16 citations · 41 across the 7 of their papers we have counts for
4 papers · 1 filter
Diagnostic Runtime Monitoring with Martingales
Ali Hindy, Rachel Luo, Somrita Banerjee +3
Machine learning systems deployed in safety-critical robotics settings must be robust to distribution shifts. However, system designers must understand the cause of a distribution…
A System-Level View on Out-of-Distribution Data in Robotics
Rohan Sinha, Apoorva Sharma, Somrita Banerjee +6
When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot…
Online Distribution Shift Detection via Recency Prediction
Rachel Luo, Rohan Sinha, Yixiao Sun +5
When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical. However, most existing methods for detecting d…
Sample-Efficient Safety Assurances using Conformal Prediction
Rachel Luo, Shengjia Zhao, Jonathan Kuck +4
When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial. Early warning systems can provide alerts when an un…