activity
20192022
most citedInformation-Theoretic Confidence Bounds for Reinforcement Learning

13 citations · 23 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ML2022

Evaluating High-Order Predictive Distributions in Deep Learning

Ian Osband, Zheng Wen, Seyed Mohammad Asghari +3

Most work on supervised learning research has focused on marginal predictions. In decision problems, joint predictive distributions are essential for good performance. Previous wor…

cs.CV2021

Event-based Motion Segmentation by Cascaded Two-Level Multi-Model Fitting

Xiuyuan Lu, Yi Zhou, Shaojie Shen

Among prerequisites for a synthetic agent to interact with dynamic scenes, the ability to identify independently moving objects is specifically important. From an application persp…

cs.RO2021

GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation

Shaozu Cao, Xiuyuan Lu, Shaojie Shen

Visual-Inertial odometry (VIO) is known to suffer from drifting especially over long-term runs. In this paper, we present GVINS, a non-linear optimization based system that tightly…

cs.LG202010 cited

Hypermodels for Exploration

Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi +3

We study the use of hypermodels to represent epistemic uncertainty and guide exploration. This generalizes and extends the use of ensembles to approximate Thompson sampling. The co…

stat.ML201913 cited

Information-Theoretic Confidence Bounds for Reinforcement Learning

Xiuyuan Lu, Benjamin Van Roy

We integrate information-theoretic concepts into the design and analysis of optimistic algorithms and Thompson sampling. By making a connection between information-theoretic quanti…