24 citations · 37 across the 8 of their papers we have counts for
8 papers
PyXAB -- A Python Library for -Armed Bandit and Online Blackbox Optimization Algorithms
Wenjie Li, Haoze Li, Jean Honorio +1
We introduce a Python open-source library for -armed bandit and online blackbox optimization named PyXAB. PyXAB contains the implementations for more than 10 $\mathcal…
Always Valid Risk Monitoring for Online Matrix Completion
Chi-Hua Wang, Wenjie Li
Always-valid concentration inequalities are increasingly used as performance measures for online statistical learning, notably in the learning of generative models and supervised l…
Federated X-Armed Bandit
Wenjie Li, Qifan Song, Jean Honorio +1
This work establishes the first framework of federated -armed bandit, where different clients face heterogeneous local objective functions defined on the same domain a…
Federated Online Sparse Decision Making
Chi-Hua Wang, Wenjie Li, Guang Cheng +1
This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and…
Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization
Wenjie Li, Chi-Hua Wang, Guang Cheng +1
In this paper, we make the key delineation on the roles of resolution and statistical uncertainty in hierarchical bandits-based black-box optimization algorithms, guiding a more ge…
A Simple Unified Framework for High Dimensional Bandit Problems
Wenjie Li, Adarsh Barik, Jean Honorio
Stochastic high dimensional bandit problems with low dimensional structures are useful in different applications such as online advertising and drug discovery. In this work, we pro…
Variance Reduction on General Adaptive Stochastic Mirror Descent
Wenjie Li, Zhanyu Wang, Yichen Zhang +1
In this work, we investigate the idea of variance reduction by studying its properties with general adaptive mirror descent algorithms in nonsmooth nonconvex finite-sum optimizatio…
AdaX: Adaptive Gradient Descent with Exponential Long Term Memory
Wenjie Li, Zhaoyang Zhang, Xinjiang Wang +1
Although adaptive optimization algorithms such as Adam show fast convergence in many machine learning tasks, this paper identifies a problem of Adam by analyzing its performance in…