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Статті в журналах з теми "Primal-Dual learning algorithm"
Overman, Tom, Garrett Blum, and Diego Klabjan. "A Primal-Dual Algorithm for Hybrid Federated Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (March 24, 2024): 14482–89. http://dx.doi.org/10.1609/aaai.v38i13.29363.
Повний текст джерелаYang, Peng, and Ping Li. "Distributed Primal-Dual Optimization for Online Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6631–38. http://dx.doi.org/10.1609/aaai.v34i04.6139.
Повний текст джерелаWang, Shuai, Yanqing Xu, Zhiguo Wang, Tsung-Hui Chang, Tony Q. S. Quek, and Defeng Sun. "Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 10175–83. http://dx.doi.org/10.1609/aaai.v37i8.26212.
Повний текст джерелаLai, Hanjiang, Yan Pan, Cong Liu, Liang Lin, and Jie Wu. "Sparse Learning-to-Rank via an Efficient Primal-Dual Algorithm." IEEE Transactions on Computers 62, no. 6 (June 2013): 1221–33. http://dx.doi.org/10.1109/tc.2012.62.
Повний текст джерелаTao, Wei, Wei Li, Zhisong Pan, and Qing Tao. "Gradient Descent Averaging and Primal-dual Averaging for Strongly Convex Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9843–50. http://dx.doi.org/10.1609/aaai.v35i11.17183.
Повний текст джерелаDing, Yuhao, and Javad Lavaei. "Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7396–404. http://dx.doi.org/10.1609/aaai.v37i6.25900.
Повний текст джерелаBai, Qinbo, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel, and Vaneet Aggarwal. "Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 3682–89. http://dx.doi.org/10.1609/aaai.v36i4.20281.
Повний текст джерелаBai, Qinbo, Amrit Singh Bedi, and Vaneet Aggarwal. "Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 6737–44. http://dx.doi.org/10.1609/aaai.v37i6.25826.
Повний текст джерелаGupta, Ankita, Lakhwinder Kaur, and Gurmeet Kaur. "Drought stress detection technique for wheat crop using machine learning." PeerJ Computer Science 9 (May 19, 2023): e1268. http://dx.doi.org/10.7717/peerj-cs.1268.
Повний текст джерелаLiu, Bo, Ian Gemp, Mohammad Ghavamzadeh, Ji Liu, Sridhar Mahadevan, and Marek Petrik. "Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity." Journal of Artificial Intelligence Research 63 (November 15, 2018): 461–94. http://dx.doi.org/10.1613/jair.1.11251.
Повний текст джерелаДисертації з теми "Primal-Dual learning algorithm"
Bouvier, Louis. "Apprentissage structuré et optimisation combinatoire : contributions méthodologiques et routage d'inventaire chez Renault." Electronic Thesis or Diss., Marne-la-vallée, ENPC, 2024. http://www.theses.fr/2024ENPC0046.
Повний текст джерелаThis thesis stems from operations research challenges faced by Renault supply chain. Toaddress them, we make methodological contributions to the architecture and training of neural networks with combinatorial optimization (CO) layers. We combine them with new matheuristics to solve Renault’s industrial inventory routing problems.In Part I, we detail applications of neural networks with CO layers in operations research. We notably introduce a methodology to approximate constraints. We also solve some off- policy learning issues that arise when using such layers to encode policies for Markov decision processes with large state and action spaces. While most studies on CO layers rely on supervised learning, we introduce a primal-dual alternating minimization scheme for empirical risk minimization. Our algorithm is deep learning-compatible, scalable to large combinatorial spaces, and generic. In Part II, we consider Renault European packaging return logistics. Our rolling-horizon policy for the operational-level decisions is based on a new large neighborhood search for the deterministic variant of the problem. We demonstrate its efficiency on large-scale industrialinstances, that we release publicly, together with our code and solutions. We combine historical data and experts’ predictions to improve performance. A version of our policy has been used daily in production since March 2023. We also consider the tactical-level route contracting process. The sheer scale of this industrial problem prevents the use of classic stochastic optimization approaches. We introduce a new algorithm based on methodological contributions of Part I for empirical risk minimization
Hendrich, Christopher. "Proximal Splitting Methods in Nonsmooth Convex Optimization." Doctoral thesis, Universitätsbibliothek Chemnitz, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-149548.
Повний текст джерелаТези доповідей конференцій з теми "Primal-Dual learning algorithm"
Qin, Jingsheng, Lingjian Ye, Xinmin Zhang, Feifan Shen, Wei Wang, and Longying Mao. "A Twin Primal-Dual DDPG Algorithm for Safety-Constrained Reinforcement Learning." In 2024 China Automation Congress (CAC), 2400–2405. IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864510.
Повний текст джерелаLee, Donghwan, and Niao He. "Stochastic Primal-Dual Q-Learning Algorithm For Discounted MDPs." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8815275.
Повний текст джерелаLee, Donghwan, Hyungjin Yoon, and Naira Hovakimyan. "Primal-Dual Algorithm for Distributed Reinforcement Learning: Distributed GTD." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619839.
Повний текст джерелаBianchi, Pascal, Walid Hachem, and Iutzeler Franck. "A stochastic coordinate descent primal-dual algorithm and applications." In 2014 IEEE 24th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2014. http://dx.doi.org/10.1109/mlsp.2014.6958866.
Повний текст джерелаWang, Shijun, Baocheng Zhu, Lintao Ma, and Yuan Qi. "A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852367.
Повний текст джерелаQu, Yang, Jinming Ma, and Feng Wu. "Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Control." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/21.
Повний текст джерелаSankaran, Raman, Francis Bach, and Chiranjib Bhattacharyya. "Learning With Subquadratic Regularization : A Primal-Dual Approach." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/272.
Повний текст джерелаWan, Yuanyu, Nan Wei, and Lijun Zhang. "Efficient Adaptive Online Learning via Frequent Directions." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/381.
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