Avatar

Tao Lin

PhD student in Computer Science

Harvard University

Biography

I am a fourth-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

*: alphabetical or random order.

Tao Lin, Yiling Chen (2024). Persuading a Learning Agent. Working paper.

PDF arXiv

Safwan Hossain*, Tonghan Wang*, Tao Lin*, Yiling Chen, David Parkes, Haifeng Xu (2024). Multi-Sender Persuasion -- A Computational Perspective. Working paper.

PDF arXiv

Publications

Jiahao Zhang, Tao Lin, Weiqiang Zheng, Zhe Feng, Yifeng Teng, Xiaotie Deng (2024). Learning Thresholds with Latent Values and Censored Feedback. ICLR.

PDF arXiv

Tao Lin, Yiling Chen (2023). Sample Complexity of Forecast Aggregation. NeurIPS (spotlight).

PDF Poster arXiv

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

PDF Poster 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)

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

PDF arXiv

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

PDF arXiv

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

PDF arXiv

Talks

Private Data Manipulation in Sponsored Search Auctions
Aug 17, 2023 Invited talk at CCF Annual Conference on Computational Economics
Sample Complexity of Forecast Aggregation
Jun 14, 2023 Peking University Turing Class "CS peer talk"
Persuading a Behavioral Agent: Approximately Best Responding and Learning
Mar 3, 2023 Harvard EconCS seminar
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

ByteDance

May 2023 – Sep 2023
Advised by Yang Liu.
 
 
 
 
 

Summer Research Visit

University of British Columbia

Jun 2019 – Sep 2019
Advised by Hu Fu.
 
 
 
 
 

Research Assistant

Center on Frontiers of Computing Studies, Peking University

Sep 2018 – Sep 2021
Advised by Xiaotie Deng.

Contact