1 citations · 1 across the 4 of their papers we have counts for
5 papers
Performances of Symmetric Loss for Private Data from Exponential Mechanism
Jing Bi, Vorapong Suppakitpaisarn
This study explores the robustness of learning by symmetric loss on private data. Specifically, we leverage exponential mechanism (EM) on private labels. First, we theoretically re…
Procedure Planning in Instructional Videos via Contextual Modeling and Model-based Policy Learning
Jing Bi, Jiebo Luo, Chenliang Xu
Learning new skills by observing humans' behaviors is an essential capability of AI. In this work, we leverage instructional videos to study humans' decision-making processes, focu…
Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences
Jing Shi, Jing Bi, Yingru Liu +1
The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences. While ODE produces the smooth…
Learning from Interventions using Hierarchical Policies for Safe Learning
Jing Bi, Vikas Dhiman, Tianyou Xiao +1
Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demo…
Navigation by Imitation in a Pedestrian-Rich Environment
Jing Bi, Tianyou Xiao, Qiuyue Sun +1
Deep neural networks trained on demonstrations of human actions give robot the ability to perform self-driving on the road. However, navigation in a pedestrian-rich environment, su…