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1
When Can Nonconvex Optimization Problems Be Solved with Gradient Descent? A Few Case Studies
When Can Nonconvex Optimization Problems Be Solved with Gradient Descent? A Few Case Studies
Gilboa, Dar. | Columbia University | 2020
impresión
2
On Extensions of Iterative Fourier Phase Retrieval Algorithms.
On Extensions of Iterative Fourier Phase Retrieval Algorithms.
Wang, Yaotian.Electrical and Computer Engineering. | Princeton University. | 2022
impresión
3
Dealing with Intransitivity, Non-Convexity, and Algorithmic Bias in Preference Learning.
Dealing with Intransitivity, Non-Convexity, and Algorithmic Bias in Preference Learning.
Bower, Amanda.Applied and Interdisciplinary Mathem | University of Michigan. | 2020
impresión
4
Provable and Efficient Algorithms for Federated, Batch and Reinforcement Learning.
Provable and Efficient Algorithms for Federated, Batch and Reinforcement Learning.
Ghosh, Avishek.Electrical Engineering & Computer S | University of California, Berkeley. | 2021
impresión
5
Deconvolution Problems for Structured Sparse Signal.
Deconvolution Problems for Structured Sparse Signal.
Kuo, Han-wen.Electrical Engineering. | Columbia University. | 2021
impresión
6
Distributed Learning in Non-Convex World: Theory, Algorithms, and Applications.
Distributed Learning in Non-Convex World: Theory, Algorithms, and Applications.
Sun, Haoran.Electrical Engineering. | University of Minnesota. | 2021
impresión
7
Towards Efficient and Effective Privacy-Preserving Machine Learning.
Towards Efficient and Effective Privacy-Preserving Machine Learning.
Wang, Lingxiao.Computer Science 0201. | University of California, Los Angeles. | 2021
impresión
8
Stochastic and Online Learning with Applications to Machine Learning and Statistics.
Stochastic and Online Learning with Applications to Machine Learning and Statistics.
Chen, You-Lin.Statistics. | The University of Chicago. | 2021
impresión
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