Molecules, (impact factor: 4.411), accepted. SDU will also host a session for presenting the short research papers and the system reports of the shared tasks. The submitted papers written in English must be in PDF format according to the AAAI camera ready style. This proposed workshop will build upon successes and learnings from last years successful AI for Behavior Change workshop, and will focus on on advances in AI and ML that aim to (1) design and target optimal interventions; (2) explore bias and equity in the context of decision-making and (3) exploit datasets in domains spanning mobile health, social media use, electronic health records, college attendance records, fitness apps, etc. Funeral for Nadine Girault will take place Saturday | CTV News Liang Zhao, Feng Chen, and Yanfang Ye. Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, Bill Wuest, Amarda Shehu, Liang Zhao. Optimal transport theory, including statistical and geometric aspects; Gromov-Wasserstein distance and its variants; Bayesian inference for/with optimal transport; Gromovization of machine learning methods; Optimal transport-based generative modeling. Papers will be peer-reviewed and selected for spotlight and/or poster presentation. 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. ISBN: 978-981-16-6053-5. 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/. 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. All deadlines are at 11:59 PM anytime in the world. Neurocomputing (Impact Factor: 5.719), accepted. Paper Submission Deadline: May 26, 2022 Author Notification: June 20, 2022 Camera Ready: July 9, 2022 Workshop: August . in the proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), (acceptance rate: 26%), pp. Onn Shehory, Bar Ilan University (onn.shehory@biu.ac.il), Eitan Farchi, IBM Research Haifa (farchi@il.ibm.com), Guy Barash, Western Digital (Guy.Barash@wdc.com), Supplemental workshop site:https://sites.google.com/view/edsmls-2022/home. ), The workshop will be organized as half-day event with 2 invited speakers, follow by presentation from accepted papers (both ordinary papers, and shared task paper). The workshop will be a one-day workshop, featuring speakers, panelists, and poster presenters from machine learning, biomedical informatics, natural language processing, statistics, behavior science. This policy also applies to papers that overlap substantially in technical content with papers previously published, accepted, or under review. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Novel methods to learn from scarce/sparse, or heterogenous, or multimodal data. The workshop will be a one-day meeting and will include a number of technical sessions, a virtual poster session where presenters can discuss their work, with the aim of further fostering collaborations, multiple invited speakers covering crucial challenges for the field of privacy-preserving AI applications, including policy and societal impacts, a tutorial talk, and will conclude with a panel discussion. We accept two types of submissions full research papers no longer than 8 pages (including references) and short/poster papers with 2-4 pages. Explainable Agency captures the idea that AI systems will need to be trusted by human agents and, as autonomous agents themselves must be able to explain their decisions and the reasoning that produced their choices (Langley et al., 2017). 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. 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. 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/. SIGKDD Explorations, Vol. Transformations in many fields are enabled by rapid advances in our ability to acquire and generate data. We invite submissions of full papers, as well as works-in-progress, position papers, and papers describing open problems and challenges. in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019. Note: Mandatory abstract deadline on May 16, 2022 Deadline: ISMIR 2022 ISMIR '22 ​ . the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018) (acceptance rate: 20.6%), Stockholm, Sweden, Jul 2018, accepted. VDS will bring together domain scientists and methods researchers (including data mining, visualization, usability and HCI, data management, statistics, machine learning, and software engineering) to discuss common interests, talk about practical issues, and identify open research problems in visualization in data science. Continuous V&V and predictability of AI safety properties, Runtime monitoring and (self-)adaptation of AI safety, Accountability, responsibility and liability of AI-based systems, Avoiding negative side effects in AI-based systems, Role and effectiveness of oversight: corrigibility and interruptibility, Loss of values and the catastrophic forgetting problem, Confidence, self-esteem and the distributional shift problem, Safety of AGI systems and the role of generality, Self-explanation, self-criticism and the transparency problem, Regulating AI-based systems: safety standards and certification, Human-in-the-loop and the scalable oversight problem, Experiences in AI-based safety-critical systems, including industrial processes, health, automotive systems, robotics, critical infrastructures, among others. Atlanta, Georgia, USA . ICONF and deep learning techniques (e.g. KDD 2022 KDD . 