Chen Ling, Carl Yang, 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. [Bests of ICDM], Zheng Zhang and Liang Zhao. VDS@KDD will be hybrid and VDS@VIS will be hybrid (both virtual and in-person) in 2022. An Invertible Graph Diffusion Model for Source Localization. Identification of information-theoretic quantities relevant for causal inference and discovery. References will not count towards the page limit. Proceedings of the IEEE (impact factor: 9.237), vol. This is especially the case for non-traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data. The cookies is used to store the user consent for the cookies in the category "Necessary". Online Flu Epidemiological Deep Modeling on Scientific documents such as research papers, patents, books, or technical reports are one of the most valuable resources of human knowledge. Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1] . We welcome the submissions in the following two formats: The submissions should adhere to theAAAI paper guidelines. System reports will be presented during poster sessions. The scope of the workshop includes, but is not limited to, the following areas: We also invite participants to an interactive hack-a-thon. We also invite papers that have been published at other venues to spark discussions and foster new collaborations. You can optionally export all deadlines to Google Calendar or .ics . Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. Submissions will be peer-reviewed, single-blinded, and assessed based on their novelty, technical quality, significance, clarity, and relevance regarding the workshop topics. 963-971, Apr-May 2015. August 14-18, 2022. Yujie Fan, Yiming Zhang, Shifu Hou, Lingwei Chen, Yanfang Ye, Chuan Shi, Liang Zhao, Shouhuai Xu. Topics include but not limited to: Large-scale and novel targeting technologies, Fraud, fairness, explainability and privacy, Intelligent assistants in job hunting and hiring automation, Large-scale and high performing data infrastructure, data analysis and tooling, Economics and causal inference in online jobs marketplace, Large-scale analytics of user behaviors in online jobs marketplace. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. We welcome full research papers, position papers, and extended abstracts. Each oral presentation will be allocated between 10-15 minutes, while the spotlight presentation will be 2 minute each. In Proceedings of the IEEE International Conference on Big Data (BigData 2014), pp. The papers may consist of up to seven pages of technical content plus up to two additional pages for references. We propose a full day workshop with the following sessions: The workshop solicits paper submissions from participants (26 pages). IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol. 1503-1512, Aug 2015. 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. iCal Outlook robotics Thank you for all your contributions, our, Paper submission deadline is now extended to. There will be live Q&A sessions at the end of each talk and oral presentation. Declarative languages and differentiable programming. Comparison or integration of self-supervised learning methods and other semi-supervised and transfer learning methods in speech and audio processing tasks. SL-VAE: Variational Autoencoder for Source Localization in Graph Information Diffusion. ASPLOS 2023 will be moving to three submission deadlines. Like other systems, ML systems must meet quality requirements. 2022. 2022. Manuscripts must be submitted as PDF files viaEasyChair online submission system. Integration of neuro and symbolic approaches. Submitted technical papers can be up to 4 pages long (excluding references and appendices). Spatiotemporal Innovation Center Team. "Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning." Authors are strongly encouraged to make data and code publicly available whenever possible. There were two workshops on similar topics hosted at ICML 2020 and NeurIPS 2020, and both workshops observed positive feedback and overwhelming participation. Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, and Liang Zhao. The workshop will be organized as a full day meeting. IBM Research, 2018. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. CoRL 2023 97 days 17h 29m 15s November 06-09, 2023. Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, and Sai Dinakarrao. Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! In the Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (acceptance rate: 17.9%), accepted, Macao, China, Aug 2019. Papers will be peer-reviewed and selected for oral and/or poster presentation at the workshop. Saliency-Augmented Memory Completion for Continual Learning. [Bests of ICDM]. Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. Its capabilities have expanded from processing structured data (e.g. KDD 2023 KDD '23 ​ ​ ​ August 6-10, 2023. Oral presentations: 10 minute presentation for oral papers. However, workshop organizers may set up any archived publication mechanism that best suits their workshop. Complex systems are often characterized by several components that interact in multiple ways among each other. Qingzhe Li, Jessica Lin, Liang Zhao and Huzefa Rangwala. Modern interface, high scalability, extensive features and outstanding support are the signatures of Microsoft CMT. In some programs, spots may be available after the deadlines. ETA (expected time-of-arrival) prediction. Winter. What safety engineering considerations are required to develop safe human-machine interaction? Knowledge discovery from various data sources has gained the attention of many practitioners in recent decades. 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. It further combines academia and industry in a quest for well-founded practical solutions. 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. The workshop is organized by paper presentations.The length of the workshop: 1-day, 6-8 pages for full papers2-4 for poster/short/position papers, Submission URL:https://easychair.org/conferences/?conf=aaai-2022-workshop, Wenzhong Guo (Fuzhou University, fzugwz@163.com), Chin-Chen Chang (Feng Chia University, alan3c@gmail.com), Chi-Hua Chen (Fuzhou University, chihua0826@gmail.com), Haishuai Wang (Fairfield University & Harvard University, hwang@fairfield.edu), Feng-Jang Hwang (University of Technology Sydney), Cheng Shi (Xian University of Technology), Ching-Chun Chang (National Institute of Informatics, Japan). in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), short paper (acceptance rate: 19.9%), Singapore, Dec 2018, accepted. 8 pages), short (max. Deadline in your local America/New_York timezone: Deadline in timezone from conference website: DASFAA 2022. We will accept the extended abstracts of the relevant and recently published work too. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted. Disentangled Dynamic Graph Deep Generation, SIAM International Conference on Data Mining (SDM 2021), (acceptance rate: 21.3%), accepted. 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." Key obstacles include lack of high-quality data, difficulty in embedding complex scientific and engineering knowledge in learning, and the need for high-dimensional design space exploration under constrained budgets. Big Data 2022 December 13-16, 2022. Papers should be up to 4 pages in length (excluding references) formatted using the AAAI template. Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao. Junxiang Wang, Hongyi Li, Zheng Chai, Yongchao Wang, Yue Cheng, Liang Zhao. The desired LENGTH of the workshop: Full-day (~8 hours). Why did so many AI/ML models fail during the pandemic? Automatic fact/claim verification has recently become a topic of interest among diverse research communities. The workshop plans to invite about 50-75 participants. This workshop covers (but not limited to) the following topics: , It is a one day workshop and includes: invited talks, interactive discussions, paper presentations, shared task presentations, poster session etc. Tips for Doing Good DM Research & Get it Published! Novel AI-based techniques to improve modeling of engineering systems. text, images, and videos). Taking the pulse of COVID-19: a spatiotemporal perspective. Mitigating Cache-Based Side-Channel Attacks through Randomization: A Comprehensive System and Architecture Level Analysis. KDD 2022 KDD . Liang Zhao. All papers must be submitted in PDF format using the AAAI-22 author kit. 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. [Best Paper Candidate], Minxing Zhang, Dazhou Yu, Yun Li, Liang Zhao. Submission site:https://openreview.net/group?id=AAAI.org/2022/Workshop/ADAM, Aarti Singh (Carnegie Mellon University), Baskar Ganapathysubramanian (ISU), Chinmay Hegde (New York University; contact: chinmay.h@nyu.edu), Mark Fuge (University of Maryland), Olga Wodo (University of Buffalo), Payel Das (IBM), Soumalya Sarkar (Raytheon), Workshop website:https://adam-aaai2022.github.io/. "A Topic-focused Trust Model for Twitter." Integration of declarative and procedural domain knowledge in learning. Papers will be peer-reviewed and selected for oral and/or poster presentations at the workshop. In spite of substantial research focusing on discovery from news, web, and social media data, its applications to datasets in professional settings such as financial filings and government reports, still present huge challenges. 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). 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. . These models can also generate instant feedback to instructors and help them to improve their teaching effectiveness. Yuyang Gao, Giorgio Ascoli, Liang Zhao. In nearly all applications, reliability, safety, and security of such systems is a critical consideration. Knowledge Discovery and Data Mining. 10, pp. Furthermore, DNNs are data greedy in the context of supervised learning, and not well developed for limited label learning, for instance for semi-supervised learning, self-supervised learning, or unsupervised learning. This workshop has no archival proceedings. 