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Specific topics of interest for the workshop include (but are not limited to) foundational and translational AI activities related to: The workshop will be a one day meeting comprising invited talks from researchers in the field, spotlight lightning talks and a poster session where contributing paper presenters can discuss their work. Integration of AI-based approaches with engineering prototyping and manufacturing. Consequently, standard notions of software quality and reliability such as deterministic functional correctness, black box testing, code coverage, and traditional software debugging become practically irrelevant for ML systems. We expect ~60 attendees. All questions about submissions should be emailed to nurendra@vt.edu, AmazonKDDCup2022: KDD Cup 2022 Workshop: ESCI Challenge for Improving Product Search, Washington DC, DC, United States, August 17, 2022, https://easychair.org/conferences/?conf=amazonkddcup2022, https://www.acm.org/publications/proceedings-template. Junxiang Wang, Hongyi Li, Liang Zhao. We especially welcome research from fields including but not limited to AI, human-computer interaction, human-robot interaction, cognitive science, human factors, and philosophy. Scott E. Fahlman, School of Computer Science, Carnegie Mellon University (sef@cs.cmu.edu), Edouard Oyallon, Sorbonne Universit LIP6 (Edouard.oyallon@lip6.fr), Dean Alderucci, School of Computer Science, Carnegie Mellon University, (dalderuc@cs.cmu.edu). However, the performance and efficiency of these techniques are big challenges for performing real-time applications. 4498-4505, New Orleans, US, Feb 2018. Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu, Hemant Purohit, Xiang Chen, Kai Zeng and Liang Zhao. However, the use of rich data sets also raises significant privacy concerns: They often reveal personal sensitive information that can be exploited, without the knowledge and/or consent of the involved individuals, for various purposes including monitoring, discrimination, and illegal activities. Submitted technical papers can be up to 4 pages long (excluding references and appendices). Hence, AI methods are required to understand and protect the cyber domain. DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. Accepted papers will be published in the workshop proceedings. We invite submissions of full papers, as well as works-in-progress, position papers, and papers describing open problems and challenges. Previously published work (or under-review) is acceptable. OARS-KDD2022: KDD 2022 Workshop on Online and Adaptive Recommender Systems Washington DC, DC, United States, August 15, 2022 Topics: data science artificial intelligence recommender system recommendation KDD 2022 Workshop on Online and Adaptive Recommender Systems (OARS) Call For Papers ================== "A Topic-focused Trust Model for Twitter." Xiaosheng Li, Jessica Lin, Liang Zhao. . In Proceedings of the IEEE International Conference on Big Data (BigData 2014), pp. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. This 1-day workshop will include a mixture of invited speakers, panels (including discussion with the audience), and presentations from authors of accepted submissions. Attendance is open to all; at least one author of each accepted paper must be virtually present at the workshop. The 39th IEEE International Conference on Data Engineering (ICDE 2023), accepted. Natural language reasoning and inference. Please keep your paper format according to AAAI Formatting Instructions (two-column format). Track 2 focuses on the state of the art advances in the computational jobs marketplace. Despite rapid recent progress, it has proven to be challenging for Artificial Intelligence (AI) algorithms to be integrated into real-world applications such as autonomous vehicles, industrial robotics, and healthcare. Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, and Chang-Tien Lu. Yuanqi Du, Xiaojie Guo, Yinkai Wang, Amarda Shehu, Liang Zhao. Roco Mercado, Massachusetts Institute of Technology. We will also select some of the best posters for spotlight talks (2 minutes each). All submissions must be original contributions and will be peer reviewed, single-blinded. Modeling Health Stage Development of Patients with Dynamic Attributed Graphs in Online Health Communities. Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. Comparison or integration of self-supervised learning methods and other semi-supervised and transfer learning methods in speech and audio processing tasks. Checklist for Revising a SIGKDD Data Mining Paper, How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering, https://researcher.watson.ibm.com/researcher/view_group.php?id=144, IEEE International Conference on Big Data (, AAAI Conference on Artificial Intelligence (, IEEE International Conference on Data Engineering (, SIAM International Conference on Data Mining (, Pacific-Asia Conference on Knowledge Discovery and Data Mining (, ACM SIGKDD International Conference on Knowledge discovery and data mining (, European Conference on Machine learning and knowledge discovery in databases (, ACM International Conference on Information and Knowledge Management (, IEEE International Conference on Data Mining (, ACM International Conference on Web Search and Data Mining (, 18.4% (181/983, research track), 22.5% (112/497, applied data science track), 59.1% (107/181, research track), 35.7% (40/112, applied data science track), 17.4% (130/748, research track), 22.0% (86/390, applied data science track), 49.2% (64/130, research track), 41.9% (36/86, applied data science track), 18.1% (142/784, research track), 19.9% (66/331, applied data science track), 49.3% (70/142, research track), 60.1% (40/66, applied data science track), 18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper), 19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper), 19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper), 19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper), 19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research), 17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper), Short papers are presented at poster sessions, 20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper), 23% (160/701, long paper), 24% (55/234, short paper), 54 extended short papers (6 pages), 26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track), 27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular), 28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular), 29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular), 40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^, 16.3% (84/514 in which 3 papers are withdrawn/rejected after the acceptance), 28.4% (23/81, long presentation), 71.6% (58/81, short presentation)^, 30% (24/80, long presentation), 70% (56/80, short presentation)^, 29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^, 53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^. Universit de MontralOffice of Admissions and RecruitmentC. