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Littérature scientifique sur le sujet « Robust aggregation »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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Articles de revues sur le sujet "Robust aggregation"

Arieli, Itai, Yakov Babichenko, and Rann Smorodinsky. "Robust forecast aggregation." Proceedings of the National Academy of Sciences 115, no. 52 (2018): E12135—E12143. http://dx.doi.org/10.1073/pnas.1813934115.

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Bayesian experts who are exposed to different evidence often make contradictory probabilistic forecasts. An aggregator, ignorant of the underlying model, uses this to calculate his or her own forecast. We use the notions of scoring rules and regret to propose a natural way to evaluate an aggregation scheme. We focus on a binary state space and construct low regret aggregation schemes whenever there are only two experts that either are Blackwell-ordered or receive conditionally independent and identically distributed (i.i.d.) signals. In contrast, if there are many experts with conditionally i.

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Cross, Daniel, Jaime Ramos, Barbara Mellers, Philip E. Tetlock, and David W. Scott. "Robust forecast aggregation: FourierL2Eregression." Journal of Forecasting 37, no. 3 (2017): 259–68. http://dx.doi.org/10.1002/for.2489.

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Allison-Bunnell, Jodi. "Finding Aid Aggregation: Toward a Robust Future." American Archivist 85, no. 2 (2022): 556–86. http://dx.doi.org/10.17723/2327-9702-85.2.556.

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ABSTRACT Over the last twenty-five years, cultural heritage professionals have formed aggregations—of finding aids, digital object metadata, or related forms of description—in order to overcome barriers to creating and presenting structured, consistent, and interoperable description and to enable expanded access. Now most of these aggregators are struggling to update their infrastructure, meet user needs for access to archival collections, and engage with some of the most promising conceptual, technical, and structural advances in the field. In 2018–2019, the “Toward a National Archival Findin

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Pillutla, Krishna, Sham M. Kakade, and Zaid Harchaoui. "Robust Aggregation for Federated Learning." IEEE Transactions on Signal Processing 70 (2022): 1142–54. http://dx.doi.org/10.1109/tsp.2022.3153135.

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Liu, Fei, Shaokang Qi, Shibin Wang, Xu Tian, Liantao Liu, and Xue Zhao. "Robust Trading Decision-Making Model for Demand-Side Resource Aggregators Considering Multi-Objective Cluster Aggregation Optimization." Energies 18, no. 2 (2025): 236. https://doi.org/10.3390/en18020236.

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In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregatio

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Labraoui, Nabila, Mourad Gueroui, Makhlouf Aliouat, and Jonathan Petit. "RAHIM: Robust Adaptive Approach Based on Hierarchical Monitoring Providing Trust Aggregation for Wireless Sensor Networks." JUCS - Journal of Universal Computer Science 17, no. (11) (2011): 1550–71. https://doi.org/10.3217/jucs-017-11-1550.

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In-network data aggregation has a great impact on the energy consumption in large-scale wireless sensor networks. However, the resource constraints and vulnerable deployment environments challenge the application of this technique in terms of security and efficiency. A compromised node may forge arbitrary aggregation value and mislead the base station into trusting a false reading. In this paper, we present RAHIM, a reactive defense to secure data aggregation scheme in cluster-based wireless sensor networks. The proposed scheme is based on a novel application of adaptive hierarchical level of

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Yiming Chen, Yiming Chen, Hongjun Duan Yiming Chen, Dong Wang Hongjun Duan, Yining Liu Dong Wang, and Chin-Chen Chang Yining Liu. "SERDA: Secure Enhanced and Robust Data Aggregation Scheme for Smart Grid." 電腦學刊 35, no. 5 (2024): 073–90. http://dx.doi.org/10.53106/199115992024103505006.

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<p>Data aggregation is considered a viable security and privacy solution for smart grid as it allows to obtain the total electricity consumption within a region without disclosing individual data. However, existing data aggregation schemes give little consideration in their threat models to use cases where devices operate in untrustworthy environments and adversaries have physical system access, which is common in the smart grid. They cannot support authentication and resist physical attacks while maintaining data privacy and supporting fault tolerance for smart meter (SM) failures. Moti

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WU, Zhong-Bo, Chong-Sheng ZHANG, Hong CHEN, and Hang QIN. "Robust Aggregation Algorithm in Sensor Networks." Journal of Software 20, no. 7 (2010): 1885–94. http://dx.doi.org/10.3724/sp.j.1001.2009.03272.

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Du, Lei, Yan Pan, and Xiaonan Luo. "Robust Spectral Clustering via Matrix Aggregation." IEEE Access 6 (2018): 53661–70. http://dx.doi.org/10.1109/access.2018.2871030.

