4 citations · 12 across the 25 of their papers we have counts for
7 papers · 1 filter
Stochastic Linear Optimization with Adversarial Corruption
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). Our algorithm is agn…
Approximately Maximizing the Broker's Profit in a Two-sided Market
Jing Chen, Bo Li, Yingkai Li
We study how to maximize the broker's (expected) profit in a two-sided market, where she buys items from a set of sellers and resells them to a set of buyers. Each seller has a sin…
Optimal Auctions vs. Anonymous Pricing: Beyond Linear Utility
Yiding Feng, Jason D. Hartline, Yingkai Li
The revenue optimal mechanism for selling a single item to agents with independent but non-identically distributed values is complex for agents with linear utility (Myerson,1981) a…
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits
Yingkai Li, Yining Wang, Xi Chen +1
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…
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits
Yingkai Li, Yining Wang, Yuan Zhou
We study the linear contextual bandit problem with finite action sets. When the problem dimension is , the time horizon is , and there are candidate actions…
Revenue Maximization with Imprecise Distribution
Yingkai Li, Pinyan Lu, Haoran Ye
We study the revenue maximization problem with an imprecisely estimated distribution of a single buyer or several independent and identically distributed buyers given that this est…