Aaron Mishkin - RankMe

19 papers A* 6A 1Journal 12

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality. ICLR Sungyoon Kim, Aaron Mishkin, Mert Pilanci
2025	J	`jnl`	Glocal Smoothness: Line Search can really help! CoRR Curtis Fox, Aaron Mishkin, Sharan Vaswani, Mark Schmidt
2025	A	`conf`	Level Set Teleportation: An Optimization Perspective. AISTATS Aaron Mishkin, Alberto Bietti, Robert M. Gower
2024	J	`jnl`	A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features. CoRR Emi Zeger, Yifei Wang, Aaron Mishkin, Tolga Ergen, Emmanuel J. Candès, Mert Pilanci
2024	A*	`conf`	Directional Smoothness and Gradient Methods: Convergence and Adaptivity. NeurIPS Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower
2024	J	`jnl`	Directional Smoothness and Gradient Methods: Convergence and Adaptivity. CoRR Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower
2024	J	`jnl`	Exploring the loss landscape of regularized neural networks via convex duality. CoRR Sungyoon Kim, Aaron Mishkin, Mert Pilanci
2024	J	`jnl`	Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation. CoRR Aaron Mishkin, Mert Pilanci, Mark Schmidt
2024	J	`jnl`	Level Set Teleportation: An Optimization Perspective. CoRR Aaron Mishkin, Alberto Bietti, Robert M. Gower
2023	J	`jnl`	Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm. CoRR Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt, Yihan Zhou, Jonathan Wilder Lavington, Jennifer She
2023	A*	`conf`	Optimal Sets and Solution Paths of ReLU Networks. ICML Aaron Mishkin, Mert Pilanci
2023	J	`jnl`	Optimal Sets and Solution Paths of ReLU Networks. CoRR Aaron Mishkin, Mert Pilanci
2022	A*	`conf`	Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions. ICML Aaron Mishkin, Arda Sahiner, Mert Pilanci
2022	J	`jnl`	Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions. CoRR Aaron Mishkin, Arda Sahiner, Mert Pilanci
2020	J	`jnl`	To Each Optimizer a Norm, To Each Norm its Generalization. CoRR Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux
2019	A*	`conf`	Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. NeurIPS Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
2019	J	`jnl`	Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. CoRR Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
2018	A*	`conf`	SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. NeurIPS Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
2018	J	`jnl`	SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. CoRR Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan