no code implementations • 29 Oct 2023 • Yingkai Li, Jonathan Libgober
A principal seeks to learn about a binary state and can do so by enlisting an agent to acquire information over time using a Poisson information arrival technology.
no code implementations • 16 Oct 2023 • Dirk Bergemann, Tan Gan, Yingkai Li
The optimal decision rule is a quota rule, i. e., the decision rule maximizes the receiver's ex-ante payoff subject to the constraint that the marginal distribution over actions adheres to a consistent quota, regardless of the sender's chosen signal structure.
no code implementations • 17 Feb 2023 • Yingkai Li, Xiaoyun Qiu
We study the design of screening mechanisms subject to competition and manipulation.
no code implementations • 13 Jul 2021 • Moshe Babaioff, Nicole Immorlica, Yingkai Li, Brendan Lucier
We show that when using balanced prices, both these approaches ensure high equilibrium welfare in the combined market.
no code implementations • 9 Mar 2021 • Yingkai Li
We consider a model of a data broker selling information to a single agent to maximize his revenue.
no code implementations • 14 Dec 2020 • Yingkai Li, Harry Pei
We examine the long-term behavior of a Bayesian agent who has a misspecified belief about the time lag between actions and feedback, and learns about the payoff consequences of his actions over time.
no code implementations • 8 Dec 2020 • Jiarui Gan, Bo Li, Yingkai Li
Clearly, the strong notion of envy-freeness, where no agent envies another for their resource or mates, cannot always be achieved and we show that even deciding the existence of such a strongly envy-free assignment is an intractable problem.
Computer Science and Game Theory
no code implementations • 28 Jul 2020 • Yingkai Li, Harry Pei
We examine a patient player's behavior when he can build reputations in front of a sequence of myopic opponents.
no code implementations • ICML 2020 • Kefan Dong, Yingkai Li, Qin Zhang, Yuan Zhou
We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.
no code implementations • 4 Sep 2019 • Yingkai Li, Edmund Y. Lou, Liren Shan
We extend the model of stochastic bandits with adversarial corruption (Lykouriset al., 2018) to the stochastic linear optimization problem (Dani et al., 2008).
no code implementations • 4 May 2019 • Yingkai Li, Yining Wang, Xi Chen, Yuan Zhou
Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks.
no code implementations • 30 Mar 2019 • Yingkai Li, Yining Wang, Yuan Zhou
We study the linear contextual bandit problem with finite action sets.
1 code implementation • 7 May 2018 • Yingkai Li, Huidong Liu
In this paper, we implement the Stochastic Damped LBFGS (SdLBFGS) for stochastic non-convex optimization.