Reducing FPGA algorithm area by avoiding redundant computation. - Brian Axelrod; Michel Laverne (ICRA, 2015)

Hardness of 3D Motion Planning Under Obstacle Uncertainty. - Luke Shimanuki; Brian Axelrod (WAFR, 2018)

An Efficient Algorithm for High-Dimensional Log-Concave Maximum Likelihood. - Brian Axelrod; Gregory Valiant (CoRR, 2018)

Provably safe robot navigation with obstacle uncertainty. - Brian Axelrod; Leslie Pack Kaelbling; Tomás Lozano-Pérez (Int. J. Robotics Res., 2018)

A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families. - Brian Axelrod; Ilias Diakonikolas; Alistair Stewart; Anastasios Sidiropoulos; Gregory Valiant (NeurIPS, 2019)

Sample Amplification: Increasing Dataset Size even when Learning is Impossible. - Brian Axelrod; Shivam Garg 001; Vatsal Sharan; Gregory Valiant (ICML, 2020)

Causal Strategic Linear Regression. - Yonadav Shavit; Benjamin L. Edelman; Brian Axelrod (ICML, 2020)

Near-optimal Approximate Discrete and Continuous Submodular Function Minimization. - Brian Axelrod; Yang P. Liu; Aaron Sidford (SODA, 2020)

Learning From Strategic Agents: Accuracy, Improvement, and Causality. - Yonadav Shavit; Benjamin L. Edelman; Brian Axelrod (CoRR, 2020)

Hardness of Motion Planning with Obstacle Uncertainty in Two Dimensions. - Luke Shimanuki; Brian Axelrod (Int. J. Robotics Res., 2021)

Efficient Motion Planning Under Obstacle Uncertainty with Local Dependencies. - Brian Axelrod; Luke Shimanuki (WAFR, 2022)

On the Statistical Complexity of Sample Amplification. - Brian Axelrod; Shivam Garg 001; Yanjun Han; Vatsal Sharan; Gregory Valiant (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 001; Jeremy J. Heit (CoRR, 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 001; Greg Zaharchuk; Benjamin F. J. Verhaaren; Abdelkader Mahammedi; Soren Christensen; Maarten G. Lansberg (CoRR, 2022)

Sidestepping hardness in statistical problems. - Brian Axelrod (2022)

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)

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 001; et al. (Medical Image Anal., 2023)

LieRE: Generalizing Rotary Position Encodings. - Sophie Ostmeier; Brian Axelrod; Michael E. Moseley; Akshay Chaudhari; Curtis P. Langlotz (CoRR, 2024)