Aaron Mishkin

According to our database, Aaron Mishkin authored at least 13 papers between 2018 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Glocal Smoothness: Line search and adaptive sizes can help in theory too!

Curtis Fox, Aaron Mishkin, Sharan Vaswani, Mark Schmidt

Trans. Mach. Learn. Res., 2026

2025

Glocal Smoothness: Line Search can really help!

Curtis Fox, Aaron Mishkin, Sharan Vaswani, Mark Schmidt

CoRR, June, 2025

Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality.

Sungyoon Kim, Aaron Mishkin, Mert Pilanci

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Level Set Teleportation: An Optimization Perspective.

Aaron Mishkin, Alberto Bietti, Robert M. Gower

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation.

Aaron Mishkin, Mert Pilanci, Mark Schmidt

CoRR, 2024

A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features.

Emi Zeger, Yifei Wang, Aaron Mishkin, Tolga Ergen, Emmanuel J. Candès, Mert Pilanci

CoRR, 2024

Directional Smoothness and Gradient Methods: Convergence and Adaptivity.

Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm.

Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt, Yihan Zhou, Jonathan Wilder Lavington, Jennifer She

CoRR, 2023

Optimal Sets and Solution Paths of ReLU Networks.

Aaron Mishkin, Mert Pilanci

Proceedings of the International Conference on Machine Learning, 2023

2022

Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions.

Aaron Mishkin, Arda Sahiner, Mert Pilanci

Proceedings of the International Conference on Machine Learning, 2022

2020

To Each Optimizer a Norm, To Each Norm its Generalization.

Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux

CoRR, 2020

2019

Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates.

Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient.

Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018