activity
20192022
most citedHandling Missing Data with Graph Representation Learning

94 citations · 99 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Recursive Reasoning Graph for Multi-Agent Reinforcement Learning

Xiaobai Ma, David Isele, Jayesh K. Gupta +2

Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other. However, in scenarios requ…

cs.LG2020★ 2 cited

Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships

Xiaobai Ma, Jiachen Li, Mykel J. Kochenderfer +2

Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios. However, identifying the subtle cues that can indicate d…

cs.LG2020★ 94 cited

Handling Missing Data with Graph Representation Learning

Jiaxuan You, Xiaobai Ma, Daisy Yi Ding +2

Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and…

cs.LG2019★ 2 cited

Monte-Carlo Tree Search for Policy Optimization

Xiaobai Ma, Katherine Driggs-Campbell, Zongzhang Zhang +1

Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free met…

cs.LG2019★ 1 cited

Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning

Xiaobai Ma, Katherine Driggs-Campbell, Mykel J. Kochenderfer

To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the env…