76, pp. The trustworthy issues of clinical AI methods were not discussed. Would you like to mark this message as the new best answer? Submit to:https://cmt3.research.microsoft.com/AIBSD2022, Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories, kp388@cornell.edu), Ziyan Wu (UII America, Inc., wuzy.buaa@gmail.com), Supplemental workshop site:https://aibsdworkshop.github.io/2022/index.html. Generative Adversarial Learning of Protein Tertiary Structures. It will start with a 60-minute mini-tutorial covering the basics of RL in games, and will include 2-4 invited talks by prominent contributors to the field, paper presentations, a poster session, and will close with a discussion panel. References will not count towards the page limit. The consideration and experience of adversarial ML from industry and policy making. ML4OR is a one-day workshop consisting of a mix of events: multiple invited talks by recognized speakers from both OR and ML covering central theoretical, algorithmic, and practical challenges at this intersection; a number of technical sessions where researchers briefly present their accepted papers; a virtual poster session for accepted papers and abstracts; a panel discussion with speakers from academia and industry focusing on the state of the field and promising avenues for future research; an educational session on best practices for incorporating ML in advanced OR courses including open software and data, learning outcomes, etc. The submissions must follow the formatting guidelines for AAAI-22. Time Series Clustering in Linear Time Complexity. These choices can only be analyzed holistically if the technological and ethical perspectives are integrated into the engineering problem, while considering both the theoretical and practical challenges of AI safety. In addition, authors can provide an optional one page supplement at the end of their submitted paper (it needs to be in the same PDF file) focused on reproducibility. 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. We also use third-party cookies that help us analyze and understand how you use this website. 2022. Submissions including full papers (6-8 pages) and short papers (2-4 pages) should be anonymized and follow the AAAI-22 Formatting Instructions (two-column format) at https://www.aaai.org/Publications/Templates/AuthorKit22.zip. IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 24.31), accepted. Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao. We invite participants to submit papers by the 12th of November, based on but not limited to, the following topics: RL in various formalisms: one-shot games, turn-based, and Markov games, partially-observable games, continuous games, cooperative games; deep RL in games; combining search and RL in games; inverse RL in games; foundations, theory, and game-theoretic algorithms for RL; opponent modeling; analyses of learning dynamics in games; evolutionary methods for RL in games; RL in games without the rules; search and planning; and online learning in games. Connor Coley, Massachusetts Institute of TechnologyProf. KDD 2022 We also invite papers that have been published at other venues to spark discussions and foster new collaborations. This thread already has a best answer. Deep Spatial Domain Generalization. Papers will be peer-reviewed and selected for spotlight and/or poster presentation at the workshop. ), responsible development of human-centric SSL (e.g., safety, limitations, societal impacts, and unintended consequences), ethical and legal implications of using SSL on human-centric data, implications of SSL on robustness and fairness, implications of SSL on privacy and security, interpretability and explainability of human-centric SSL frameworks, if your work broadly addresses the use of unlabeled human-centric data with unsupervised or semi-supervised learning, if your work focuses on architectures and frameworks for SSL for sensory data beyond CV and NLP (but not necessarily human-centric data). Typically, we receive around 40~60 submissions to each previous workshop. Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao. Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! Cleansing and image enhancement techniques for scanned documents. It is also central for tackling decision-making problems such as reinforcement learning, policy or experimental design. Proceedings of the ACM on Human-Computer Interaction (CSCW 2022), to appear, 2022. In the financial services industry particularly, a large amount of financial analysts work requires knowledge discovery and extraction from different data sources, such as SEC filings and industry reports, etc., before they can conduct any analysis. Poster session: One poster session of all accepted papers which leads for interaction and personal feedback to the research. Recent years have witnessed growing interest in human and AI systems with the increasing realisation that machines can indeed meet objectives specified but the real question becomes have they been given the right objectives. 