Relevant bibliographies by topics / Descente en mirroir

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Descente en mirroir'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Descente en mirroir"

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Duchi, John C., Alekh Agarwal, Mikael Johansson, and Michael I. Jordan. "Ergodic Mirror Descent." SIAM Journal on Optimization 22, no. 4 (2012): 1549–78. http://dx.doi.org/10.1137/110836043.

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Lei, Yunwen, and Ding-Xuan Zhou. "Convergence of online mirror descent." Applied and Computational Harmonic Analysis 48, no. 1 (2020): 343–73. http://dx.doi.org/10.1016/j.acha.2018.05.005.

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Chen, Po-An, and Chi-Jen Lu. "Generalized mirror descents in congestion games." Artificial Intelligence 241 (December 2016): 217–43. http://dx.doi.org/10.1016/j.artint.2016.09.002.

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Lei, Yunwen, and Ding-Xuan Zhou. "Analysis of Online Composite Mirror Descent Algorithm." Neural Computation 29, no. 3 (2017): 825–60. http://dx.doi.org/10.1162/neco_a_00930.

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We study the convergence of the online composite mirror descent algorithm, which involves a mirror map to reflect the geometry of the data and a convex objective function consisting of a loss and a regularizer possibly inducing sparsity. Our error analysis provides convergence rates in terms of properties of the strongly convex differentiable mirror map and the objective function. For a class of objective functions with Hölder continuous gradients, the convergence rates of the excess (regularized) risk under polynomially decaying step sizes have the order [Formula: see text] after [Formula: se
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Miyashita, Megumi, Shiro Yano, and Toshiyuki Kondo. "Mirror descent search and its acceleration." Robotics and Autonomous Systems 106 (August 2018): 107–16. http://dx.doi.org/10.1016/j.robot.2018.04.009.

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Raskutti, Garvesh, and Sayan Mukherjee. "The Information Geometry of Mirror Descent." IEEE Transactions on Information Theory 61, no. 3 (2015): 1451–57. http://dx.doi.org/10.1109/tit.2015.2388583.

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Yu, Yue, and Behcet Acikmese. "RLC Circuits-Based Distributed Mirror Descent Method." IEEE Control Systems Letters 4, no. 3 (2020): 548–53. http://dx.doi.org/10.1109/lcsys.2020.2972908.

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Hanzely, Filip, and Peter Richtárik. "Fastest rates for stochastic mirror descent methods." Computational Optimization and Applications 79, no. 3 (2021): 717–66. http://dx.doi.org/10.1007/s10589-021-00284-5.

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Wei, Xiaohan, Hao Yu, and Michael J. Neely. "Online Primal-Dual Mirror Descent under Stochastic Constraints." ACM SIGMETRICS Performance Evaluation Review 48, no. 1 (2020): 3–4. http://dx.doi.org/10.1145/3410048.3410051.

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Wei, Xiaohan, Hao Yu, and Michael J. Neely. "Online Primal-Dual Mirror Descent under Stochastic Constraints." Proceedings of the ACM on Measurement and Analysis of Computing Systems 4, no. 2 (2020): 1–36. http://dx.doi.org/10.1145/3392157.

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Dissertations / Theses on the topic "Descente en mirroir"

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Babichev, Dmitry. "On efficient methods for high-dimensional statistical estimation." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE032.

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Dans cette thèse, nous examinons plusieurs aspects de l'estimation des paramètres pour les statistiques et les techniques d'apprentissage automatique, aussi que les méthodes d'optimisation applicables à ces problèmes. Le but de l'estimation des paramètres est de trouver les paramètres cachés inconnus qui régissent les données, par exemple les paramètres dont la densité de probabilité est inconnue. La construction d'estimateurs par le biais de problèmes d'optimisation n'est qu'une partie du problème, trouver la valeur optimale du paramètre est souvent un problème d'optimisation qui doit être ré
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Hadikhanloo, Saeed. "Apprentissage dans les jeux à champ moyen." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED001/document.

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Les jeux à champ moyen (MFG) sont une classe de jeux différentiels dans lequel chaque agent est infinitésimal et interagit avec une énorme population d'agents. Dans cette thèse, nous soulevons la question de la formation effective de l'équilibre MFG. En effet, le jeu étant très complexe, il est irréaliste de supposer que les agents peuvent réellement calculer la configuration d'équilibre. Cela semble indiquer que si la configuration d'équilibre se présente, c'est parce que les agents ont appris à jouer au jeu. Donc, la question principale est de trouver des procédures d'apprentissage dans les
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Silveti, Falls Antonio. "First-order noneuclidean splitting methods for large-scale optimization : deterministic and stochastic algorithms." Thesis, Normandie, 2021. http://www.theses.fr/2021NORMC204.

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Dans ce travail, nous développons et examinons deux nouveaux algorithmes d'éclatement du premier ordre pour résoudre des problèmes d'optimisation composites à grande échelle dans des espaces à dimensions infinies. Ces problèmes sont au coeur de nombres de domaines scientifiques et d'ingénierie, en particulier la science des données et l'imagerie. Notre travail est axé sur l'assouplissement des hypothèses de régularité de Lipschitz généralement requises par les algorithmes de fractionnement du premier ordre en remplaçant l'énergie euclidienne par une divergence de Bregman. Ces développements pe
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Flammarion, Nicolas. "Stochastic approximation and least-squares regression, with applications to machine learning." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE056/document.

