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2020 – today
- 2024
[i10]Emi Zeger, Yifei Wang, Aaron Mishkin, Tolga Ergen, Emmanuel J. Candès, Mert Pilanci:
A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features. CoRR abs/2403.01046 (2024)- [i9]Aaron Mishkin, Alberto Bietti, Robert M. Gower:
Level Set Teleportation: An Optimization Perspective. CoRR abs/2403.03362 (2024) - [i8]Aaron Mishkin, Ahmed Khaled, Yuanhao Wang, Aaron Defazio, Robert M. Gower:
Directional Smoothness and Gradient Methods: Convergence and Adaptivity. CoRR abs/2403.04081 (2024) - [i7]Aaron Mishkin, Mert Pilanci, Mark Schmidt:
Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation. CoRR abs/2404.02378 (2024) - 2023
- [c4]Aaron Mishkin, Mert Pilanci:
Optimal Sets and Solution Paths of ReLU Networks. ICML 2023: 24888-24924 - [i6]Aaron Mishkin, Mert Pilanci:
Optimal Sets and Solution Paths of ReLU Networks. CoRR abs/2306.00119 (2023) - [i5]Amrutha Varshini Ramesh, Aaron Mishkin, Mark Schmidt, Yihan Zhou, Jonathan Wilder Lavington, Jennifer She:
Analyzing and Improving Greedy 2-Coordinate Updates for Equality-Constrained Optimization via Steepest Descent in the 1-Norm. CoRR abs/2307.01169 (2023) - 2022
- [c3]Aaron Mishkin, Arda Sahiner, Mert Pilanci:
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions. ICML 2022: 15770-15816 - [i4]Aaron Mishkin, Arda Sahiner, Mert Pilanci:
Fast Convex Optimization for Two-Layer ReLU Networks: Equivalent Model Classes and Cone Decompositions. CoRR abs/2202.01331 (2022) - 2020
- [i3]Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux:
To Each Optimizer a Norm, To Each Norm its Generalization. CoRR abs/2006.06821 (2020)
2010 – 2019
- 2019
- [c2]Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien:
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. NeurIPS 2019: 3727-3740 - [i2]Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien:
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. CoRR abs/1905.09997 (2019) - 2018
- [c1]Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan:
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. NeurIPS 2018: 6248-6258 - [i1]Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan:
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient. CoRR abs/1811.04504 (2018)
Coauthor Index
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