1 citations · 2 across the 6 of their papers we have counts for
6 papers
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
Rui Ai, David Simchi-Levi, Feng Zhu
We study the pricing behavior of third-party platforms facing strategic agents. Assuming the platform is a revenue maximizer, it observes market features that generally affect dema…
Online Resource Allocation with Average Budget Constraints
Ruicheng Ao, Hongyu Chen, David Simchi-Levi +1
We consider the problem of online resource allocation with average budget constraints. At each time point the decision maker makes an irrevocable decision of whether to accept or r…
Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk
David Simchi-Levi, Zeyu Zheng, Feng Zhu
We study the optimal trade-off between expectation and tail risk for regret distribution in the stochastic multi-armed bandit model. We fully characterize the interplay among three…
ABS: Adaptive Bounded Staleness Converges Faster and Communicates Less
Qiao Tan, Feng Zhu, Jingjing Zhang
Wall-clock convergence time and communication rounds are critical performance metrics in distributed learning with parameter-server setting. While synchronous methods converge fast…
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits
David Simchi-Levi, Zeyu Zheng, Feng Zhu
We study the stochastic multi-armed bandit problem and design new policies that enjoy both worst-case optimality for expected regret and light-tailed risk for regret distribution.…
Offline Planning and Online Learning under Recovering Rewards
David Simchi-Levi, Zeyu Zheng, Feng Zhu
Motivated by emerging applications such as live-streaming e-commerce, promotions and recommendations, we introduce and solve a general class of non-stationary multi-armed bandit pr…