Tao Lin
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    • 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
    • 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
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    • On Clearing Prices in Matching Markets: A Simple Characterization without Duality
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scikit-learn

Oct 26, 2023 ยท 1 min read
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scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Last updated on Oct 26, 2023
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Tao Lin
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Tao Lin
PhD student in Computer Science

← PyTorch Oct 26, 2023

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

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