SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. - Aaron Mishkin; Frederik Kunstner; Didrik Nielsen; Mark Schmidt 001; Mohammad Emtiyaz Khan (NeurIPS, 2018)

Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. - Sharan Vaswani; Aaron Mishkin; Issam H. Laradji; Mark Schmidt 001; Gauthier Gidel; Simon Lacoste-Julien (NeurIPS, 2019)

To Each Optimizer a Norm, To Each Norm its Generalization. - Sharan Vaswani; Reza Babanezhad 001; Jose Gallego; Aaron Mishkin; Simon Lacoste-Julien; Nicolas Le Roux (CoRR, 2020)

Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions. - Aaron Mishkin; Arda Sahiner; Mert Pilanci (ICML, 2022)

Optimal Sets and Solution Paths of ReLU Networks. - Aaron Mishkin; Mert Pilanci (ICML, 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 001; Yihan Zhou; Jonathan Wilder Lavington; Jennifer She (CoRR, 2023)

Directional Smoothness and Gradient Methods: Convergence and Adaptivity. - Aaron Mishkin; Ahmed Khaled 001; Yuanhao Wang 001; Aaron Defazio; Robert M. Gower (NeurIPS, 2024)

A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features. - Emi Zeger; Yifei Wang 005; Aaron Mishkin; Tolga Ergen; Emmanuel J. Candès; Mert Pilanci (CoRR, 2024)

Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation. - Aaron Mishkin; Mert Pilanci; Mark Schmidt 001 (CoRR, 2024)

Level Set Teleportation: An Optimization Perspective. - Aaron Mishkin; Alberto Bietti; Robert M. Gower (AISTATS, 2025)

Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality. - Sungyoon Kim; Aaron Mishkin; Mert Pilanci (ICLR, 2025)

Glocal Smoothness: Line Search can really help! - Curtis Fox; Aaron Mishkin; Sharan Vaswani; Mark Schmidt 001 (CoRR, 2025)