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Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectures.
“Dynamic energy management”, 2019
N. Moehle, E. Busseti, S. Boyd and M. Wytock · 1903
Earlier work this paper cites.
“Least squares auto-tuning”, 2019
Shane Barratt and Stephen Boyd · 1904
Earlier work this paper cites.
“The limited multi-label projection layer”, 2019
Brandon Amos, Vladlen Koltun and J Kolter · 1906
Earlier work this paper cites.
“Deep declarative networks: A new hope”, 2019
Stephen Gould, Richard Hartley and Dylan Campbell · 1909
Earlier work this paper cites.
“Fitting a kalman smoother to data”, 2019
Shane Barratt and Stephen Boyd · 1910
Earlier work this paper cites.
“Coercing machine learning to output physically accurate results”, 2019
Zhenglin Geng, Dan Johnson and Ronald Fedkiw · 1910
Earlier work this paper cites.
“Portfolio selection”
H. Markowitz · 1952
Earlier work this paper cites.
“When is a linear control system optimal?”
Rudolf Kalman · 1964
Earlier work this paper cites.
“Nonlinear programming: Sequential unconstrained minimization techniques”
A. Fiacco and G. McCormick · 1968
Earlier work this paper cites.
“Strongly regular generalized equations”
S. Robinson · 1980
Earlier work this paper cites.
“LSQR: An algorithm for sparse linear equations and sparse least squares”
Christopher Paige and Michael Saunders · 1982
Earlier work this paper cites.
“Introduction to sensitivity and stability analysis in nonlinear programming” 165
Anthony. Fiacco · 1983
Earlier work this paper cites.
“On the limited memory BFGS method for large scale optimization”
Dong Liu and Jorge Nocedal · 1989
Earlier work this paper cites.
“Linear matrix inequalities in system and control theory”
S. Boyd, L. El Ghaoui, E. Feron and V. Balakrishnan · 1994
Earlier work this paper cites.
“An O ( n L ) O(\sqrt{nL}) -iteration homogeneous and self-dual linear programming algorithm”
Yinyu Ye, Michael Todd and Shinji Mizuno · 1994
Earlier work this paper cites.
“Computational geometry algorithms and applications”
Marc Van, Otfried Schwarzkopf, Mark de Berg and Mark Overmars · 2000
Earlier work this paper cites.
“Optimal design of a CMOS op-amp via geometric programming”
Maria Hershenson, Stephen Boyd and Thomas Lee · 2001
Earlier work this paper cites.
“A robust optimization approach to supply chain management”
D. Bertsimas and A. Thiele · 2004
Earlier work this paper cites.
“Convex Optimization”
Stephen Boyd and Lieven Vandenberghe · 2004
Earlier work this paper cites.
“Retailer-supplier flexible commitments contracts: A robust optimization approach”
A. Ben-Tal, B. Golany, A. Nemirovski and J.-P. Vial · 2005
Earlier work this paper cites.
“Dynamic programming and optimal control”
Dimitri Bertsekas · 2005
Earlier work this paper cites.
“Digital circuit optimization via geometric programming”
Stephen Boyd, Seung-Jean Kim, Dinesh Patil and Mark Horowitz · 2005
Earlier work this paper cites.
“Disciplined convex programming”
Michael Grant, Stephen Boyd and Yinyu Ye · 2006
Earlier work this paper cites.
“Graph implementations for nonsmooth convex programs”
M. Grant and S. Boyd · 2008
Earlier work this paper cites.
“Evaluating derivatives: principles and techniques of algorithmic differentiation”
Andreas Griewank and Andrea Walther · 2008
Earlier work this paper cites.
“Implicit functions and solution mappings”
Asen Dontchev and R Rockafellar · 2009
Earlier work this paper cites.
“Fast evaluation of quadratic control-Lyapunov policy”
Yang Wang and Stephen Boyd · 2010
Cited alongside, same era.
“Nonsmooth Mechanics and Convex Optimization”
Yoshihiro Kanno · 2011
Cited alongside, same era.
“Empirical risk minimization of graphical model parameters given approximate inference, decoding, and model structure”
Veselin Stoyanov, Alexander Ropson and Jason Eisner · 2011
Cited alongside, same era.
“Generic methods for optimization-based modeling.”
Justin Domke · 2012
Cited alongside, same era.
“CVXGEN: A code generator for embedded convex optimization”
Jacob Mattingley and Stephen Boyd · 2012
Cited alongside, same era.
“Optimal sensitivity based on IPOPT”
Hans Pirnay, Rodrigo L\’opez-Negrete and Lorenz Biegler · 2012
Cited alongside, same era.
