33 citations · 100 across the 9 of their papers we have counts for
11 papers
Phase Transitions in Learning and Earning under Price Protection Guarantee
Qing Feng, Ruihao Zhu, Stefanus Jasin
Motivated by the prevalence of ``price protection guarantee", which allows a customer who purchased a product in the past to receive a refund from the seller during the so-called p…
Risk-Aware Linear Bandits: Theory and Applications in Smart Order Routing
Jingwei Ji, Renyuan Xu, Ruihao Zhu
Motivated by practical considerations in machine learning for financial decision-making, such as risk aversion and large action space, we consider risk-aware bandits optimization w…
Safe Data Collection for Offline and Online Policy Learning
Ruihao Zhu, Branislav Kveton
Motivated by practical needs of experimentation and policy learning in online platforms, we study the problem of safe data collection. Specifically, our goal is to develop a loggin…
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control
Weichao Mao, Kaiqing Zhang, Ruihao Zhu +2
We consider model-free reinforcement learning (RL) in non-stationary Markov decision processes. Both the reward functions and the state transition functions are allowed to vary arb…
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under drifting non-stationarity, i.e., both the reward and state transition distributions…
Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under temporal drifts, ie, both the reward and state transition distributions are allowed…
Hedging the Drift: Learning to Optimize under Non-Stationarity
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We introduce data-driven decision-making algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary bandit settings. These settings capture applicatio…
Meta Dynamic Pricing: Transfer Learning Across Experiments
Hamsa Bastani, David Simchi-Levi, Ruihao Zhu
We study the problem of learning shared structure \emph{across} a sequence of dynamic pricing experiments for related products. We consider a practical formulation where the unknow…
Learning to Route Efficiently with End-to-End Feedback: The Value of Networked Structure
Ruihao Zhu, Eytan Modiano
We introduce efficient algorithms which achieve nearly optimal regrets for the problem of stochastic online shortest path routing with end-to-end feedback. The setting is a natural…
Learning to Optimize under Non-Stationarity
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We introduce algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary linear stochastic bandit setting. It captures natural applications such as dyn…
PRINCE: Privacy-Preserving Mechanisms for Influence Diffusion in Online Social Networks
Ruihao Zhu, Dongxin Liu, Fan Wu +1
This paper has been withdrawn by the author due to a crucial sign error in equation 1. With the advance of online social networks, there has been extensive research on how to sprea…