activity
20192022
most citedPreference-based Learning of Reward Function Features

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.RO2022

Collision Risk and Operational Impact of Speed Change Advisories as Aircraft Collision Avoidance Maneuvers

Sydney M. Katz, Luis E. Alvarez, Michael Owen +4

Aircraft collision avoidance systems have long been a key factor in keeping our airspace safe. Over the past decade, the FAA has supported the development of a new family of collis…

cs.LG2021

Verification of Image-based Neural Network Controllers Using Generative Models

Sydney M. Katz, Anthony L. Corso, Christopher A. Strong +1

Neural networks are often used to process information from image-based sensors to produce control actions. While they are effective for this task, the complex nature of neural netw…

cs.RO20212 cited

Preference-based Learning of Reward Function Features

Sydney M. Katz, Amir Maleki, Erdem Bıyık +1

Preference-based learning of reward functions, where the reward function is learned using comparison data, has been well studied for complex robotic tasks such as autonomous drivin…

cs.AI2021

Generating Probabilistic Safety Guarantees for Neural Network Controllers

Sydney M. Katz, Kyle D. Julian, Christopher A. Strong +1

Neural networks serve as effective controllers in a variety of complex settings due to their ability to represent expressive policies. The complex nature of neural networks, howeve…

cs.AI2019

Learning an Urban Air Mobility Encounter Model from Expert Preferences

Sydney M. Katz, Anne-Claire Le Bihan, Mykel J. Kochenderfer

Airspace models have played an important role in the development and evaluation of aircraft collision avoidance systems for both manned and unmanned aircraft. As Urban Air Mobility…