kdd 2022 deadline

27, 2022: Please check out Speical Days at, Apr. Zirui Xu, Fuxun Xu, Liang Zhao, and Xiang Chen. AI is one of these transformative technologies that is now achieving great successes in various real-world applications and making our life more convenient and safer. Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted. Deadline: FSE 2023. In addition, authors can provide an optional two (2) page supplement at the end of their submitted paper (it needs to be in the same PDF file) focused on reproducibility. 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. "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." Deep learning has achieved significant success for artificial intelligence (AI) in multiple fields. Despite the great success of deep neural networks (DNNs) in many artificial intelligence (AI) tasks, they still suffer from limitations, such as poor generalization behavior for out-of-distribution (OOD) data, vulnerability to adversarial examples, and the black-box nature of DNNs. DOI:https://doi.org/10.1145/3339823. This workshop on Trustworthy Autonomous Systems Engineering (TRASE) offers an opportunity to highlight state of the art research in trustworthy autonomous systems, as well as provide a vision for future foundational and applied advances in this critical area at the intersection of AI and Cyber-Physical Systems. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. While classical security vulnerabilities are relevant, ML techniques have additional weaknesses, some already known (e.g., sensitivity to training data manipulation), and some yet to be discovered. The workshop follows a single-blind reviewing process. Oct. 24, 2021: The KDD2022 website is LIVE! in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), applied data science track, accepted (acceptance rate: 19.9%), pp. For example, AI tools are built to ease the workload for teachers. We welcome submissions of long (max. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. 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. arXiv preprint arXiv:2212.03954 (2022). Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao. Games provide an abstract and formal model of environments in which multiple agents interact: each player has a well-defined goal and rules to describe the effects of interactions among the players. However, research in the AI field also shows that their performance in the wild is far from practical due to the lack of model efficiency and robustness towards open-world data and scenarios. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), short paper (acceptance rate: 19.9%), Singapore, Dec 2018, accepted. 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. We consider submissions that havent been published in any peer-reviewed venue (except those under review). Guangji Bai and Liang Zhao. May 8, 2022: Student Travel Awards announcement is, Apr. Besides academia, many companies and institutions are researching on topics specific to their particular domains. Naren Ramakrishnan, Patrick Butler, Sathappan Muthiah, Nathan Self, Rupinder Khandpur, Parang Saraf, Wei Wang, Jose Cadena, Anil Vullikanti, Gizem Korkmaz, Chris Kuhlman, Achla Marathe, Liang Zhao, Ting Hua, Feng Chen, et al.. "'Beating the news' with EMBERS:forecasting civil unrest using open source indicators." The third AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-22) builds on the success of previous years PPAI-20 and PPAI-21 to provide a platform for researchers, AI practitioners, and policymakers to discuss technical and societal issues and present solutions related to privacy in AI applications. In our workshop, we specifically focus on the trustworthy issues in AI for healthcare, aiming to make clinical AI methods more reliable in real clinical settings and be willingly used by physicians. Out of these, around 20~30 papers are accepted. The growing popularity of NAS methods demonstrates the communitys hunger for better ways of choosing or evolving network architectures that are well-matched to the problem at hand. Topics of interest in the biomedical space include: Topics of general interest to cyber-security include: Submission site:https://easychair.org/conferences/?conf=aics22, Tamara Broderick (MIT CSAIL, tamarab@mit.edu), James Holt (Laboratory for Physical Sciences, USA, holt@lps.umd.edu), Edward Raff (Booz Allen Hamilton, USA, Raff_Edward@bah.com), Ahmad Ridley (National Security Agency), Dennis Ross (MIT Lincoln Laboratory, USA, dennis.ross@ll.mit.edu), Arunesh Sinha (Singapore Management University, Singapore, aruneshs@smu.edu.sg), Diane Staheli (MIT Lincoln Laboratory, USA, diane.staheli@ll.mit.edu), William W. Streilein (MIT Lincoln Laboratory, USA, wws@ll.mit.edu), Milind Tambe (Harvard University, USA, milind_tambe@harvard.edu), Yevgeniy Vorobeychik (Washington University in Saint Louis, USA, eug.vorobey@gmail.com) Allan Wollaber (MIT Lincoln Laboratory, USA, Allan.Wollaber@ll.mit.edu), Supplemental workshop site:http://aics.site/. We welcome the submissions in the following two formats: The submissions should adhere to theAAAI paper guidelines. Feature Constrained Multi-Task Learnings for Event Forecasting in Social Media." Algorithms and theories for trustworthy AI models. Spatial Event Forecasting in Social Media with Geographically Hierarchical Regularization. 