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De multiples problèmes en apprentissage automatique consistent à minimiser une fonction lisse sur un espace euclidien. Pour l’apprentissage supervisé, cela inclut les régressions par moindres carrés et logistique. Si les problèmes de petite taille sont résolus efficacement avec de nombreux algorithmes d’optimisation, les problèmes de grande échelle nécessitent en revanche des méthodes du premier ordre issues de la descente de gradient. Dans ce manuscrit, nous considérons le cas particulier de la perte quadratique. Dans une première partie, nous nous proposons de la minimiser grâce à un oracle
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Bhadra, Sahely. "Learning Robust Support Vector Machine Classifiers With Uncertain Observations." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2475.

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The central theme of the thesis is to study linear and non linear SVM formulations in the presence of uncertain observations. The main contribution of this thesis is to derive robust classfiers from partial knowledge of the underlying uncertainty. In the case of linear classification, a new bounding scheme based on Bernstein inequality has been proposed, which models interval-valued uncertainty in a less conservative fashion and hence is expected to generalize better than the existing methods. Next, potential of partial information such as bounds on second order moments along with support inf
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Books on the topic "Descente en mirroir"

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Dinello, Dan. Children of Men. Liverpool University Press, 2019. http://dx.doi.org/10.3828/liverpool/9781999334024.001.0001.

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A mirror of tomorrow, Alfonso Cuarón's visionary Children of Men (2006) was released to good reviews and a poor box office but is now regarded by many as a twenty-first-century masterpiece. Its propulsive story dramatizes a dystopian future when an infertile humanity hurtles toward extinction and an African refugee holds the key to its survival. Cuarón creates a documentary of the near future when Britain's totalitarian government hunts down and cages refugees like animals as the world descends into violent chaos. In the midst of xenophobia and power abuses that have led to a permanent state o
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Book chapters on the topic "Descente en mirroir"

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J. Zaslavski, Alexander. "The Mirror Descent Algorithm." In Convex Optimization with Computational Errors. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37822-6_3.

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Zaslavski, Alexander J. "The Mirror Descent Algorithm." In Springer Optimization and Its Applications. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30921-7_3.

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Jain, Prateek, and Abhradeep Thakurta. "Mirror Descent Based Database Privacy." In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32512-0_49.

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Luong, Duy V. N., Panos Parpas, Daniel Rueckert, and Berç Rustem. "Solving MRF Minimization by Mirror Descent." In Advances in Visual Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33179-4_56.

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Raskutti, Garvesh, and Sayan Mukherjee. "The Information Geometry of Mirror Descent." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25040-3_39.

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Titov, Alexander A., Fedor S. Stonyakin, Alexander V. Gasnikov, and Mohammad S. Alkousa. "Mirror Descent and Constrained Online Optimization Problems." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10934-9_5.

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Kunapuli, Gautam, and Jude Shavlik. "Mirror Descent for Metric Learning: A Unified Approach." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33460-3_60.

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Baes, Michel, Timm Oertel, Christian Wagner, and Robert Weismantel. "Mirror-Descent Methods in Mixed-Integer Convex Optimization." In Facets of Combinatorial Optimization. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38189-8_5.

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Nazin, Alexander. "Algorithms of Inertial Mirror Descent in Stochastic Convex Optimization Problems." In Analytical and Computational Methods in Probability Theory. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71504-9_31.

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Cheng, Lingyun, Sadhana Sundaresh, Mohamed-Rafik Bouguelia, and Onur Dikmen. "Interactive Anomaly Detection Based on Clustering and Online Mirror Descent." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66770-2_13.

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Conference papers on the topic "Descente en mirroir"

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Duchi, John C., Alekh Agarwal, Mikael Johansson, and Michael I. Jordan. "Ergodic mirror descent." In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2011. http://dx.doi.org/10.1109/allerton.2011.6120236.

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Zhou, Zhengyuan, Panayotis Mertikopoulos, Aris L. Moustakas, Nicholas Bambos, and Peter Glynn. "Mirror descent learning in continuous games." In 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8264532.

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Bayandina, Anastasia. "Adaptive mirror descent for constrained optimization." In 2017 Constructive Nonsmooth Analysis and Related Topics (dedicated to the memory of V.F. Demyanov) (CNSA). IEEE, 2017. http://dx.doi.org/10.1109/cnsa.2017.7973937.

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Ding, Dongsheng, Xiaohan Wei, and Mihailo R. Jovanovic. "Distributed robust statistical learning: Byzantine mirror descent." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029491.

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Gao, Bolin, and Lacra Pavel. "Discounted Mirror Descent Dynamics in Concave Games." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9029722.

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Bayandina, Anastasia. "Adaptive stochastic mirror descent for constrained optimization." In 2017 Constructive Nonsmooth Analysis and Related Topics (dedicated to the memory of V.F. Demyanov) (CNSA). IEEE, 2017. http://dx.doi.org/10.1109/cnsa.2017.7973938.

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Si Salem, Tareq, Giovanni Neglia, and Stratis Ioannidis. "No-Regret Caching via Online Mirror Descent." In ICC 2021 - IEEE International Conference on Communications. IEEE, 2021. http://dx.doi.org/10.1109/icc42927.2021.9500487.

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Rabbat, Michael. "Multi-agent mirror descent for decentralized stochastic optimization." In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2015. http://dx.doi.org/10.1109/camsap.2015.7383850.

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Wei, Xiaohan, Hao Yu, and Michael J. Neely. "Online Primal-Dual Mirror Descent under Stochastic Constraints." In SIGMETRICS '20: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. ACM, 2020. http://dx.doi.org/10.1145/3393691.3394209.

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Krichene, Walid, Syrine Krichene, and Alexandre Bayen. "Convergence of mirror descent dynamics in the routing game." In 2015 European Control Conference (ECC). IEEE, 2015. http://dx.doi.org/10.1109/ecc.2015.7330604.

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Journal articles
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