Search Results for author: Rachel Luo

Found 6 papers, 1 papers with code

A System-Level View on Out-of-Distribution Data in Robotics

no code implementations28 Dec 2022 Rohan Sinha, Apoorva Sharma, Somrita Banerjee, Thomas Lew, Rachel Luo, Spencer M. Richards, Yixiao Sun, Edward Schmerling, Marco Pavone

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 autonomy stack.

Online Distribution Shift Detection via Recency Prediction

no code implementations17 Nov 2022 Rachel Luo, Rohan Sinha, Yixiao Sun, Ali Hindy, Shengjia Zhao, Silvio Savarese, Edward Schmerling, Marco Pavone

When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical.

Sample-Efficient Safety Assurances using Conformal Prediction

no code implementations28 Sep 2021 Rachel Luo, Shengjia Zhao, Jonathan Kuck, Boris Ivanovic, Silvio Savarese, Edward Schmerling, Marco Pavone

When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial.

Conformal Prediction Robotic Grasping

Local Calibration: Metrics and Recalibration

no code implementations22 Feb 2021 Rachel Luo, Aadyot Bhatnagar, Yu Bai, Shengjia Zhao, Huan Wang, Caiming Xiong, Silvio Savarese, Stefano Ermon, Edward Schmerling, Marco Pavone

In this work, we propose the local calibration error (LCE) to span the gap between average and individual reliability.

Decision Making Fairness

Privacy Preserving Recalibration under Domain Shift

no code implementations21 Aug 2020 Rachel Luo, Shengjia Zhao, Jiaming Song, Jonathan Kuck, Stefano Ermon, Silvio Savarese

In an extensive empirical study, we find that our algorithm improves calibration on domain-shift benchmarks under the constraints of differential privacy.

Privacy Preserving

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