10 citations · 10 across the 4 of their papers we have counts for
6 papers
First-Order Bilevel Optimization for Non-truthful Auction Design
Shing-Hei Ho, April Niu, Ellen Vitercik +1
Automated mechanism design has enabled the learning of complex auction mechanisms using neural networks. However, designing non-truthful auctions remains challenging because bidder…
Bayesian Retraction Optimization for Tissue Attachment Mapping in Surgical Dissection
Shing-Hei Ho, Bao Thach, Toan Vo +2
With growing surgeon shortages, automating surgical sub-tasks such as tissue dissection offers a promising step toward reducing workload and expanding patient access. Prior work ha…
A Fully First-Order Layer for Differentiable Optimization
Zihao Zhao, Kai-Chia Mo, Shing-Hei Ho +2
Differentiable optimization layers enable learning systems to make decisions by solving embedded optimization problems. However, computing gradients via implicit differentiation re…
DiffDef: A Diffusion Model for Generating Multimodal Goal Shapes From Demonstrations for Deformable Object Manipulation
Bao Thach, Tanner Watts, Siyeon Kim +6
Deformable object manipulation is a key capability in many robotic applications. A promising paradigm for this problem is shape servoing, which aims to control deformable objects t…
LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes
Shing-Hei Ho, Bao Thach, Minghan Zhu
We present LiDAR-EDIT, a novel paradigm for generating synthetic LiDAR data for autonomous driving. Our framework edits real-world LiDAR scans by introducing new object layouts whi…
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery
Zohre Karimi, Shing-Hei Ho, Bao Thach +2
Automating robotic surgery via learning from demonstration (LfD) techniques is extremely challenging. This is because surgical tasks often involve sequential decision-making proces…