“Task-based end-to-end model learning in stochastic optimization”
Priya Donti, Brandon Amos and J Kolter · 2017
Later among the works it cites.
“Model-agnostic meta-learning for fast adaptation of deep networks”
Chelsea Finn, Pieter Abbeel and Sergey Levine · 2017
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“CVXR: An R package for disciplined convex optimization”
Anqi Fu, Balasubramanian Narasimhan and Stephen Boyd · 2017
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“Learning what’s easy: Fully differentiable neural easy-first taggers”
Andr\’e Martins and Julia Kreutzer · 2017
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“SCS: Splitting conic solver, version 2.1.0”, https://github.com/cvxgrp/scs , 2017
Brendan O’Donoghue, Eric Chu, Neal Parikh and Stephen Boyd · 2017
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“Automatic differentiation in PyTorch”
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“MuJoCo: A physics engine for model-based control”
Emanuel Todorov, Tom Erez and Yuval Tassa · 2012
Cited alongside, same era.
“Training energy-based models for time-series imputation.”
Phil\’emon Brakel, Dirk Stroobandt and Benjamin Schrauwen · 2013
Cited alongside, same era.
“Code generation for embedded second-order cone programming”
Eric Chu, Neal Parikh, Alexander Domahidi and Stephen Boyd · 2013
Cited alongside, same era.
“ECOS: An SOCP solver for embedded systems”
Alexander Domahidi, Eric Chu and Stephen Boyd · 2013
Cited alongside, same era.
“Multi-prediction deep Boltzmann machines”
Ian Goodfellow, Mehdi Mirza, Aaron Courville and Yoshua Bengio · 2013
Cited alongside, same era.
“SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives”
Aaron Defazio, Francis Bach and Simon Lacoste-Julien · 2014
Cited alongside, same era.
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga and Adam Lerer · 2017
Later among the works it cites.
“OSQP: An operator splitting solver for quadratic programs”, 2017
B. Stellato, G. Banjac, P. Goulart, A. Bemporad and S. Boyd · 2017
Later among the works it cites.
“A rewriting system for convex optimization problems”
Akshay Agrawal, Robin Verschueren, Steven Diamond and Stephen Boyd · 2018
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“Differentiable MPC for end-to-end planning and control”
Brandon Amos, Ivan Jimenez, Jacob Sacks, Byron Boots and J. Kolter · 2018
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“On the differentiability of the solution to convex optimization problems”, 2018
Shane Barratt · 2018
Later among the works it cites.
“Stochastic control with affine dynamics and extended quadratic costs”, 2018
Shane Barratt and Stephen Boyd · 2018
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“Wild patterns: Ten years after the rise of adversarial machine learning”
Battista Biggio and Fabio Roli · 2018
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“Solution refinement at regular points of conic problems”, 2018
Enzo Busseti, Walaa Moursi and Stephen Boyd · 2018
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“End-to-end differentiable physics for learning and control”
Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum and J. Kolter · 2018
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“Manipulating machine learning: Poisoning attacks and countermeasures for regression learning”
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru and Bo Li · 2018
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“What game are we playing? End-to-end learning in normal and extensive form games”, 2018
Chun Ling, Fei Fang and J. Kolter · 2018
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“Sparse and constrained attention for neural machine translation”, 2018
Chaitanya Malaviya, Pedro Ferreira and Andr\’e Martins · 2018
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“Neural proximal gradient descent for compressive imaging”, 2018
Morteza Mardani, Qingyun Sun, Shreyas Vasawanala, Vardan Papyan, Hatef Monajemi, John Pauly and David Donoho · 2018
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Bryan Wilder, Bistra Dilkina and Milind Tambe · 2018
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“Differentiating through a cone program”
Akshay Agrawal, Shane Barratt, Stephen Boyd, Enzo Busseti and Walaa Moursi · 2019
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“TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning”
Akshay Agrawal, Akshay Modi, Alexandre Passos, Allen Lavoie, Ashish Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat Monga and Shanqing Cai · 2019
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“Differentiable optimization-based modeling for machine learning”, 2019
Brandon Amos · 2019
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“Computational Bounds for Photonic Design”
Guillermo Angeris, Jelena Vuckovi\’c and Stephen Boyd · 2019
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“MOSEK optimization suite”, http://docs.mosek.com/9.0/intro.pdf , 2019
MOSEK ApS · 2019
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“Meta-learning with differentiable convex optimization”
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran and Stefano Soatto · 2019
Closest in time.