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Guan, Yuyao, Tingting Sun, Jun Ding, and Zhigang Xie. "Robust organic nanoparticles for noninvasive long-term fluorescence imaging." Journal of Materials Chemistry B 7, no. 44 (2019): 6879–89. http://dx.doi.org/10.1039/c9tb01905g.

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Plus de sources

Thèses sur le sujet "Robust aggregation"

Husain, Syed S. "Robust aggregation of local image descriptors for visual search." Thesis, University of Surrey, 2016. http://epubs.surrey.ac.uk/810765/.

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Visual search and recognition underpins numerous applications including management of multimedia content, mobile commerce, surveillance, navigation, robotics and many others. However the task is still challenging predominantly due to the variability of object appearance and ever increasing size of the databases, often exceeding billions of images. The objective of this thesis is to develop a robust, compact and discriminative image representation suitable for tasks of visual search. This thesis contributes to four research areas. First we propose a novel method, named Robust Visual Descriptor

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Maillard, Guillaume. "Hold-out and Aggregated hold-out Aggregated Hold-Out Aggregated hold-out for sparse linear regression with a robust loss function." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM005.

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En statistiques, il est fréquent d'avoir à choisir entre plusieurs estimateurs (sélection d'estimateurs) ou à les combiner. Cela permet notamment d'adapter la complexité d'un modèle statistique en fonction des données (compromis biais-variance). Pour les problèmes de minimisation de risque, une méthode simple et générale, la validation ou hold-out, consiste à consacrer une partie de l'échantillon à l'estimation du risque des estimateurs, dans le but de choisir celui de risque minimal. Cette procédure nécessite de choisir arbitrairement un sous-échantillon "de validation". Afin de réduire l'inf

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Claici, Sebastian. "Aggregation for modular robots in the pivoting cube model." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107376.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 61-66).<br>In this thesis, we present algorithms for self-aggregation and self-reconfiguration of modular robots in the pivoting cube model. First, we provide generic algorithms for aggregation of robots following integrator dynamics in arbitrary dimensional configuration spaces. We describe solutions to the problem under different assumptions on the capabilities of the robots, and the config

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Kittner, Madeleine [Verfasser], and Robert [Akademischer Betreuer] Seckler. "Folding and aggregation of amyloid peptides / Madeleine Kittner. Betreuer: Robert Seckler." Potsdam : Universitätsbibliothek der Universität Potsdam, 2011. http://d-nb.info/1015259812/34.

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Kittner, Madeleine Verfasser], and Robert [Akademischer Betreuer] [Seckler. "Folding and aggregation of amyloid peptides / Madeleine Kittner. Betreuer: Robert Seckler." Potsdam : Universitätsbibliothek der Universität Potsdam, 2011. http://d-nb.info/1015259812/34.

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Gaillard, Pierre. "Contributions à l’agrégation séquentielle robuste d’experts : Travaux sur l’erreur d’approximation et la prévision en loi. Applications à la prévision pour les marchés de l’énergie." Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112133/document.

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Nous nous intéressons à prévoir séquentiellement une suite arbitraire d'observations. À chaque instant, des experts nous proposent des prévisions de la prochaine observation. Nous formons alors notre prévision en mélangeant celles des experts. C'est le cadre de l'agrégation séquentielle d'experts. L'objectif est d'assurer un faible regret cumulé. En d'autres mots, nous souhaitons que notre perte cumulée ne dépasse pas trop celle du meilleur expert sur le long terme. Nous cherchons des garanties très robustes~: aucune hypothèse stochastique sur la suite d'observations à prévoir n'est faite. Cel

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Shirizadeh, Ghezeljeh Behrang. "Reaching carbon neutrality in France by 2050 : optimal choice of energy sources, carriers and storage options." Electronic Thesis or Diss., Paris, EHESS, 2021. http://www.theses.fr/2021EHES0013.

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Pour contribuer à l’objectif de contenir le réchauffement climatique à 1,5°C, le gouvernement français a adopté l'objectif de zéro émission nette de gaz à effet de serre d'ici 2050. Le principal gaz à effet de serre étant le dioxyde de carbone, et la plupart des émissions de CO2 étant dues à la combustion d'énergies fossiles, cette thèse porte sur l'atteinte de la neutralité carbone des émissions françaises de CO2 liées à l'énergie d'ici 2050. Cette thèse vise à étudier le rôle relatif des différentes options bas-carbone dans le secteur de l'énergie pour atteindre la neutralité carbone. Plus p

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Heimfarth, Leif Erec [Verfasser], Oliver [Akademischer Betreuer] Mußhoff, Robert [Akademischer Betreuer] Finger, and Gunnar [Akademischer Betreuer] Breustedt. "Theory and application of weather index-based insurance in agriculture : to pitfalls of aggregation biases and the insurability of farmers in the North China Plain / Leif Erec Heimfarth. Gutachter: Oliver Mußhoff ; Robert Finger ; Gunnar Breustedt. Betreuer: Oliver Mußhoff." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1043717579/34.

