I am a Research Scientist at Facebook AI (FAIR) and study machine learning and optimization. I completed a PhD with Prof. Zico Kolter at CMU on optimization-based modeling components for learning and control.
Interests
Machine learning, optimization, control, reinforcement learning, meta-learning, structured prediction, and energy-based modeling
Latest Publications
ICML - July 18, 2021
CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints
Anselm Paulus, Michal Rolinek, Vít Musil, Brandon Amos, Georg Martius
L4DC - June 7, 2021
On the model-based stochastic value gradient for continuous reinforcement learning
Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson
ICLR - May 3, 2021
Neural Spatio-Temporal Point Processes
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
AISTATS - April 13, 2021
Aligning Time Series on Incomparable Spaces
Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth
ICLR - March 3, 2021
Learning Neural Event Functions for Ordinary Differential Equations
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel
Learning Meets Combinatorial Optimization Workshop at NeurIPS - December 12, 2020
Fit The Right NP-Hard Problem: End-to-end Learning of Integer Programming Constraints
Anselm Paulus, Michal Rolinek, Vít Musil, Brandon Amos, Georg Martius
Differential Geometry for ML Workshop at NeurIPS - December 11, 2020
Deep Riemannian Manifold Learning
Aaron Lou, Maximilian Nickel, Brandon Amos
Learning for Dynamics & Control (L4DC) - June 10, 2020
Objective Mismatch in Model-based Reinforcement Learning
Nathan Lambert, Brandon Amos, Omry Yadan, Roberto Calandra
NeurIPS - December 8, 2019
Differentiable Convex Optimization Layers
Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, J. Zico Kolter