most citedOutlier-Robust Training of Machine Learning Models

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

cs.RO2025

A Roadmap for Climate-Relevant Robotics Research

Alan Papalia, Charles Dawson, Laurentiu L. Anton +25

Climate change is one of the defining challenges of the 21st century, and many in the robotics community are looking for ways to contribute. This paper presents a roadmap for clima…

cs.RO2025

Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automation

Xihang Yu, Rajat Talak, Jingnan Shi +3

Modern warehouse automation systems rely on fleets of intelligent robots that generate vast amounts of data -- most of which remains unannotated. This paper develops a self-supervi…

cs.RO2025

Max Entropy Moment Kalman Filter for Polynomial Systems with Arbitrary Noise

Sangli Teng, Harry Zhang, David Jin +4

Designing optimal Bayes filters for nonlinear non-Gaussian systems is a challenging task. The main difficulties are: 1) representing complex beliefs, 2) handling non-Gaussian noise…

cs.LG20241 cited

Outlier-Robust Training of Machine Learning Models

Rajat Talak, Charis Georgiou, Jingnan Shi +1

Robust training of machine learning models in the presence of outliers has garnered attention across various domains. The use of robust losses is a popular approach and is known to…

cs.RO2024

Integrating Vision Systems and STPA for Robust Landing and Take-Off in VTOL Aircraft

Sandeep Banik, Jinrae Kim, Naira Hovakimyan +3

Vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are versatile platforms widely used in applications such as surveillance, search and rescue, and urban air mobi…

cs.CV2024

CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty

Harry Zhang, Luca Carlone

We introduce CUPS, a novel method for learning sequence-to-sequence 3D human shapes and poses from RGB videos with uncertainty quantification. To improve on top of prior work, we d…