1145/3394486.3403221. Self-supervised learning approaches involving the interaction of speech/audio and other modalities. KDD 2023 August 06-10, 2023. Online marketplaces exist in a diverse set of domains and industries, for example, rideshare (Lyft, DiDi, Uber), house rental (Airbnb), real estate (Beke), online retail (Amazon, Ebay), job search (LinkedIn, Indeed.com, CareerBuilder), and food ordering and delivery (Doordash, Meituan). We invite a long research paper (8 pages) and a demo paper (4 pages) (including references). We will accept the extended abstracts of the relevant and recently published work too. the 33rd Annual Computer Security Applications Conference (ACSAC 2018), (acceptance rate: 20.1%), San Juan, Puerto Rico, USA, Dec 2018, accepted. . SUPERB is a benchmarking platform that allows the community to train, evaluate, and compare the speech representations on diverse downstream speech processing tasks. The workshop will focus on both the theoretical and practical challenges related to the design of privacy-preserving AI systems and algorithms and will have strong multidisciplinary components, including soliciting contributions about policy, legal issues, and societal impact of privacy in AI. Research track papers reporting the results of ongoing or new research, which have not been published before. Paper Final Version Due: Monday August 1, 2022. Yiming Zhang, Yujie Fan, Wei Song, Shifu Hou, Yanfang Ye, Xin Li, Liang Zhao, Chuan Shi, Jiabin Wang, Qi Xiong. Integration of AI-based approaches with engineering prototyping and manufacturing. The adversarial ML could also result in potential data privacy and ethical issues when deploying ML techniques in real-world applications. Submissions of technical papers can be up to 7 pages excluding references and appendices. In addition to that, we propose a shared task on one of the challenging SDU tasks, i.e., acronym extraction and disambiguation in multiple languages text. 105, no. Any participant who experiences unacceptable behavior may contact any current member of the SIGMOD Executive Committee, the PODS Executive Committee, DBCares, or this year's D&I co-chairs Pnar Tzn (pito@itu.dk) and Renata Borovica-Gajic (renata.borovica@unimelb.edu.au). Some examples of the success of information theory in causal inference are: the use of directed information, minimum entropy couplings and common entropy for bivariate causal discovery; the use of the information bottleneck principle with applications in the generalization of machine learning models; analyzing causal structures of deep neural networks with information theory; among others. Short or position papers of up to 4 pages are also welcome. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2015), regular paper (acceptance rate: 8.4%), Atlantic City, NJ, pp. This cookie is set by GDPR Cookie Consent plugin. Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. 639-648, Nov 2015. Amir A. Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng. Yuyang Gao, Lingfei Wu, Houman Homayoun, and Liang Zhao. 15, pp. arXiv preprint arXiv:2207.09542 (2022). Identification of information-theoretic quantities relevant for causal inference and discovery. Check the deadlines for submitting your application. Zhiqian Chen, Gaurav Kolhe, Setareh Rafatirad, Chang-Tien Lu, Sai Dinakarrao, Houman Homayoun, Liang Zhao. This workshop aims to bring together researchers from AI and diverse science/engineering communities to achieve the following goals: 1) Identify and understand the challenges in applying AI to specific science and engineering problems2) Develop, adapt, and refine AI tools for novel problem settings and challenges3) Community-building and education to encourage collaboration between AI researchers and domain area experts. The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains. System reports will be presented during poster sessions. Jos Miguel Hernndez-Lobato, University of CambridgeProf. DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. Oral Paper (Top 5% among the accepted papers). Publication in HC-SSL does not prohibit authors from publishing their papers in archival venues such as NeurIPS/ICLR/ICML or IEEE/ACM Conferences and Journals. Detailed information could be found on the website of the workshop. You also have the option to opt-out of these cookies. Topics of interest include, but are not limited to: Paper submissions will be in two formats: full paper (8 pages) and position paper (4 pages): The submission website ishttps://easychair.org/conferences/?conf=trase2022. Yuanqi du, George Mason University, USA; Jian Pei, Simon Fraser University, Canada; Charu Aggarwal, IBM Research AI, USA; Philip S. Yu, University of Illinois at Chicago, USA; Xuemin Lin, University of New South Wales, Australia; Jiebo Luo, University of Rochester, USA; Lingfei Wu, JD.Com Silicon Valley Research Center, USA; Yinglong Xia, Facebook AI, USA; Jiliang Tang, Michigan State University, USA; Peng Cui, Tsinghua University, China; William L. Hamilton, McGill University, Canada; Thomas Kipf, University of Amsterdam, Netherlands, Workshop URL:https://deep-learning-graphs.bitbucket.io/dlg-aaai22/. Held in conjunction with KDD'22 Aug 15, 2022 - Washington DC, USA. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is [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. Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala. All submissions must be anonymous and conform to AAAI standards for double-blind review. SL-VAE: Variational Autoencoder for Source Localization in Graph Information Diffusion. After the submission deadline, the names and order of authors cannot be changed. 29, no. These submissions would benefit from additional exposure and discussion that can shape a better future publication. Guangji Bai, Chen Ling, Yuyang Gao, Liang Zhao. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. We solicit papers describing significant and innovative research and applications to the field of job marketplaces. upon methodologies and applications for extracting useful knowledge from data [1]. Papers that are under review at another conference or journal are acceptable for submission at this workshop, but we will not accept papers that have already been accepted or published at a venue with formal proceedings (including KDD 2022). A tag already exists with the provided branch name. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 20%=174/870), short paper, to appear, 2022. Liang Zhao, Olga Gkountouna, and Dieter Pfoser. The bottleneck to discovery is now our ability to analyze and make sense of heterogeneous, noisy, streaming, and often massive datasets. SDU accepts both long (8 pages including references) and short (4 pages including references) papers. Disease Contact Network.

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