10 citations · 14 across the 10 of their papers we have counts for
10 papers
Gradient Dynamics in First-Price Auctions: Iterative Strategy Elimination via Cubic Potentials
Mete Şeref Ahunbay, Weiqiang Zheng, Tao Lin
We show that in discretised first-price auctions with complete information, if the buyers learn to bid with online gradient ascent, in time-average the outcome is (almost) the effi…
Learning Thresholds with Latent Values and Censored Feedback
Jiahao Zhang, Tao Lin, Weiqiang Zheng +3
In this paper, we investigate a problem of actively learning threshold in latent space, where the unknown reward depends on the proposed threshold and latent value $v…
Persuading a Behavioral Agent: Approximately Best Responding and Learning
Yiling Chen, Tao Lin
The classic Bayesian persuasion model assumes a Bayesian and best-responding receiver. We study a relaxation of the Bayesian persuasion model where the receiver can approximately b…
How Does Independence Help Generalization? Sample Complexity of ERM on Product Distributions
Tao Lin
While many classical notions of learnability (e.g., PAC learnability) are distribution-free, utilizing the specific structures of an input distribution may improve learning perform…
Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions
Xiaotie Deng, Xinyan Hu, Tao Lin +1
The convergence properties of learning dynamics in repeated auctions is a timely and important question, with numerous applications in, e.g., online advertising markets. This work…
From Monopoly to Competition: Optimal Contests Prevail
Xiaotie Deng, Yotam Gafni, Ron Lavi +2
We study competition among contests in a general model that allows for an arbitrary and heterogeneous space of contest design, where the goal of the contest designers is to maximiz…
The Optimal Size of an Epistemic Congress
Manon Revel, Tao Lin, Daniel Halpern
We analyze the optimal size of a congress in a representative democracy. We take an epistemic view where voters decide on a binary issue with one ground truth outcome, and each vot…
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
Xiaotie Deng, Ron Lavi, Tao Lin +3
The Empirical Revenue Maximization (ERM) is one of the most important price learning algorithms in auction design: as the literature shows it can learn approximately optimal reserv…
Learning Utilities and Equilibria in Non-Truthful Auctions
Hu Fu, Tao Lin
In non-truthful auctions, agents' utility for a strategy depends on the strategies of the opponents and also the prior distribution over their private types; the set of Bayes Nash…
On Clearing Prices in Matching Markets: A Simple Characterization without Duality
Xiaoming Li, Tao Lin
Duality of linear programming is a standard approach to the classical weighted maximum matching problem. From an economic perspective, the dual variables can be regarded as prices…