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. Liang Zhao, Junxiang Wang, and Xiaojie Guo. 1, Sec. "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." And considering robustness, input data with noises frequently occur in open-world scenarios, which presents critical challenges for the building of robust AI systems in practice. Please email to Lingfei Wu: lwu@email.wm.edu for any query. "Online and Distributed Robust Regressions under Adversarial Data Corruption", in Proceedings of the IEEE International Conference on Data Mining (ICDM 2017) , regular paper; (acceptance rate: 9.25%), pp. How can the financial services industry balance the regulatory compliance and model governance pressures with adaptive models, Methods to combine scientific knowledge and data to build accurate predictive models, Adaptive experiment design under resource constraints, Learning cheap surrogate models to accelerate simulations, Learning effective representations for structured data, Uncertainty quantification and reasoning tools for decision-making, Explainable AI for both prediction and decision-making, Integrating AI tools into existing workflows, Challenges in applying and deployment of AI in the real-world. With the rapid development of advanced techniques on the intersection between information theory and machine learning, such as neural network-based or matrix-based mutual information estimator, tighter generalization bounds by information theory, deep generative models and causal representation learning, information theoretic methods can provide new perspectives and methods to deep learning on the central issues of generalization, robustness, explainability, and offer new solutions to different deep learning related AI applications.This workshop aims to bring together both academic researchers and industrial practitioners to share visions on the intersection between information theory and deep learning, and their practical usages in different AI applications. For previous workshops held physically, each workshop attracts around 150~300 participants. Note: Mandatory abstract deadline on May 16, 2022 Deadline: ISMIR 2022 ISMIR '22 ​ . Apr 11-14, 2022. We cordially welcome researchers, practitioners, and students from academia and industry who are interested in understanding and discussing how data scarcity and bias can be addressed in AI to participate. Document structure and layout learning and recognition. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Analytical cookies are used to understand how visitors interact with the website. 47, no. https://doi.org/10.1007/s10707-019-00376-9. Held in conjunction with KDD'22 Aug 15, 2022 - Washington DC, USA. By registering, you agree to receive emails from UdeM. 3434-3440, Melbourne, Australia, Aug 2017. Novel mechanisms for eliciting and consuming user feedback, recommender, structured and generative models, concept acquisition, data processing, optimization; HCI and visualization challenges; Analysis of human factors/cognition and user modelling; Design, testing and assessment of IML systems; Studies on risks of interaction mechanisms, e.g., information leakage and bias; Business use cases and applications. Submissions will be assessed based on their novelty, technical quality, significance of impact, interest, clarity, relevance, and reproducibility. Self-supervised learning approaches involving the interaction of speech/audio and other modalities. Amitava Das (Wipro AI Labs; amitava.santu@gmail.com), Workshop Chairs: Amitava Das (Wipro AI Labs) [India], Amit Sheth (University of South Carolina) [USA], Tanmoy Chakraborty (IIIT Delhi) [India], Asif Ekbal (IIT Patna) [India], Chaitanya Ahuja (CMU) [USA], Parth Patwa (UCLA) [USA], Parul Chopra (CMU) [USA], Amrit Bhaskar (ASU) [USA], Nethra Gunti (IIIT Sri City) [USA], Sathyanarayanan R. (IIIT Sri City) [India], Shreyash Mishra (IIIT Sri City) [India], S. Suryavardan (IIIT Sri City) [India], Vishal Pallagani (University of South Carolina), Supplemental workshop site:https://aiisc.ai/defactify/. Accepted papers will not be archived but will be hosted on the workshop website. sup-port vector machine (SVM), decision tree, random forest, etc.) 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. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. All time are 23:59, AoE (Anywhere on Earth), Hongteng Xu (Renmin University of China, hongtengxu@ruc.edu.cn, main contact), Julie Delon (Universit de Paris, julie.delon@u-paris.fr), Facundo Mmoli (Ohio State University, facundo.memoli@gmail.com), Tom Needham (Florida State University, tneedham@fsu.edu). We will include a panel discussion to close the workshop, in which the audience can ask follow up questions and to identify the key AI challenges to push the frontiers in Chemistry. 11-13. We will instead host the accepted papers on this website (https://aka.ms/di-2022) indefinitely. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13.
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