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Neural Spatio-Temporal Point Processes

Nov 09, 2020
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel


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Learning Neural Event Functions for Ordinary Differential Equations

Nov 08, 2020
Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel


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

Aug 28, 2020
Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson


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Aligning Time Series on Incomparable Spaces

Jun 22, 2020
Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth


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Objective Mismatch in Model-based Reinforcement Learning

Feb 11, 2020
Nathan Lambert, Brandon Amos, Omry Yadan, Roberto Calandra

* 8 pages, 2 pages references, 5 pages appendices 

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Differentiable Convex Optimization Layers

Oct 28, 2019
Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, Zico Kolter

* In NeurIPS 2019. Code available at https://www.github.com/cvxgrp/cvxpylayers. Authors in alphabetical order 

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Generalized Inner Loop Meta-Learning

Oct 07, 2019
Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala

* 17 pages, 3 figures, 1 algorithm 

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Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Oct 07, 2019
Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus


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The Differentiable Cross-Entropy Method

Sep 27, 2019
Brandon Amos, Denis Yarats


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The Limited Multi-Label Projection Layer

Jun 22, 2019
Brandon Amos, Vladlen Koltun, J. Zico Kolter


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Differentiable MPC for End-to-end Planning and Control

Oct 31, 2018
Brandon Amos, Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots, J. Zico Kolter

* NIPS 2018 

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Depth-Limited Solving for Imperfect-Information Games

May 21, 2018
Noam Brown, Tuomas Sandholm, Brandon Amos


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Learning Awareness Models

Apr 17, 2018
Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

* Accepted to ICLR 2018 

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

Apr 03, 2018
Priya L. Donti, Brandon Amos, J. Zico Kolter

* Donti, P., Amos, B., & Kolter, J. Z. (2017). Task-based End-to-end Model Learning in Stochastic Optimization. In Advances in Neural Information Processing Systems (pp. 5484-5494) 
* In NIPS 2017. Code available at https://github.com/locuslab/e2e-model-learning 

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OptNet: Differentiable Optimization as a Layer in Neural Networks

Jan 12, 2018
Brandon Amos, J. Zico Kolter

* ICML 2017 

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Input Convex Neural Networks

Jun 14, 2017
Brandon Amos, Lei Xu, J. Zico Kolter

* ICML 2017 

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