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. At least one author of each accepted submission must register and present their paper at the workshop. Microsoft Research CMT: https://cmt3.research.microsoft.com/DI2022, https://document-intelligence.github.io/DI-2022/ or https://aka.ms/di-2022, Workshop registration will be processed with the main KDD 2022 conference: https://kdd.org/kdd2022/, Standard ACM Conference Proceedings Template, Conflict of Interest Policy for ACM Publications, https://cmt3.research.microsoft.com/DI2022, https://document-intelligence.github.io/DI-2022/, Second Document Intelligence Workshop @ KDD 2021, First Document Intelligence Workshop @ NeurIPS 2019, Hamid Motahari, Nigel Duffy, Paul Bennett, and Tania Bedrax-Weiss. 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. Because of the time needed to complete the formalities for entering Canada and Quebec, the admission period for international applicants ends several weeks before the session begins. Amir A. Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng. Please note that foreign students must allow for 3 to 6 months to complete all the formalities required to study in Canada. We allow papers that are concurrently submitted to or currently under review at other conferences or venues. Journal of Biomedical Semantics, (impact factor: 1.845), 2018, accepted. The accepted papers will be allocated either a contributed talk or a poster presentation. All the workshop chairs, most of the Committees, and the authors of the accepted papers will attend the workshop also. Registration in each workshop is required by all active participants, and is also open to all interested individuals. KDD 2022 is a dual-track conference that provides distinct programming in research and applied data science. ^All accepted WSDM papers are associated with an interactive poster presentation in addition to oral presentations. "A Uniform Representation for Trajectory Learning Tasks", 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2017), short paper, DOI=10.1145/3139958.3140017, Redondo Beach, CA, USA, Nov 2017. Using a social media account will simply make the application process easier: none of your activities on this site will be posted to your profile. Submission link:https://easychair.org/cfp/raisa-2022, William Streilein, MIT Lincoln Laboratory, 244 Wood St., Lexington, MA, 02420, (781) 981-7200, wws@ll.mit.edu, Olivia Brown (MIT Lincoln Laboratory, Olivia.Brown@ll.mit.edu), Rajmonda Caceres (MIT Lincoln Laboratory, Rajmonda.Caceres@ll.mit.edu), Tina Eliassi-Rad (Northeastern University, teliassirad@northeastern.edu), David Martinez (MIT Lincoln Laboratory, dmartinez@ll.mit.edu), Sanjeev Mohindra (MIT Lincoln Laboratory, smohindra@ll.mit.edu), Elham Tabassi (National Institute of Standards and Technology, elham.tabassi@nist.gov), Workshop URL:https://sites.google.com/view/raisa-2022/. Apr 11-14, 2022. Out of these, around 20~30 papers are accepted. The workshop will focus on the application of AI to problems in cyber-security. We collaborate with Saudi Aramco to use machine learning for simulating oil and water flows, . In this 2nd instance of GCLR (Graphs and more Complex structures for Learning and Reasoning) workshop, we will focus on various complex structures along with inference and learning algorithms for these structures. Xiaojie Guo, Yuanqi Du, Liang Zhao. Finally, there is an increasing interest in AI in moving beyond traditional supervised learning approaches towards learning causal models, which can support the identification of targeted behavioral interventions. Big Data 2022 December 13-16, 2022. Information-theoretic approaches provide a novel set of tools that can expand the scope of classical approaches to causal inference and discovery problems in a variety of applications. KDD: Knowledge Discovery and Data Mining 2024 2023 2022 - WikiCFP Integration of Deep Learning and Relational Learning. Xiaosheng Li, Jessica Lin, Liang Zhao. In light of these issues, and the ever-increasing pervasiveness of AI in the real world, we seek to provide a focused venue for academic and industry researchers and practitioners to discuss research challenges and solutions associated with building AI systems under data scarcity and/or bias. Submissions tackling new problems or more than one of the aforementioned topics simultaneously are encouraged. Undergraduate (bachelor's, certificate, etc. Causality has received significant interest in ML in recent years in part due to its utility for generalization and robustness. 