67 citations · 106 across the 9 of their papers we have counts for
9 papers
Confidence Aware Inverse Constrained Reinforcement Learning
Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi +2
In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement lea…
An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient
Yudong Luo, Guiliang Liu, Pascal Poupart +1
Restricting the variance of a policy's return is a popular choice in risk-averse Reinforcement Learning (RL) due to its clear mathematical definition and easy interpretability. Tra…
Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation
Rongyu Zhang, Xiaowei Chi, Guiliang Liu +3
Multimodal learning has seen great success mining data features from multiple modalities with remarkable model performance improvement. Meanwhile, federated learning (FL) addresses…
Benchmarking Constraint Inference in Inverse Reinforcement Learning
Guiliang Liu, Yudong Luo, Ashish Gaurav +2
When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints. In many real-world problems…
Learning Soft Constraints From Constrained Expert Demonstrations
Ashish Gaurav, Kasra Rezaee, Guiliang Liu +1
Inverse reinforcement learning (IRL) methods assume that the expert data is generated by an agent optimizing some reward function. However, in many settings, the agent may optimize…
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge
Xiangyu Sun, Oliver Schulte, Guiliang Liu +1
We describe NTS-NOTEARS, a score-based structure learning method for time-series data to learn dynamic Bayesian networks (DBNs) that captures nonlinear, lagged (inter-slice) and in…
Cracking the Black Box: Distilling Deep Sports Analytics
Xiangyu Sun, Jack Davis, Oliver Schulte +1
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics. Neural nets achieve great predictive accuracy through deep learn…
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees
Guiliang Liu, Oliver Schulte, Wang Zhu +1
Deep Reinforcement Learning (DRL) has achieved impressive success in many applications. A key component of many DRL models is a neural network representing a Q function, to estimat…
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation
Guiliang Liu, Oliver Schulte
A variety of machine learning models have been proposed to assess the performance of players in professional sports. However, they have only a limited ability to model how player p…