Brian Axelrod

According to our database, Brian Axelrod authored at least 17 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

USE-Evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging.

Sophie Ostmeier, Brian Axelrod, Fabian Isensee, Jeroen Bertels, Michael Mlynash, Soren Christensen, Maarten G. Lansberg, Gregory W. Albers, Rajen Sheth, Benjamin F. J. Verhaaren, Abdelkader Mahammedi, Li-Jia Li, Greg Zaharchuk, Jeremy J. Heit

Medical Image Anal., December, 2023

Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT.

Sophie Ostmeier, Brian Axelrod, Benjamin Pulli, Benjamin F. J. Verhaaren, Abdelkader Mahammedi, Yongkai Liu, Christian Federau, Greg Zaharchuk, Jeremy J. Heit

CoRR, 2023

2022

Sidestepping hardness in statistical problems.

Brian Axelrod

PhD thesis, 2022

Non-inferiority of Deep Learning Model to Segment Acute Stroke on Non-contrast CT Compared to Neuroradiologists.

Sophie Ostmeier, Jeremy J. Heit, Brian Axelrod, Li-Jia Li, Greg Zaharchuk, Benjamin F. J. Verhaaren, Abdelkader Mahammedi, Soren Christensen, Maarten G. Lansberg

CoRR, 2022

Evaluation of Medical Image Segmentation Models for Uncertain, Small or Empty Reference Annotations.

Sophie Ostmeier, Brian Axelrod, Jeroen Bertels, Fabian Isensee, Maarten G. Lansberg, Soren Christensen, Gregory W. Albers, Li-Jia Li, Jeremy J. Heit

CoRR, 2022

On the Statistical Complexity of Sample Amplification.

Brian Axelrod, Shivam Garg, Yanjun Han, Vatsal Sharan, Gregory Valiant

CoRR, 2022

Efficient Motion Planning Under Obstacle Uncertainty with Local Dependencies.

Brian Axelrod, Luke Shimanuki

Proceedings of the Algorithmic Foundations of Robotics XV, 2022

2021

Hardness of Motion Planning with Obstacle Uncertainty in Two Dimensions.

Luke Shimanuki, Brian Axelrod

Int. J. Robotics Res., 2021

2020

Learning From Strategic Agents: Accuracy, Improvement, and Causality.

Yonadav Shavit, Benjamin L. Edelman, Brian Axelrod

CoRR, 2020

Near-optimal Approximate Discrete and Continuous Submodular Function Minimization.

Brian Axelrod, Yang P. Liu, Aaron Sidford

Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Causal Strategic Linear Regression.

Yonadav Shavit, Benjamin L. Edelman, Brian Axelrod

Proceedings of the 37th International Conference on Machine Learning, 2020

Sample Amplification: Increasing Dataset Size even when Learning is Impossible.

Brian Axelrod, Shivam Garg, Vatsal Sharan, Gregory Valiant

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families.

Brian Axelrod, Ilias Diakonikolas, Alistair Stewart, Anastasios Sidiropoulos, Gregory Valiant

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Provably safe robot navigation with obstacle uncertainty.

Brian Axelrod, Leslie Pack Kaelbling, Tomás Lozano-Pérez

Int. J. Robotics Res., 2018

An Efficient Algorithm for High-Dimensional Log-Concave Maximum Likelihood.

Brian Axelrod, Gregory Valiant

CoRR, 2018

Hardness of 3D Motion Planning Under Obstacle Uncertainty.

Luke Shimanuki, Brian Axelrod

Proceedings of the Algorithmic Foundations of Robotics XIII, 2018

2015

Reducing FPGA algorithm area by avoiding redundant computation.

Brian Axelrod, Michel Laverne

Proceedings of the IEEE International Conference on Robotics and Automation, 2015