Brandon Amos

RESEARCH SCIENTIST | NEW YORK CITY, UNITED STATES

Brandon is a research scientist at Facebook AI Research (FAIR), working on machine learning, reinforcement learning, optimization, and control. His current focus is specialized modeling components to help add domain-specific priors. He earned his Ph.D. at Carnegie Mellon University, where he studied optimization-based modeling components for learning and control.

Brandon's Publications

December 11, 2023

REINFORCEMENT LEARNING

CORE MACHINE LEARNING

TaskMet: Task-driven Metric Learning for Model Learning

Dishank Bansal, Ricky Chen, Mustafa Mukadam, Brandon Amos

December 11, 2023

July 20, 2023

CORE MACHINE LEARNING

Tutorial on amortized optimization

Brandon Amos

July 20, 2023

July 20, 2023

CORE MACHINE LEARNING

Meta Optimal Transport

Brandon Amos, Giulia Luise, Ievgen Redko, Samuel Cohen

July 20, 2023

June 21, 2023

REINFORCEMENT LEARNING

CORE MACHINE LEARNING

Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories

Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover

June 21, 2023

October 25, 2022

AR/VR

RESEARCH

Theseus: A Library for Differentiable Nonlinear Optimization

Mustafa Mukadam, Austin Wang, Brandon Amos, Daniel DeTone, Jing Dong, Joe Ortiz, Luis Pineda, Maurizio Monge, Ricky Chen, Shobha Venkataraman, Stuart Anderson, Taosha Fan, Paloma Sodhi

October 25, 2022

June 21, 2022

Matching Normalizing Flows and Probability Paths on Manifolds

Yaron Lipman, Brandon Amos, Maximilian Nickel, Ricky Chen, Samuel Cohen, Aditya Grover, Heli Ben-Hamu, Joey Bose

June 21, 2022

July 09, 2021

CORE MACHINE LEARNING

Neural Fixed-Point Acceleration

Shobha Venkataraman, Brandon Amos

July 09, 2021

April 20, 2021

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning

Luis Pineda, Amy Zhang, Brandon Amos, Roberto Calandra, Nathan Lambert

April 20, 2021

December 12, 2020

CORE MACHINE LEARNING

Fit The Right NP-Hard Problem: End-to-end Learning of Integer Programming Constraints

Brandon Amos, Anselm Paulus, Georg Martius, Michal Rolinek, Vit Musil

December 12, 2020

December 11, 2020

CORE MACHINE LEARNING

Deep Riemannian Manifold Learning

Brandon Amos, Maximilian Nickel, Aaron Lou

December 11, 2020

July 06, 2020

ML APPLICATIONS

The Differentiable Cross-Entropy Method

Brandon Amos, Denis Yarats

July 06, 2020

May 01, 2020

ML APPLICATIONS

Objective Mismatch in Model-based Reinforcement Learning

Roberto Calandra, Brandon Amos, Nathan Owen Lambert, Omry Yadan

May 01, 2020

November 01, 2019

RESEARCH

NLP

Differentiable Convex Optimization Layers

Brandon Amos, Akshay Agrawal, Shane Barratt, Stephen Boyd, Steven Diamond, Zico Kolter

November 01, 2019