Articles with public access mandates - Satyen KaleLearn more
Available somewhere: 15
Online learning of quantum states
S Aaronson, X Chen, E Hazan, S Kale, A Nayak
Advances in Neural Information Processing Systems, 8962-8972, 2018
Mandates: US National Science Foundation, US Department of Defense, Natural Sciences …
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
Mandates: US National Science Foundation, US Department of Defense
Logistic regression: The importance of being improper
DJ Foster, S Kale, H Luo, M Mohri, K Sridharan
Conference On Learning Theory, 167-208, 2018
Mandates: US National Science Foundation, US Department of Defense
A combinatorial, primal-dual approach to semidefinite programs
S Arora, S Kale
Journal of the ACM (JACM) 63 (2), 1-35, 2016
Mandates: US National Science Foundation
Parameter-free online learning via model selection
DJ Foster, S Kale, M Mohri, K Sridharan
Advances in Neural Information Processing Systems, 6020-6030, 2017
Mandates: US Department of Defense
Sgd: The role of implicit regularization, batch-size and multiple-epochs
A Sekhari, K Sridharan, S Kale
Advances In Neural Information Processing Systems 34, 27422-27433, 2021
Mandates: US National Science Foundation
Hypothesis Set Stability and Generalization
DJ Foster, S Greenberg, S Kale, H Luo, M Mohri, K Sridharan
Advances in Neural Information Processing Systems, 6729-6739, 2019
Mandates: US National Science Foundation
Loss decomposition for fast learning in large output spaces
IEH Yen, S Kale, F Yu, D Holtmann-Rice, S Kumar, P Ravikumar
International Conference on Machine Learning, 5640-5649, 2018
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation, US Department of Defense
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
Mandates: US National Science Foundation, US Department of Defense
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
Mandates: US National Science Foundation, US Department of Defense, National Natural …
Federated functional gradient boosting
Z Shen, H Hassani, S Kale, A Karbasi
International Conference on Artificial Intelligence and Statistics, 7814-7840, 2022
Mandates: US National Science Foundation, US Department of Defense
Agnostic learnability of halfspaces via logistic loss
Z Ji, K Ahn, P Awasthi, S Kale, S Karp
International Conference on Machine Learning, 10068-10103, 2022
Mandates: US National Science Foundation
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
Mandates: US National Science Foundation, US Department of Defense
Private Matrix Approximation and Geometry of Unitary Orbits
O Mangoubi, Y Wu, S Kale, A Thakurta, NK Vishnoi
Conference on Learning Theory, 3547-3588, 2022
Mandates: US National Science Foundation
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