Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. The 48th International Conference on Parallel Processing (ICPP 2019), (acceptance rate: 20%), accepted, Kyoto, Japan. Pattern Recognition, (impact factor: 7.196),112 (2021): 107711. Reasons include: (1) a lack of certification of AI for security, (2) a lack of formal study of the implications of practical constraints (e.g., power, memory, storage) for AI systems in the cyber domain, (3) known vulnerabilities such as evasion, poisoning attacks, (4) lack of meaningful explanations for security analysts, and (5) lack of analyst trust in AI solutions. How do metrics of capability and generality, and the trade-offs with performance affect safety? to protect data owner privacy in FL. Yuyang Gao and Liang Zhao. Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. 2022. "Unsupervised Spatial Event Detection in Targeted Domains with Applications to Civil Unrest Modeling." Merge remote-tracking branch 'origin/master', 2. Countdowns to conference deadlines in the field of autonomous driving. Workshop registration is available to AAAI-22 technical registrants at a discounted rate, or separately to workshop only registrants. 1-39, November 2016. The robust development and assured deployment of AI systems: Participants will discuss how to leverage and update common software development paradigms, e.g., DevSecOps, to incorporate relevant aspects of system-level AI assurance. Federated learning (FL) is one promising machine learning approach that trains a collective machine learning model using sharing data owned by various parties. Jan 13, 2022: Notification. The scope of the workshop includes, but is not limited to, the following areas: We also invite participants to an interactive hack-a-thon. ^All accepted WSDM papers are associated with an interactive poster presentation in addition to oral presentations. Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. Whats more, various AI based models are trained on massive student behavioral and exercise data to have the ability to take note of a students strengths and weaknesses, identifying where they may be struggling. Welcome to the home of the 2023 ACM SIGMOD/PODS Conference, to be held in the Seattle metropolitan area, Washington, USA, on June 18 - June 23, 2023. This cookie is set by GDPR Cookie Consent plugin. 19-25, 2016. Optimal transport-based analysis of structured data, such as networks, meshes, sequences, and so on; The applications of optimal transport in molecule analysis, network analysis, natural language processing, computer vision, and bioinformatics. Attendance is open to all; at least one author of each accepted submission must be physically/virtually present at the workshop. For authors who do not wish their papers to be posted online, please mention this in the workshop submission. 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. Papers will be submitted electronically using Easychair. The deep learning community must often confront serious time and hardware constraints from suboptimal architectural decisions. The accepted papers will be posted on the workshop website and will not appear in the AAAI proceedings. Ranking, acceptance rate, deadline, and publication tips. Integration of Deep learning and Constraint programming. 27, 2022: Please check out Speical Days at, Apr. The following paper categories are welcome: Submission site:https://sites.google.com/view/eaai-ws-2022/call, Silvia Tulli (Dept. Despite gratifying achievements that have demonstrated the great potential and bright development prospect of introducing AI in education, developing and applying AI technologies to educational practice is fraught with its unique challenges, including, but not limited to, extreme data sparsity, lack of labeled data, and privacy issues. 2022. The mission of the TRASE workshop is to bring together researchers from multiple engineering disciplines, including Computer Science, and Computer, Mechanical, Electrical, and Systems Engineering, who focus their energies in understanding both specific TRASE subproblems, such as perception, planning, and control, as well as robust and reliable end-to-end integration of autonomy. This AAAI workshop aims to bring together researchers from core AI/ML, robotics, sensing, cyber physical systems, agriculture engineering, plant sciences, genetics, and bioinformatics communities to facilitate the increasingly synergistic intersection of AI/ML with agriculture and food systems. Deep Geometric Neural Networks for Spatial Interpolation. This workshop aims to discuss important topics about adversarial ML to deepen our understanding of ML models in adversarial environments and build reliable ML systems in the real world. Yuyang Gao, Giorgio Ascoli, Liang Zhao. Attendance is virtual and open to all. Neurocomputing (Impact Factor: 5.719), accepted. SUPERB is a benchmarking platform that allows the community to train, evaluate, and compare the speech representations on diverse downstream speech processing tasks. Papers more suited for a poster, rather than a presentation, would be invited for a poster session. Registration information will be mailed directly to all invited participants in December. Liyan Xu, Xuchao Zhang, Zong Bo, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho Choi. The format is the standard double-column AAAI Proceedings Style. Held in conjunction with KDD'22 Aug 15, 2022 - Washington DC, USA. The extraction, representation, and sharing of health data, patient preference elicitation, personalization of generic therapy plans, adaptation to care environments and available health expertise, and making medical information accessible to patients are some of the relevant problems in need of AI-based solutions. A Report on the First Workshop on Document Intelligence (DI) at NeurIPS 2019. Workshop Date: Sunday August 14, 2022 EDT. Eliminating the need to guess the right topology in advance of training is a prominent benefit of learning network architecture during training. As for the Kraken, they made one trade a month ago to acquire a seventh defenceman, Jaycob Megna and did nothing else (from 'Kraken remain quiet as NHL trade deadline passes,' The Seattle . The trained models are intended to assign scores to novel utterances, assessing whether they are possible or likely utterances in the training language. Journal of Biomedical Semantics, (impact factor: 1.845), 2018, accepted. