Howard Heaton

Orcid: 0000-0002-5560-750X

According to our database1, Howard Heaton authored at least 14 papers between 2019 and 2023.

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Bibliography

2023
Faster Predict-and-Optimize with Three-Operator Splitting.
CoRR, 2023

Safeguarded Learned Convex Optimization.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Wasserstein-Based Projections with Applications to Inverse Problems.
SIAM J. Math. Data Sci., 2022

Learning to Optimize: A Primer and A Benchmark.
J. Mach. Learn. Res., 2022

Explainable AI via Learning to Optimize.
CoRR, 2022

JFB: Jacobian-Free Backpropagation for Implicit Networks.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Learning to Optimize with Guarantees.
PhD thesis, 2021

Learn to Predict Equilibria via Fixed Point Networks.
CoRR, 2021

Feasibility-based Fixed Point Networks.
CoRR, 2021

Fixed Point Networks: Implicit Depth Models with Jacobian-Free Backprop.
CoRR, 2021

Learning A Minimax Optimizer: A Pilot Study.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Projecting to Manifolds via Unsupervised Learning.
CoRR, 2020

2019
Derivative-free superiorization with component-wise perturbations.
Numer. Algorithms, 2019

Asynchronous sequential inertial iterations for common fixed points problems with an application to linear systems.
J. Glob. Optim., 2019


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