507-516, Singapore, Nov 2017. Cesa Salaam (Howard University, USA), Hwanhee Lee (Seoul National University, South Korea), Jaemin Cho (University of North Carolina at Chapel Hill, USA), Jielin Qiu (Carnegie Mellon University, USA), Joseph Barrow (University of Maryland, US), Mengnan Du (Texas A&M University, USA), Minh Van Nguyen (University of Oregon, USA), Nicole Meister (Princeton University, USA), Sajad Sotudeh Gharebagh (Georgetown University, USA), Sampreeth Chebolu (University of Houston, USA), Sarthak Jain (Northeastern University, USA),Shufan Wang (University of Massachusetts Amherst, USA), Supplemental Workshop site:https://vtuworkshop.github.io/2022/, https://research.ibm.com/haifa/Workshops/AAAI-22-AI4DO/. https://doi.org/10.1007/s10707-019-00376-9. Neural Networks, (impact factor: 8.05), accepted. text, images, and videos). Submissions should follow the AAAI-2022https://aaai.org/Conferences/AAAI-22/aaai22call/. 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. "STED: semi-supervised targeted-interest event detectionin in twitter." This cookie is set by GDPR Cookie Consent plugin. Poster/short/position papers submission deadline: Nov 5, 2021Full paper submission deadline: Nov 5, 2021Paper notification: Dec 3, 2021. Deep Generative Model for Periodic Graphs. Andrew White, University of RochesterDr. Workshop registration is available to AAAI-22 technical registrants at a discounted rate, or separately to workshop only registrants. This date takes priority over those shown below and could be extended for some programs. Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. considered to be more practical and more related with real-world applications. Liang Zhao, Jiangzhuo Chen, Feng Chen, Fang Jin, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. At the AAAI-22 Workshop on Scientific Document Understanding (SDU@AAAI-22), we aim to gather insights into the recent advances and remaining challenges on scientific document understanding. ASPLOS 2023 will be moving to three submission deadlines. What are the primary lessons learned from the model failures? We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. 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." 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. Deadline in your local America/New_York timezone: Deadline in timezone from conference website: DASFAA 2022. of London). Please refer to the KDD 2022 website for the policies of Conflict of Interest, Violations of Originality, and Dual Submission: A Best Paper Award will be presented to the best full paper as voted by the reviewers. Junxiang Wang, Zheng Chai, Yue Cheng, and Liang Zhao. The bottleneck to discovery is now our ability to analyze and make sense of heterogeneous, noisy, streaming, and often massive datasets. 3, pp. [Best Paper Award Shortlist]. AD Conference Deadlines This one-day workshop will bring concentrated discussions on self-supervision for the field of speech/audio processing via keynote speech, invited talks, contributed talks and posters based on community-submitted high-quality papers, and the result representation of SUPERB and Zero Speech challenge. 14, 2022: The information of Keynote Speakers is available at, Apr. 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. Deep learning and statistical methods for data mining. For general inquiries about AI2ASE, please write to the lead organizer aryan.deshwal@wsu.edu or jana.doppa@wsu.edu. Liang Zhao, Feng Chen, Jing Dai, Ting Hua, Chang-Tien Lu, and Naren Ramakrishnan. in Proceedings of the 24st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018), research track (acceptance rate: 18.4%), London, United Kingdom, Aug 2018, accepted. 625-634, New Orleans, US, Dec 2017. This cookie is set by GDPR Cookie Consent plugin. May 8, 2022: Student Travel Awards announcement is, Apr. Online Flu Epidemiological Deep Modeling on 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. Combating fake news is one of the burning societal crises. Check the CFP for details Deadline: ICDM 2020 . Continuous refinement of AI models using active/online learning. Yujie Fan, Yiming Zhang, Shifu Hou, Lingwei Chen, Yanfang Ye, Chuan Shi, Liang Zhao, Shouhuai Xu. 2022. ETA (expected time-of-arrival) prediction. IEEE, 2014. Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, and Chang-Tien Lu. Knowledge representation for business documents. Papers will be submitted to OpenReview system: Waiting for approval,https://openreview.net/forum?id=6uMNTvU-akO, Workshop Chair:Parisa Kordjamshidi, +1-2174187004, kordjams@msu.edu, Organizing Committee:Parisa Kordjamshidi (Michigan State University, kordjams@msu.edu), Behrouz Babaki (Mila/HEC Montreal, behrouz.babaki@mila.quebec), Sebastijan Dumani (KU Leuven, sebastijan.dumancic@cs.kuleuven.be), Alex Ratner (University of Washington, ajratner@cs.washington.edu), Hossein Rajaby Faghihi (Michigan State University, rajabyfa@msu.edu), Hamid Karimian (Michigan State University, karimian@msu.edu), Organizing Committee:Dan Roth (University of Pennsylvania, danroth@seas.upenn.edu) and Guy Van Den Broeck (University of California Los Angeles, guyvdb@cs.ucla.edu), Supplemental workshop site:https://clear-workshop.github.io.

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