXiv

Talks

Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions
May 10, 2022 AAMAS Workshop on Learning with Strategic Agents
Robustness of Empirical Revenue Maximization in Auction Learning
Dec 23, 2020 Institute for Theoretical Computer Science (ITCS), SUFE

Experience

 
 
 
 
 

Research Intern

University of British Columbia

Jun 2019 – Sep 2019 Vancouver
Advised by Hu Fu.
 
 
 
 
 

Research Assistant

Center on Frontiers of Computing Studies, Peking University

Sep 2018 – Sep 2021 Beijing
Advised by Xiaotie Deng.

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