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Heimfarth, Leif Erec Verfasser], Oliver [Akademischer Betreuer] [Mußhoff, Robert [Akademischer Betreuer] Finger, and Gunnar [Akademischer Betreuer] Breustedt. "Theory and application of weather index-based insurance in agriculture : to pitfalls of aggregation biases and the insurability of farmers in the North China Plain / Leif Erec Heimfarth. Gutachter: Oliver Mußhoff ; Robert Finger ; Gunnar Breustedt. Betreuer: Oliver Mußhoff." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://nbn-resolving.de/urn:nbn:de:gbv:7-webdoc-3728-3.

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Cambier, Nicolas. "Bio-inspired collective exploration and cultural organisation." Thesis, Compiègne, 2019. http://www.theses.fr/2019COMP2511.

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Les systèmes autonomes sont récemment devenus une solution efficace pour des applications telles que l'exploration et la surveillance d'environnements. Dans ces situations, l'utilisation de plusieurs robots pourrait améliorer l'efficacité des solutions proposées, bien que cela nécessite des stratégies d'organisation qui soient à la fois robuste, flexible et adaptables à la taille de la flotte de robots. En robotique en essaim, ces qualités sont assurées par la décentralisation, la redondance (plusieurs/tous les robots effectuent la même tâche), des interactions locales et des règles simples. L

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Livres sur le sujet "Robust aggregation"

WECAFC Ad Hoc Working Group on the Development of Sustainable Moored Fish Aggregating Device Fishing in the Lesser Antilles. Meeting. Report of the Second Meeting of the WECAFC Ad Hoc Working Group on the Development of Sustainable Moored Fish Aggregating Device Fishing in the Lesser Antilles: Le Robert, Martinique, 8-11 October 2004. Food and Agriculture Organization of the United Nations, 2007.

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Western Central Atlantic Fishery Commission. Ad Hoc Working Group on the Development of Sustainable Moored Fish Aggregating Device Fishing in the Lesser Antilles. Meeting. Report of the First Meeting of the WECAFC Ad Hoc Working Group on the Development of Sustainable Moored Fish Aggregating Device Fishing in the Lesser Antilles: Le Robert, Martinique, 8-11 October 2001. Food and Agriculture Organization of the United Nations, 2002.

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Meeting, Western Central Atlantic Fishery Commission Ad Hoc Working Group on the Development of Sustainable Moored Fish Aggregating Device Fishing in the Lesser Antilles. National reports and technical papers presented at the First Meeting of the WECAFC Ad Hoc Working Group on the Development of Sustainable Moored Fish Aggregating Device Fishing in the Lesser Antilles: Le Robert, Martinique, 8-11 October 2001. Food and Agriculture Organization of the United Nations, 2002.

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Shockley, Kenneth. Individual and Contributory Responsibility for Environmental Harm. Edited by Stephen M. Gardiner and Allen Thompson. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199941339.013.24.

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Many environmental problems are the result of the aggregation of seemingly innocuous individual actions. As a result, recognizing the moral significance of our contributory, indirect role in the generation of collective harms is crucial in environmental contexts. This chapter argues that taking our contributory role seriously provides a means of accepting a robust form of responsibility for collective harms. Our responsibilities include not only our individual actions, what we have done directly as individuals, but also the influence we might have on the wide range of institutions and practice

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Chapitres de livres sur le sujet "Robust aggregation"

Ovchinnikov, Sergei. "On Robust Aggregation Procedures." In Aggregation and Fusion of Imperfect Information. Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-7908-1889-5_1.

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Weinmann, Alexander. "Control Using Aggregation Models." In Uncertain Models and Robust Control. Springer Vienna, 1991. http://dx.doi.org/10.1007/978-3-7091-6711-3_34.

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Ayiad, Mosab M., and Giuseppe Di Fatta. "Robust Epidemic Aggregation Under Churn." In Internet and Distributed Computing Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97795-9_16.

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Taheri, Rahim, Farzad Arabikhan, Alexander Gegov, and Negar Akbari. "Robust Aggregation Function in Federated Learning." In Advances in Information Systems, Artificial Intelligence and Knowledge Management. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51664-1_12.

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Mi, Yuxi, Yutong Mu, Shuigeng Zhou, and Jihong Guan. "FedMDR: Federated Model Distillation with Robust Aggregation." In Web and Big Data. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85899-5_2.

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Bärmann, Andreas. "Computational Assessment of Approximate Robust Optimization." In Solving Network Design Problems via Decomposition, Aggregation and Approximation. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13913-1_13.

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Álvarez-Esteban, Pedro C., Luis A. García-Escudero, and Agustín Mayo-Iscar. "Aggregation of Random Initializations for Robust Linear Clustering." In Advances in Intelligent Systems and Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65993-5_1.