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. Taking the pulse of COVID-19: a spatiotemporal perspective. SDU will be a one-day workshop. "Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design", 28th International Conference on Field Programmable Logic and Applications (FPL 2019), (acceptance rate: 18%), Barcelona, Spain, accepted. Integration of Deep Learning and Relational Learning. Counter-intuitive behaviors of ML models will largely affect the public trust on AI techniques, while a revolution of machine learning/deep learning methods may be an urgent need. ", ACM Transactions on Spatial Algorithms and Systems (TSAS), (Acceptance Rate: 11%), Volume 2 Issue 4, Acticle No. The PAKDD is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery. 2085-2094, Aug 2016. ), Learning with algebraic or combinatorial structure, Link analysis/prediction, node classification, graph classification, clustering for complex graph structures, Theoretical analysis of graph algorithms or models, Optimization methods for graphs/manifolds, Probabilistic and graphical models for structured data, Unsupervised graph/manifold embedding methods. There will be live Q&A sessions at the end of each talk and oral presentation. Submissions are limited to a total of 5 pages for initial submission (up to 6 pages for final camera-ready submission), excluding references or supplementary materials, and authors should only rely on the supplementary material to include minor details that do not fit in the 5 pages. First, large data sources, both conventionally used in social sciences (EHRs, health claims, credit card use, college attendance records) and unconventional (social networks, fitness apps), are now available, and are increasingly used to personalize interventions. iDetective: An Intelligent System for Automatic Identification of Key Actors in Online Hack Forums. Spatio-temporal Event Forecasting Using Incremental Multi-source Feature Learning. Researchers from related fields are invited to submit papers on the recent advances, resources, tools, and upcoming challenges for SDU. In recent years, various information theoretic principles have also been applied to different deep learning related AI applications in fruitful and unorthodox ways. Papers will be peer-reviewed and selected for spotlight and/or poster presentation. The submission website ishttps://cmt3.research.microsoft.com/TAIH2022. "Multi-Task Learning for Spatio-Temporal Event Forecasting." 41-50, New Orleans, US, Dec 2017. Submissions will be assessed based on their novelty, technical quality, significance of impact, interest, clarity, relevance, and reproducibility. Some good examples include recommender systems, clustering, graph mining, Submission URL:https://easychair.org/conferences/?conf=rl4edaaai22. There were two workshops on similar topics hosted at ICML 2020 and NeurIPS 2020, and both workshops observed positive feedback and overwhelming participation. Other submissions will be evaluated by a committee based on their novelty and insights. Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena et al. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities. Thank you for all your contributions, our, Paper submission deadline is now extended to. [materials]. Notable examples include the information bottleneck (IB) approach on the explanation of the generalization behavior of DNNs and the information maximization principle in visual representation learning. Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. The eligibility criteria for attending the workshop will be registration in the conference/workshop as per AAAI norms. Merge remote-tracking branch 'origin/master', 2. Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. A challenge is how to integrate people into the learning loop in a way that is transparent, efficient, and beneficial to the human-AI team as a whole, supporting different requirements and users with different levels of expertise. Algorithms and theories for trustworthy AI models. The following paper categories are welcome: Submission site:https://sites.google.com/view/eaai-ws-2022/call, Silvia Tulli (Dept. 2022. The design and implementation of these AI techniques to meet financial business operations require a joint effort between academia researchers and industry practitioners. We invite the submission of original and high-quality research papers in the topics related to biased or scarce data. These abrupt changes impacted the environmental assumptions used by AI/ML systems and their corresponding input data patterns. Highlights: Government day with NSF, NIH, DARPA, NIST, and IARPA Local industries in the DC Metro Area, including the Amazon's second headquarter New initiatives at KDD 2022: undergraduate research and poster session Early career research day with postdoctoral scholars and assistant professors in a mentoring workshop and panel Workshops and hands-on tutorials on emerging topics Topics include, but our not limited to: learning optimization models from data, constraint and objective learning, AutoAI, especially if combined with decision optimization models or environments, AutoRL, incorporating the inaccuracy of the automatically learnt models in the decision making process, and using machine learning to efficiently solve combinatorial optimization models. NOTE: May 19: Notification. A primary reason for this is the inherent long-tailed nature of our world, and the need for algorithms to be trained with large amounts of data that includes as many rare events as possible. "Spatiotemporal Event Forecasting from Incomplete Hyper-local Price Data" The 26th ACM International Conference on Information and Knowledge Management (CIKM 2017) , (acceptance rate: 21%), pp.