Search Results for author: Brandon Amos

Found 22 papers, 19 papers with code

Neural Fixed-Point Acceleration for Convex Optimization

1 code implementation21 Jul 2021 Shobha Venkataraman, Brandon Amos

Fixed-point iterations are at the heart of numerical computing and are often a computational bottleneck in real-time applications that typically need a fast solution of moderate accuracy.


Riemannian Convex Potential Maps

1 code implementation18 Jun 2021 samuel cohen, Brandon Amos, Yaron Lipman

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e. g., in physics and geology.

CombOptNet: Fit the Right NP-Hard Problem by Learning Integer Programming Constraints

1 code implementation5 May 2021 Anselm Paulus, Michal Rolínek, Vít Musil, Brandon Amos, Georg Martius

Bridging logical and algorithmic reasoning with modern machine learning techniques is a fundamental challenge with potentially transformative impact.

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

2 code implementations20 Apr 2021 Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra

MBRL-Lib is designed as a platform for both researchers, to easily develop, debug and compare new algorithms, and non-expert user, to lower the entry-bar of deploying state-of-the-art algorithms.

Model-based Reinforcement Learning

Neural Potts Model

no code implementations1 Jan 2021 Tom Sercu, Robert Verkuil, Joshua Meier, Brandon Amos, Zeming Lin, Caroline Chen, Jason Liu, Yann Lecun, Alexander Rives

We propose the Neural Potts Model objective as an amortized optimization problem.

Neural Spatio-Temporal Point Processes

1 code implementation ICLR 2021 Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel

We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space.

Epidemiology Point Processes

On the model-based stochastic value gradient for continuous reinforcement learning

1 code implementation28 Aug 2020 Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson

For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents.

Continuous Control Model-based Reinforcement Learning +1

Aligning Time Series on Incomparable Spaces

1 code implementation22 Jun 2020 Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth

Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in comparable spaces.

Dynamic Time Warping Imitation Learning +1

Objective Mismatch in Model-based Reinforcement Learning

1 code implementation ICLR 2020 Nathan Lambert, Brandon Amos, Omry Yadan, Roberto Calandra

In our experiments, we study this objective mismatch issue and demonstrate that the likelihood of one-step ahead predictions is not always correlated with control performance.

Model-based Reinforcement Learning

Differentiable Convex Optimization Layers

1 code implementation NeurIPS 2019 Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, Zico Kolter

In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization.

Generalized Inner Loop Meta-Learning

3 code implementations3 Oct 2019 Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala

Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem.


The Differentiable Cross-Entropy Method

1 code implementation ICML 2020 Brandon Amos, Denis Yarats

We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function's parameters.

Continuous Control Structured Prediction

The Limited Multi-Label Projection Layer

1 code implementation20 Jun 2019 Brandon Amos, Vladlen Koltun, J. Zico Kolter

We propose the Limited Multi-Label (LML) projection layer as a new primitive operation for end-to-end learning systems.

Classification General Classification +2

Differentiable MPC for End-to-end Planning and Control

2 code implementations NeurIPS 2018 Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, J. Zico Kolter

We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces.

Imitation Learning

Depth-Limited Solving for Imperfect-Information Games

no code implementations NeurIPS 2018 Noam Brown, Tuomas Sandholm, Brandon Amos

This paper introduces a principled way to conduct depth-limited solving in imperfect-information games by allowing the opponent to choose among a number of strategies for the remainder of the game at the depth limit.

Learning Awareness Models

no code implementations ICLR 2018 Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world.

Task-based End-to-end Model Learning in Stochastic Optimization

1 code implementation NeurIPS 2017 Priya L. Donti, Brandon Amos, J. Zico Kolter

With the increasing popularity of machine learning techniques, it has become common to see prediction algorithms operating within some larger process.

Stochastic Optimization

OptNet: Differentiable Optimization as a Layer in Neural Networks

6 code implementations ICML 2017 Brandon Amos, J. Zico Kolter

This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks.

bilevel optimization

Input Convex Neural Networks

3 code implementations ICML 2017 Brandon Amos, Lei Xu, J. Zico Kolter

We show that many existing neural network architectures can be made input-convex with a minor modification, and develop specialized optimization algorithms tailored to this setting.

Imputation Structured Prediction

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