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Shen, Shihe, Huachen Gao, Wangze Xu, et al. "Disentangled Generation and Aggregation for Robust Radiance Fields." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72967-6_13.

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Bärmann, Andreas. "Polyhedral Approximation of Second-Order Cone Robust Counterparts." In Solving Network Design Problems via Decomposition, Aggregation and Approximation. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-13913-1_12.

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Sun, Fan, Hong Wen, Wenjing Hou, et al. "Malicious Node Detection and Robust Federated Aggregation Algorithm Based on TD3." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2409-6_37.

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Abstract Malicious client nodes in federated learning can corrupt the global model of central server by modifying dataset or submitted model, making it less accurate or distorted. A TD3-based malicious node detection and robust federated aggregation algorithm is proposed. The algorithm excludes malicious clients based on model distance, and uses reinforcement learning to assign model weight to client nodes to reduce the influence of malicious nodes gradually. The experimental results show that the proposed algorithm is effective against data and model modification attacks and is superior to tr

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Actes de conférences sur le sujet "Robust aggregation"

Kabbaj, Hibatallah, Rachid El-Azouzi, and Abdellatif Kobbane. "Robust Federated Learning via Weighted Median Aggregation*." In 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA). IEEE, 2024. https://doi.org/10.1109/flta63145.2024.10839757.

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Choudhary, Sarthak, Aashish Kolluri, and Prateek Saxena. "Attacking Byzantine Robust Aggregation in High Dimensions." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00217.

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Chen, Dongmei, Yu Xiao, and Jun Wu. "Are Rank Aggregation Methods Robust to Collusive Disturbance?" In 2024 World Conference on Complex Systems (WCCS). IEEE, 2024. https://doi.org/10.1109/wccs62745.2024.10765583.

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Li, Wenrui, Hanlin Gu, Sheng Wan, et al. "Byzantine Robust Aggregation in Federated Distillation with Adversaries." In 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2024. http://dx.doi.org/10.1109/icdcs60910.2024.00086.

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Li, Youpeng, Xinda Wang, Fuxun Yu, Lichao Sun, Wenbin Zhang, and Xuyu Wang. "FedCAP: Robust Federated Learning via Customized Aggregation and Personalization." In 2024 Annual Computer Security Applications Conference (ACSAC). IEEE, 2024. https://doi.org/10.1109/acsac63791.2024.00067.

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Chen, Juanjuan, and Laicheng Cao. "Personalized Federated Learning Privacy with Adaptive Distillation and Robust Aggregation." In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025. https://doi.org/10.1109/iscait64916.2025.11010751.

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Hu, Chenyu, Mingyue Zhang, Nianyu Li, et al. "Adapting Aggregation Rule for Robust Federated Learning under Dynamic Attacks." In 2025 IEEE/ACM 20th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE, 2025. https://doi.org/10.1109/seams66627.2025.00026.

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Wu, Huiyu, and Diego Klabjan. "Robust Softmax Aggregation on Blockchain based Federated Learning with Convergence Guarantee." In 2024 IEEE International Conference on Omni-layer Intelligent Systems (COINS). IEEE, 2024. http://dx.doi.org/10.1109/coins61597.2024.10622138.

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gou, li, ying guo, donglin peng, and jiqin feng. "High robust signal processing method based on sensor pulse energy compression aggregation." In Eleventh International Symposium on Precision Mechanical Measurements, edited by Liandong Yu, Lianqing Zhu, Zai Luo, and Haojie Xia. SPIE, 2024. http://dx.doi.org/10.1117/12.3032868.

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Guo, Yongkang, Jason D. Hartline, Zhihuan Huang, Yuqing Kong, Anant Shah, and Fang-Yi Yu. "Algorithmic Robust Forecast Aggregation." In EC '25: 26th ACM Conference on Economics and Computation. ACM, 2025. https://doi.org/10.1145/3736252.3742674.

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Rapports d'organisations sur le sujet "Robust aggregation"

Cucuringu, Mihai. Sync-rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and SDP Synchronization. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada625017.

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Ampatzidis, Yiannis, Mahendra Bhandari, Andres Ferreyra, et al. AI in Agriculture: Opportunities, Challenges, and Recommendations. Chair Alex Thomasson. Council for Agricultural Science and Technology, 2025. https://doi.org/10.62300/iaag042514.

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Artificial Intelligence (AI) is rapidly being integrated into people’s lives, reshaping industries, and enabling previously unimagined innovation, even in agriculture. Generative AI focuses on creating content like text and pictures based on vast quantities of data. ExtensionBot is a generative AI platform that supports agricultural extension by providing farmers with accurate scientific information and specific recommendations. It has been shown to deliver more accurate responses to agricultural questions than broader generative AI models. Other forms of AI have been used to analyze data to p

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