Zheng Zhang and Liang Zhao. Participants will be given access to publicly available datasets and will be asked to use tools from AI and ML to generate insight from the data. Submissions are limited to 4 pages, not including references. STGEN: Deep Continuous-time Spatiotemporal Graph Generation. 11-13. The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization. These approaches make it possible to use a tremendous amount of unlabeled data available on the web to train large networks and solve complicated tasks. Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Chang-Tien Lu, Sai Dinakarrao, Houman Homayoun, Liang Zhao. Submissions should follow the AAAI 2022 formatting guidelines and the AAAI 2022 standards for double-blind review including anonymous submission. Submissions must be formatted in the AAAI submission format (https://www.aaai.org/Publications/Templates/AuthorKit22.zip) All submissions should be done electronically via EasyChair. Track 1 covers the issues and algorithms pertinent to general online marketplaces as well as specific problems and applications arising from those diverse domains, such as ridesharing, online retail, food delivery, house rental, real estate, and more. The workshop is being organized by application area or other, panels, invited speakers, interactive, small groups, discussions, presentations. Temporal Domain Generalization with Drift-Aware Dynamic Neural Network. Causality has received significant interest in ML in recent years in part due to its utility for generalization and robustness. We allow papers that are concurrently submitted to or currently under review at other conferences or venues. ; (2) Deep Learning (DL) approaches that can exploit large datasets, particularly Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL); (3) End-to-end learning methodologies that mend the gap between ML model training and downstream optimization problems that use ML predictions as inputs; (4) Datasets and benchmark libraries that enable ML approaches for a particular OR application or challenging combinatorial problems. For example, AI tools are built to ease the workload for teachers. arXiv preprint arXiv:2212.03954 (2022). For research track papers and applied data science track papers. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Factorized Deep Generative Models for End-to-End Trajectory Generation with Spatiotemporal Validity Constraints. 25, 2022: We have announced Call for Nominations: , Mar. Each accepted paper presentation will be allocated between 15 and 20 minutes. While a variety of research has advanced the fundamentals of document understanding, the majority have focused on documents found on the web which fail to capture the complexity of analysis and types of understanding needed across business documents. . In this workshop we would like to focus on a contrasting approach, to learn the architecture during training. Incomplete Label Multi-Task Ordinal Regression for Spatial Event Scale Forecasting. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. Performance characterization of AI algorithms and systems under bias and scarcity. The cookie is used to store the user consent for the cookies in the category "Other. The 19th International Conference on Data Mining (ICDM 2019), short paper, (acceptance rate: 18.05%), Beijing, China, accepted. Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting.Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), (acceptance rate: 16.2%), Hawaii, USA, Feb 2019, accepted. As far as we know, we are the first workshop to focus on practical deep learning in the wild for AI, which is of great significance. Yanfang Ye, Yiming Zhang, Yujie Fan, Chuan Shi and Liang Zhao. Knowledge and Information Systems (KAIS), (impact factor: 2.936), accepted. text, images, and videos). Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. Dialog systems and related technologies, including natural language processing, audio and speech processing, and vision information processing. By registering, you agree to receive emails from UdeM. Modeling Health Stage Development of Patients with Dynamic Attributed Graphs in Online Health Communities. 3, pp. Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. "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. Automatic fact/claim verification has recently become a topic of interest among diverse research communities. The ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2022 (ACM SIGSPATIAL 2022) (Acceptance Rate: 23.8%), full paper track, to appear, 2022. Integration of Deep learning and Constraint programming. The theme of the hack-a-thon will be decided before submission is closed and will be focused around finding creative solutions to novel problems in health. IEEE Transactions on Information Forensics and Security (TIFS), (impact factor: 7.178), accepted. However, the performance and efficiency of these techniques are big challenges for performing real-time applications. Recent years have witnessed growing efforts from the AI research community devoted to advancing our education and promising results have been obtained in solving various critical problems in education. "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." Attendance is expected to be 150-200 participants (estimated), including organizers and speakers. Data mining systems and platforms, and their efficiency, scalability, security and privacy. 1923-1935, 1 Oct. 2020, doi: 10.1109/TKDE.2019.2912187. In addition to the keynote and presentations of accepted works, the workshop will include both a general discussion session on defining and addressing the key challenges in this area , and a lightning tutorial session that will include brief overviews and demos of relevant tools, including open source frameworks such as Ecole. The aim of this workshop is to focus on both original research and review articles on various disciplines of ITS applications, including particularly AI techniques for ITS time-series data analyses, ITS spatio-temporal data analyses, advanced traffic management systems, advanced traveler information systems, commercial vehicle operation systems, advanced vehicle control and safety systems, advanced public transportation services, advanced information management services, etc. The role of adjacent fields of study (e.g, computational social science) in mitigating issues of bias and trust in AI. Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. The study of complex graphs is a highly interdisciplinary field that aims to study complex systems by using mathematical models, physical laws, inference and learning algorithms, etc. This workshop aims to bring together researchers and practitioners working on different facets of these problems, from diverse backgrounds to share challenges, new directions, recent research results, and lessons from applications. Technology has transformed over the last few years, turning from futuristic ideas into todays reality. The first AAAI Workshop on AI for Design and Manufacturing, ADAM, aims to bring together researchers from core AI/ML, design, manufacturing, scientific computing, and geometric modeling. Unsupervised Deep Subgraph Anomaly Detection. Submissions are due by 12 November 2021. Dr. Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond. For questions on submission and the workshop, please send email through the following link: Track 1: Tony Qin (Lyft), Rui Song (NC State & Amazon), Hongtu Zhu (UNC), Michael Jordan (Berkeley), Track 2: Liangjie Hong (LinkedIn), Mohammed Korayem (CareerBuilder), Haiyan Luo (Indeed). KDD 2022 Encore track papers that have been recently published, or accepted for publication in a conference or journal. Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. We are excited to announce our upcoming workshop at KDD 2022 | Washington DC, U.S.: Decision Intelligence and Analytics for Online Marketplaces - Jobs, Ridesharing, Retail, and Beyond. References will not count towards the page limit. This topic also encompasses techniques that augment or alter the network as the network is trained. Supplemental Workshop site:https://rl4ed.org/aaai2022/index.html. Attendance is open to all registered participants. RES: A Robust Framework for Guiding Visual Explanation. SL-VAE: Variational Autoencoder for Source Localization in Graph Information Diffusion. Liang Zhao, Ting Hua, Chang-Tien Lu, and Ing-Ray Chen. 40 attendees including: invited speakers, authors of accepted papers and shared task participants. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), short paper (acceptance rate: 19.9%), Singapore, Dec 2018, accepted. [materials][data]. Virtual . P. 6205, succursale Centre-villeMontral, (Qubec) H3C 3T5Canada. All papers will be peer-reviewed, single-blinded (i.e., please include author names/affiliations/email addresses on your first page). All submissions must be anonymous and conform to AAAI standard for double-blind review. 32, no. 9, no. Negar Etemadyrad, Qingzhe Li, Liang Zhao. We invite submissions from participants who can contribute to the theory and applications of modeling complex graph structures such as hypergraphs, multilayer networks, multi-relational graphs, heterogeneous information networks, multi-modal graphs, signed networks, bipartite networks, temporal/dynamic graphs, etc. Applications of causal inference and discovery in machine learning/deep learning motivated by information-theoretic approaches (e.g. This is a one-day workshop, planned with a 10-minute opening, 6 invited keynotes, ~6 contributed talks, 2 poster sessions, and 2 panel discussions. Xuchao Zhang, Liang Zhao, Zhiqian Chen, and Chang-Tien Lu. KDD 2022 -ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. The AAAI template https://aaai.org/Conferences/AAAI-22/aaai22call/ should be used for all submissions. These models can also generate instant feedback to instructors and help them to improve their teaching effectiveness. Apr 25th through Fri the 29th, 2022. . Hierarchical Incomplete Multisource Feature Learning for Spatiotemporal Event Forecasting. in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), research track (acceptance rate: 18.2%), San Francisco, California, pp. We hope this will help bring the communities of data mining and visualization more closely connected. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang, Kun Xu, Liang Zhao, Xi Peng, Yinglong Xia and Charu Aggarwal, "Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding", In the Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019. Workshops are one day unless otherwise noted in the individual descriptions. Nowadays, machine learning solutions are widely deployed. Benchmarks to reliably evaluate attacks/defenses and measure the real progress of the field. in Proceedings of the SIAM International Conference on Data Mining (SDM 2015), (acceptance rate: 22%), Vancouver, BC, pp. Workshop registration is available to AAAI-22 technical registrants at a discounted rate, or separately to workshop only registrants. In fact, the increasingly digitized education tools and the popularity of online learning have produced an unprecedented amount of data that provides us with invaluable opportunities for applying AI in education. "Robust Regression via Heuristic Hard Thresholding". We invite submissions of technical papers up to 7 pages excluding references and appendices. Both the research papers track and the applied data science papers track will take . This date takes priority over those shown below and could be extended for some programs. Social Media based Simulation Models for Understanding Disease Dynamics. Deadlines are shown in America/Los_Angeles time. Submit to:https://openreview.net/group?id=AAAI.org/2022/Workshop/AdvML, Yinpeng Dong (dyp17@mails.tsinghua.edu.cn, 30 Shuangqing Road, Haidian District, Tsinghua University, Beijing, China, 100084, Phone: +86 18603303421), Yinpeng Dong (Tsinghua University, dyp17@mail.tsinghua.edu.cn), Tianyu Pang (Tsinghua University, pty17@mails.tsinghua.edu.cn), Xiao Yang (Tsinghua University, yangxiao19@mails.tsinghua.edu.cn), Eric Wong (MIT, wongeric@mit.edu), Zico Kolter (CMU, zkolter@cs.cmu.edu), Yuan He (Alibaba, heyuan.hy@alibaba-inc.com ). Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. These cookies track visitors across websites and collect information to provide customized ads. TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints. Transfer learning methods for business document reading and understanding. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Journal of Biomedical Semantics, (impact factor: 1.845), 2018, accepted. Frontiers in Big Data, accepted, 2021.
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