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Smooth minimization of non-smooth functions
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Citations
Cited by
(2010) · Cited by 8,261
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Chambolle, A., & Pock, T. (2011) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-010-0251-1 · Cited by 4,336
Parikh, N., & Boyd, S. (2014) · Foundations and Trends® in Optimization · doi:10.1561/2400000003 · Cited by 4,102
Fast Distributed Gradient Methods
Jakovetic, D., Xavier, J., & Moura, J. M. F. (2014) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2014.2298712 · Cited by 624
Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning
Cinbis, R. G., Verbeek, J., & Schmid, C. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2535231 · Cited by 420
Multi-Agent Distributed Optimization via Inexact Consensus ADMM
Chang, T.-H., Hong, M., & Wang, X. (2015) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2014.2367458 · Cited by 444
An optimal method for stochastic composite optimization
Lan, G. (2012) · Mathematical Programming · doi:10.1007/s10107-010-0434-y · Cited by 427
High-order social interactions in groups of mice
Shemesh, Y., Sztainberg, Y., Forkosh, O., Shlapobersky, T., Chen, A., & Schneidman, E. (2013) · eLife · doi:10.7554/elife.00759 · Cited by 213
Better Mini-Batch Algorithms via Accelerated Gradient Methods
Cotter, A., Shamir, O., Srebro, N., & Sridharan, K. (2011) · arXiv · doi:10.48550/arxiv.1106.4574 · Cited by 173
Communication Complexity of Distributed Convex Learning and Optimization
Arjevani, Y., & Shamir, O. (2015) · arXiv · doi:10.48550/arxiv.1506.01900 · Cited by 133
Privacy Amplification by Iteration
Feldman, V., Mironov, I., Talwar, K., & Thakurta, A. (2018) · 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) · doi:10.1109/focs.2018.00056 · Cited by 80
Optogenetic Light Sensors in Human Retinal Organoids
Garita-Hernandez, M., Guibbal, L., Toualbi, L., Routet, F., Chaffiol, A., Winckler, C., Harinquet, M., Robert, C., Fouquet, S., Bellow, S., et al. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00789 · Cited by 65
Weakly Supervised Deep Detection Networks
Bilen, H., & Vedaldi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.311 · Cited by 144
“[31] apply Nesterov’s smoothing technique [23] to the latent SVM formulation [10] to be more robust against poor initializations.”
MethodDevice Heterogeneity in Federated Learning: A Superquantile Approach
Laguel, Y., Pillutla, K., Malick, J., & Harchaoui, Z. (2020) · arXiv · doi:10.48550/arxiv.2002.11223 · Cited by 15
Metadata
- Authors
- Nesterov, Y.
- Venue
- Mathematical Programming
- Published
- 2004-12-29
- Volume
- 103
- Issue
- 1
- Pages
- 127-152
- ISSN
- 0025-5610 (ISSN-L)
- 1436-4646
- OpenAlex
- W2167732364
- Cited by
- 2,455
- References
- 0
- FWCI
- 17.54
- Type
- journal article
- Language
- en
- Open access
- closed
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.