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. . Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala. ISBN: 978-981-16-6053-5. 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. Topics of interest include but are not limited to: Acronyms, i.e., short forms of long phrases, are common in scientific writing. For example, failures in IoT can result in infrastructure disruptions, and failures in autonomous cars can lead to congestion and crashes. Han Wang, Hossein Sayadi, Avesta Sasan, Houman Homayoun, Liang Zhao, Tinoosh Mohsenin, Setareh Rafatirad. Submission site:https://easychair.org/conferences/?conf=kdf22, Chair:Xiaomo Liu (J.P. Morgan Chase AI Research, xiaomo.liu@jpmchase.com), Zhiqiang Ma (J.P. Morgan Chase AI Research), Armineh Nourbakhsh (J.P. Morgan Chase AI Research), Sameena Shah (J.P. Morgan Chase AI Research), Gerard de Melo (Hasso Plattner Institute), Le Song (Mohamed bin Zayed University of Artificial Intelligence), Workshop URL:https://aaai-kdf.github.io/kdf2022/. "Efficient Global String Kernel with Random Features: Beyond Counting Substructures", 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. Inspired by the question, there is a trend in the machine learning community to adopt self-supervised approaches to pre-train deep networks. Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye. Algorithms for secure and privacy-aware machine learning for AI. The workshop will be organized as a full day meeting. 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. Yuyang Gao, Tanmoy Chowdhury (co-first author), Lingfei Wu, Liang Zhao. Yujie Fan, Yanfang (Fanny) Ye, Qian Peng, Jianfei Zhang, Yiming Zhang, Xusheng Xiao, Chuan Shi, Qi Xiong, Fudong Shao, and Liang Zhao. Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu, "Robust Regression via Heuristic Corruption Thresholding and Its Adaptive Estimation Variation", ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 1.98), accepted, 2019. After the submission deadline, the names and order of authors cannot be changed. The post-lunch session will feature a second keynote talk, two invited talks. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Although textual data is prevalent in a large amount of finance-related business problems, we also encourage submissions of studies or applications pertinent to finance using other types of unstructured data such as financial transactions, sensors, mobile devices, satellites, social media, etc. The final schedule will be available in November. 105, no. Colin Camerer (California Institute of Technology), Susan Murphy (Harvard University). https://doi.org/10. Qiang Yang, Hong Kong University of Science and Technology/ WeBank, China, (qyang@cse.ust.hk ), Sin G. Teo, Institute for Infocomm Research, Singapore (teosg@i2r.a-star.edu.sg), Han Yu, Nanyang Technological University, Singapore (han.yu@ntu.edu.sg), Lixin Fan, WeBank, China (lixinfan@webank.com), Chao Jin, Institute for Infocomm Research, Singapore (jin_chao@i2r.a-star.edu.sg), Le Zhang, University of Electronic Science and Technology of China (zhangleuestc@gmail.com), Yang Liu, Tsinghua University, China (liuy03@air.tsinghua.edu.cn), Zengxiang Li, Digital Research Institute, ENN Group, China (lizengxiang@enn.cn), Workshop site:http://federated-learning.org/fl-aaai-2022/. Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. Computer Science and Engineering, INESC-ID, IST Ulisboa, Lisbon, Portugal currently at Sorbonne University, Paris, France silvia.tulli@gaips.inesc-id.pt), Prashan Madumal (Science and Information Systems, University of Melbourne, Parkville, Australia pmathugama@student.unimelb.edu.au), Mark T. Keane (School of Computer Science, University College Dublin, Dublin, Ireland mark.keane@ucd.ie), David W. Aha (Navy Center for Applied Research in AI, Naval Research Laboratory, Washington, DC, USA david.aha@nrl.navy.mil), Adam Johns (Drexel University, Philadelphia, PA USA), Tathagata Chakraborti (IBM Research AI, Cambridge, MA USA), Kim Baraka (VU University Amsterdam, Netherlands), Isaac Lage (Harvard University, Cambridge, MA USA), David Martens (University of Antwerp, Belgium), Mohamed Chetouani (Sorbonne Universit, Paris, France), Peter Flach (University of Bristol, United Kingdom), Kacper Sokol (University of Bristol, United Kingdom), Ofra Amir (Technion, Haifa, Israel), Dimitrios Letsios (Kings College London, London, United Kingdom), Supplemental workshop site:https://sites.google.com/view/eaai-ws-2022/topic. Automatic fact/claim verification has recently become a topic of interest among diverse research communities. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted. This workshop will encourage researchers from interdisciplinary domains working on multi-modality and/or fact-checking to come together and work on multimodal (images, memes, videos etc.) The bottleneck to discovery is now our ability to analyze and make sense of heterogeneous, noisy, streaming, and often massive datasets. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), accepted. with other vehicles via vehicular communication systems (e.g., dedicated short range communication (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and 5G/6G mobile networks) for cooperation. Novel algorithmic solutions to causal inference or discovery problems using information-theoretic tools or assumptions. ECoST: Energy-Efficient Co-Locating and Self-Tuning MapReduce Applications. GNES: Learning to Explain Graph Neural Networks. Yuyang Gao, Giorgio Ascoli, Liang Zhao. KDD 2022 Reveals Schedule of Data Mining and Knowledge Discovery Papers IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol.
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