Jianqing Fan

Two-way Homogeneity Pursuit for Quantile Network Vector Autoregression

We propose a two-way grouped network quantile (TGNQ) autoregression model for time series data observed on networks with substantial heterogeneity and complex directional interactions. Motivated by prior studies on network effects in directed networks, our model assigns each node...

Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning

This paper studies reward-agnostic exploration in reinforcement learning (RL)—a scenario where the learner is unaware of the reward functions during the exploration stage—and designs an algorithm that improves over the state of the art. More precisely, consider a finite-horizon i...