Articles with public access mandates - Satyen Kale - US Department of DefenseLearn more
NoteFor this mandate, articles should be available from specific locations.
Available based on mandate: 9
Online learning of quantum states
S Aaronson, X Chen, E Hazan, S Kale, A Nayak
Advances in Neural Information Processing Systems, 8962-8972, 2018
Escaping saddle points with adaptive gradient methods
M Staib, S Reddi, S Kale, S Kumar, S Sra
International Conference on Machine Learning, 5956-5965, 2019
Logistic regression: The importance of being improper
DJ Foster, S Kale, H Luo, M Mohri, K Sridharan
Conference On Learning Theory, 167-208, 2018
Parameter-free online learning via model selection
DJ Foster, S Kale, M Mohri, K Sridharan
Advances in Neural Information Processing Systems, 6020-6030, 2017
On the convergence of federated averaging with cyclic client participation
YJ Cho, P Sharma, G Joshi, Z Xu, S Kale, T Zhang
International Conference on Machine Learning, 5677-5721, 2023
Reproducibility in optimization: Theoretical framework and limits
K Ahn, P Jain, Z Ji, S Kale, P Netrapalli, GI Shamir
Advances in Neural Information Processing Systems 35, 18022-18033, 2022
Self-Consistency of the Fokker Planck Equation
Z Shen, Z Wang, S Kale, A Ribeiro, A Karbasi, H Hassani
Conference on Learning Theory, 817-841, 2022
Federated functional gradient boosting
Z Shen, H Hassani, S Kale, A Karbasi
International Conference on Artificial Intelligence and Statistics, 7814-7840, 2022
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
CM De Sa, S Kale, JD Lee, A Sekhari, K Sridharan
Advances in Neural Information Processing Systems 35, 30963-30976, 2022
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