10 citations · 19 across the 9 of their papers we have counts for
9 papers
Non-Stationary Contextual Bandit Learning via Neural Predictive Ensemble Sampling
Zheqing Zhu, Yueyang Liu, Xu Kuang +1
Real-world applications of contextual bandits often exhibit non-stationarity due to seasonality, serendipity, and evolving social trends. While a number of non-stationary contextua…
Learning-based Two-tiered Online Optimization of Region-wide Datacenter Resource Allocation
Chang-Lin Chen, Hanhan Zhou, Jiayu Chen +8
Online optimization of resource management for large-scale data centers and infrastructures to meet dynamic capacity reservation demands and various practical constraints (e.g., fe…
Scalable Neural Contextual Bandit for Recommender Systems
Zheqing Zhu, Benjamin Van Roy
High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neu…
IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control
Rohan Chitnis, Yingchen Xu, Bobak Hashemi +4
Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline setti…
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
Ruiyang Xu, Jalaj Bhandari, Dmytro Korenkevych +4
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected retur…
Evaluating Online Bandit Exploration In Large-Scale Recommender System
Hongbo Guo, Ruben Naeff, Alex Nikulkov +1
Bandit learning has been an increasingly popular design choice for recommender system. Despite the strong interest in bandit learning from the community, there remains multiple bot…
Deep Exploration for Recommendation Systems
Zheqing Zhu, Benjamin Van Roy
Modern recommendation systems ought to benefit by probing for and learning from delayed feedback. Research has tended to focus on learning from a user's response to a single recomm…
Multi-Agent Safe Planning with Gaussian Processes
Zheqing Zhu, Erdem Bıyık, Dorsa Sadigh
Multi-agent safe systems have become an increasingly important area of study as we can now easily have multiple AI-powered systems operating together. In such settings, we need to…
A Modified Levy Jump-Diffusion Model Based on Market Sentiment Memory for Online Jump Prediction
Zheqing Zhu, Jian-guo Liu, Lei Li
In this paper, we propose a modified Levy jump diffusion model with market sentiment memory for stock prices, where the market sentiment comes from data mining implementation using…