kdd 2022 deadline

Online. Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. Examples of the datasets which may be considered are the DBTex Radiology Mammogram dataset and the Johns Hopkins COVID-19 case reports. Winter. These challenges and issues call for robust artificial intelligence (AI) algorithms and systems to help. Can AI achieve the same goal without much low-level supervision? Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. CS Conference Deadlines - Yanlin In particular, we encourage papers covering late-breaking results and work-in-progress research. Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. The 33rd European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databasesg (ECML-PKDD 2022) (Acceptance Rate: 26%), accepted, 2022. The desired LENGTH of the workshop: Full-day (~8 hours). Methods for learning network architecture during training, including Incrementally building neural networks during training, new performance benchmarks for the above. Each full paper will be reviewed by three PC members, while extended abstracts will not be reviewed. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. Graph Neural Networks: Foundations, Frontiers, and Applications. Information theory has demonstrated great potential to solve the above challenges. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. These datasets can be leveraged to learn individuals behavioral patterns, identify individuals at risk of making sub-optimal or harmful choices, and target them with behavioral interventions to prevent harm or improve well-being. The workshop follows a single-blind reviewing process. Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen. 2022. In Proceedings of the 20th International Conference on Data Mining (ICDM 2020), (acceptance rate: 9.8%), November 17-20, 2020, Virtual Event, Sorrento, Italy, 10 pages. It highlights the importance of declarative languages that enable such integration for covering multiple formalisms at a high-level and points to the need for building a new generation of ML tools to help domain experts in designing complex models where they can declare their knowledge about the domain and use data-driven learning models based on various underlying formalisms. By entering your email, you consent to receive communications from UdeM. Please note that foreign students must allow for 3 to 6 months to complete all the formalities required to study in Canada. IBM Research, 2018. TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints. DI-2022 accepted papers will not be archived in the main KDD 2022 proceedings. Pattern Recognition, (impact factor: 7.196),112 (2021): 107711. Liang Zhao, Jiangzhuo Chen, Feng Chen, Fang Jin, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. Shi, Y., Deng, M., Yang, X., Liu, Q., Zhao, L., & Lu, C. T. "A Framework for Discovering Evolving Domain Related Spatio-Temporal Patterns in Twitter." Junxiang Wang, Hongyi Li, Zheng Chai, Yongchao Wang, Yue Cheng, Liang Zhao. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is for causal estimation in behavioral science. We plan to invite 2-4 keynote speakers from prestigious universities and leading industrial companies. The AAAI template https://aaai.org/Conferences/AAAI-22/aaai22call/ should be used for all submissions. 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. 2022. Attendance is open to all, subject to any room occupancy constraints. 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. ; (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. Yuyang Gao and Liang Zhao. 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/. And with particular focuses but not limited to these application domains: Our program consists of two sessions: academic session and industry session. Proposals of technical talk (up to one-page abstract including short Bio of the main speaker). 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. Poster session: One poster session of all accepted papers which leads for interaction and personal feedback to the research. 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. We invite researchers to submit either full-length research papers (8 pages) or extended abstracts (2 pages) describing novel contributions and preliminary results, respectively, to the topics above; a more extensive list of topics is available on the Workshop website. Generative Adversarial Learning of Protein Tertiary Structures. Ting Hua, Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. 1503-1512, Aug 2015. Securing personal information, genomics, and intellectual property, Adversarial attacks and defenses on biomedical datasets, Detecting and preventing spread of misinformation, Usable security and privacy for digital health information, Phishing and other attacks using health information, Novel use of biometrics to enhance security, Machine learning (including RL) security and resiliency, Automation of data labeling and ML techniques, Operational and commercial applications of AI, Explanations of security decisions and vulnerability of explanations. All accepted papers will be archived on the workshop website, but there will not be formal proceedings. Babies learn their first language through listening, talking, and interacting with adults. Yuyang Gao, Lingfei Wu, Houman Homayoun, and Liang Zhao. December 2020, July 21: Clarified that the workshop this year will be held, June 20: Paper notification is now extended to, Paper reviews are underway! Computer Communications, (impact factor: 3.34), Elsevier, vo. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), short paper (acceptance rate: 19.9%), Singapore, Dec 2018, accepted. We will also organize 3 shared tasks in this workshop: punctuation restoration, domain adaptation for punctuation restoration, and chitchat detection. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. Balaraman Ravindran (Indian Institute of Technology Madras, India ravi@cse.iitm.ac.in), Balaraman Ravindran (Indian Institute of Technology Madras, India Primary contact (ravi@cse.iitm.ac.in), Kristian Kersting (TU Darmstadt, Germany, kersting@cs.tu-darmstadt.de), Sriraam Natarajan (Univ of Texas Dallas, USA, Sriraam.Natarajan@utdallas.edu), Ginestra Bianconi (Queen Mary University of London, UK, ginestra.bianconi@gmail.com), Philip S. Chodrow (University of California, Los Angeles, USA, phil@math.ucla.edu) Tarun Kumar (Indian Institute of Technology Madras, India, tkumar@cse.iitm.ac.in), Deepak Maurya (Purdue University, India, maurya@cse.iitm.ac.in), Shreya Goyal (Indian Institute of Technology Madras, India, Goyal.3@iitj.ac.in), Workshop URL:https://sites.google.com/view/gclr2022/. This will include invited talks, poster sessions and a panel to discuss the achievements of past DSTC series, and future direction. Algorithms and theories for learning AI models under bias and scarcity. Accepted submissions will have the option of being posted online on the workshop website. How to do good research, Get it published in SIGKDD and get it cited! Small Molecule Generation via Disentangled Representation Learning. Workshop Date: Sunday August 14, 2022 EDT. Fang Jin, Wei Wang, Liang Zhao, Edward Dougherty, Yang Cao, Chang-Tien Lu, and Naren Ramakrishnan. Like other systems, ML systems must meet quality requirements. Junxiang Wang, Liang Zhao, Yanfang Ye, and Yuji Zhang. Such systems are better modeled by complex graph structures such as edge and vertex labeled graphs (e.g., knowledge graphs), attributed graphs, multilayer graphs, hypergraphs, temporal/dynamic graphs, etc. Submission Site:https://cmt3.research.microsoft.com/SAS2022, Abdelrahman Mohamed (Facebook, abdo@fb.com), Hung-yi Lee (NTU, hungyilee@ntu.edu.tw), Shinji Watanabe (CMU, shinjiw@ieee.org), Tara Sainath (Google, tsainath@google.com), Karen Livescu (TTIC, klivescu@ttic.edu), Shang-Wen Li (Facebook, shangwel@fb.com), Ewan Dunbar (University of Toronto, ewan.dunbar@utoronto.ca) Emmanuel Dupoux (EHESS/Facebook, dpx@fb.com), Workshop URL:https://aaai-sas-2022.github.io/. Accelerated Gradient-free Neural Network Training by Multi-convex Alternating Optimization. Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data. This workshop wants to emphasize on the importance of integrative paradigms for solving the new wave of AI applications. 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. The workshop will focus on the application of AI to problems in cyber-security. 11-13. Dataset(s) will be provided to hack-a-thon participants. No supplement is allowed for extended abstracts. Advances in complex engineering systems such as manufacturing and materials synthesis increasingly seek artificial intelligence/machine learning (AI/ML) solutions to enhance their design, development, and production processes. Adversarial attacking deep learning systems, Robust architectures against adversarial attacks, Hardware implementation and on-device deployment, Benchmark for evaluating model robustness, New methodologies and architectures for efficient and robust deep learning, December 3, 2021 Acceptance Notification, Applications of privacy-preserving AI systems, Differential privacy: theory and applications, Distributed privacy-preserving algorithms, Privacy preserving optimization and machine learning, Privacy preserving test cases and benchmarks. Semantic understanding of business documents. Deadlines are shown in America/Los_Angeles time. 205-214, San Francisco, California, Aug 2016. 2022. 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. Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1] . Lastly, learning joint modalities is of interest to both Natural Language Processing (NLP) and Computer Vision (CV) forums. AI System Robustness: participants will consider techniques for detecting and mitigating vulnerabilities at each of the processing stages of an AI system, including: the input stage of sensing and measurement, the data conditioning stage, during training and application of machine learning algorithms, the human-machine teaming stage, and during operational use. The excellent papers will be recommended for publications in SCI or EI journals. Attendance is open to all registered participants. Deep Spatial Domain Generalization. Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. Rather than studying robustness with respect to particular ML algorithms, our approach will be to explore robustness assurance at the system architecture level, during both development and deployment, and within the human-machine teaming context. Business documents are central to the operation of all organizations, and they come in all shapes and sizes: project reports, planning documents, technical specifications, financial statements, meeting minutes, legal agreements, contracts, resumes, purchase orders, invoices, and many more. After seventh highly successful events, the eighth Symposium on Visualization in Data Science (VDS) will be held at a new venue, ACM KDD 2022 as well as IEEE VIS 2022. In recent months/years, major global shifts have occurred across the globe triggered by the Covid pandemic. World Wide Web Conference (WWW 2018), (acceptance rate: 14.8%), Lyon, FR, Apr 2018, accepted. Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. In general, AI techniques are still not widely adopted in the real world. Yuyang Gao, Tong Sun, Sungsoo Hong, 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. Amir A. Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng. Research efforts and datasets on text fact verification could be found, but there is not much attention towards multi-modal or cross-modal fact-verification. Finally, the workshop will welcome papers that describe the release of privacy-preserving benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples. All submissions must be in PDF format and formatted according to the new Standard AAAI Conference Proceedings Template. 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.



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