Daniel P. Robinson
Orcid: 0000-0003-0251-4227
According to our database1,
Daniel P. Robinson
authored at least 68 papers
between 2010 and 2024.
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Bibliography
2024
Fair machine learning through constrained stochastic optimization and an ε-constraint method.
Optim. Lett., December, 2024
Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization.
Math. Program., May, 2024
Sequential Quadratic Optimization for Stochastic Optimization with Deterministic Nonlinear Inequality and Equality Constraints.
SIAM J. Optim., 2024
A proximal-gradient method for problems with overlapping group-sparse regularization: support identification complexity.
Optim. Methods Softw., 2024
A Stochastic Sequential Quadratic Optimization Algorithm for Nonlinear-Equality-Constrained Optimization with Rank-Deficient Jacobians.
Math. Oper. Res., 2024
A Stochastic Inexact Sequential Quadratic Optimization Algorithm for Nonlinear Equality-Constrained Optimization.
INFORMS J. Optim., 2024
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy through Single-Shot Federated Learning.
CoRR, 2024
2023
IEEE Trans. Autom. Control., May, 2023
An Adaptive Half-Space Projection Method for Stochastic Optimization Problems with Group Sparse Regularization.
Trans. Mach. Learn. Res., 2023
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems.
CoRR, 2023
A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
2022
A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer.
SIAM J. Optim., 2022
IEEE Trans. Pattern Anal. Mach. Intell., 2022
2021
IEEE Trans. Inf. Theory, 2021
Trust-Region Newton-CG with Strong Second-Order Complexity Guarantees for Nonconvex Optimization.
SIAM J. Optim., 2021
Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization.
SIAM J. Optim., 2021
Math. Program., 2021
Proceedings of the 38th International Conference on Machine Learning, 2021
Dual Principal Component Pursuit for Robust Subspace Learning: Theory and Algorithms for a Holistic Approach.
Proceedings of the 38th International Conference on Machine Learning, 2021
Dual Principal Component Pursuit for Learning a Union of Hyperplanes: Theory and Algorithms.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021
2020
SIAM J. Optim., 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020
2019
Math. Program., 2019
Basis Pursuit and Orthogonal Matching Pursuit for Subspace-preserving Recovery: Theoretical Analysis.
CoRR, 2019
A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the 36th International Conference on Machine Learning, 2019
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019
2018
Complexity Analysis of a Trust Funnel Algorithm for Equality Constrained Optimization.
SIAM J. Optim., 2018
FaRSA for ℓ1-regularized convex optimization: local convergence and numerical experience.
Optim. Methods Softw., 2018
CoRR, 2018
Sparse Recovery over Graph Incidence Matrices: Polynomial Time Guarantees and Location Dependent Performance.
CoRR, 2018
A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis.
Comput. Optim. Appl., 2018
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
Proceedings of the 35th International Conference on Machine Learning, 2018
Proceedings of the Computer Vision - ECCV 2018, 2018
Proceedings of the 57th IEEE Conference on Decision and Control, 2018
2017
SIAM J. Optim., 2017
A trust region algorithm with a worst-case iteration complexity of O(ϵ <sup>-3/2</sup>) for nonconvex optimization.
Math. Program., 2017
Math. Program., 2017
A dual gradient-projection method for large-scale strictly convex quadratic problems.
Comput. Optim. Appl., 2017
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017
2016
Adaptive augmented Lagrangian methods: algorithms and practical numerical experience.
Optim. Methods Softw., 2016
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016
Proceedings of the 50th Asilomar Conference on Signals, Systems and Computers, 2016
2015
SIAM J. Optim., 2015
SIAM J. Optim., 2015
Math. Program., 2015
J. Optim. Theory Appl., 2015
A globally convergent primal-dual active-set framework for large-scale convex quadratic optimization.
Comput. Optim. Appl., 2015
Proceedings of the 32nd International Conference on Machine Learning, 2015
2014
SIAM J. Optim., 2014
SIAM J. Optim., 2014
2013
Subspace Accelerated Matrix Splitting Algorithms for Asymmetric and Symmetric Linear Complementarity Problems.
SIAM J. Optim., 2013
Trajectory-following methods for large-scale degenerate convex quadratic programming.
Math. Program. Comput., 2013
2012
2010
SIAM J. Optim., 2010
Math. Program. Comput., 2010