kdd 2022 deadline

We invite workshop participants to submit their original contributions following the AAAI format through EasyChair. Fine tuning a neural network is very time consuming and far from optimal. Submission site:https://cmt3.research.microsoft.com/ITCI2022, Murat Kocaoglu, Chair (Purdue University, mkocaoglu@purdue.edu), Negar Kiyavash (EPFL, negar.kiyavash@epfl.ch), Todd Coleman (UCSD, tpcoleman@ucsd.edu), Supplemental workshop site:https://sites.google.com/view/itci22. For further information, please have a look at the call for contributions. Interpreting and Evaluating Neural Network Robustness. The 11th International Conference on Learning Representations (ICLR 2023), accepted. These complex demands have brought profound implications and an explosion of interest for research into the topic of this workshop, namely building practical AI with efficient and robust deep learning models. 105, no. It has profoundly impacted several areas, including computer vision, natural language processing, and transportation. November 11-17, 2023. Welcome to the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022), which will be held in Chengdu, China on May 16-19, 2022. 10, pp. STGEN: Deep Continuous-time Spatiotemporal Graph Generation. 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. Negar Etemadyrad, Qingzhe Li, Liang Zhao. However, most models and AI systems are built with conservative operating environment assumptions due to regulatory compliance concerns. [Submission deadline extended, June 3] KDD 2022 Workshop on - INFORMS 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). The workshop will focus on the application of AI to problems in cyber-security. Distributed Self-Paced Learning in Alternating Direction Method of Multipliers. System reports will be presented during poster sessions. 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. Submissions are limited to a maximum of four (4) pages, including all content and references, and must be in PDF format. The goal of this workshop is to focus on creating and refining AI-based approaches that (1) process personalized data, (2) help patients (and families) participate in the care process, (3) improve patient participation, (4) help physicians utilize this participation to provide high quality and efficient personalized care, and (5) connect patients with information beyond that available within their care setting. [paper] job seekers, employers, recruiters and job agents. We invite novel contributions following the AAAI-22 formatting guidelines, camera-ready style. Check the deadlines for submitting your application. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities. Call for Papers Document Intelligence Workshop @ KDD 2022 The goal of this workshop is to bring together the optimal transport, artificial intelligence, and structured data modeling, gathering insights from each of these fields to facilitate collaboration and interactions. The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), long paper, (acceptance rate: 19.4%), Beijing, China, accepted. Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior. In addition, any other work on dialog research is welcome to the general technical track. Junxiang Wang, Hongyi Li, Liang Zhao. 689-698, Barcelona, Spain, Dec 2016. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), pp. Eliminating the need to guess the right topology in advance of training is a prominent benefit of learning network architecture during training. This cookie is set by GDPR Cookie Consent plugin. SDU is expected to host 50-60 attendees. The workshop will focus on two thrusts: 1) Exploring how we can leverage recent advances in RL methods to improve state-of-the-art technology for ED; 2) Identifying unique challenges in ED that can help nurture technical innovations and next breakthroughs in RL. Short or position papers of up to 4 pages are also welcome. We will end the workshop with a panel discussion by invited speakers from different fields to enlist future directions. "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." 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! The cookie is used to store the user consent for the cookies in the category "Performance". Advances in IML promise to make AIs more accessible and controllable, more compatible with the values of their human partners and more trustworthy. We invite submission of papers describing innovative research and applications around the following topics. 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. Yuyang Gao, Siyi Gu, Junji Jiang, Sungsoo Ray Hong, Dazhou Yu, and Liang Zhao. Tanmoy Chowdhury, Chen Ling, Xuchao Zhang, Xujiang Zhao, Guangji Bai, Jian Pei, Haifeng Chen, Liang Zhao. 2, no. Novel AI-enabled generative models for system design and manufacturing. The discussion in the workshop can lead to implementing FL solutions that are more accurate, robust and interpretable, and gain the trust of the FL participants. CFP - EasyChair California, United Stes. 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. Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universit de Montral), Elias B. Khalil (University of Toronto), Pashootan Vaezipoor (University of Toronto). 12 (2014): 90-94. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), (acceptance rate: 15.4%), accepted. Neural Networks, (impact factor: 8.05), accepted. We hope this will help bring the communities of data mining and visualization more closely connected. The workshop is being organized by application area or other, panels, invited speakers, interactive, small groups, discussions, presentations. 29, no. DB transactions) to unstructured data (e.g. Data-driven Humanitarian Mapping and Policymaking Research New theory and fundamentals of AI-aided design and manufacturing. However, workshop organizers may set up any archived publication mechanism that best suits their workshop. Transfer learning methods for business document reading and understanding. Detailed information could be found on the website of the workshop. Technology has transformed over the last few years, turning from futuristic ideas into todays reality. considered to be more practical and more related with real-world applications. Malicious attacks for ML models to identify their vulnerability in black-box/real-world scenarios. 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). Data Mining Conference Acceptance Rate. Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. We will accept the extended abstracts of the relevant and recently published work too. Computer Communications, (impact factor: 3.34), Elsevier, vo. Please note that the KDD Cup workshop will have no proceedings and the authors retain full rights to submit or post the paper at any other venue. 1, Sec. sup-port vector machine (SVM), decision tree, random forest, etc.) This AAAI-22 workshop on AI for Decision Optimization (AI4DO) will explore how AI can be used to significantly simplify the creation of efficient production level optimization models, thereby enabling their much wider application and resulting business values.The desired outcome of this workshop is to drive forward research and seed collaborations in this area by bringing together machine learning and decision-making from the lens of both dynamic and static optimization models. 8 pages), short (max. For each accepted paper, at least one author must attend the workshop and present the paper. ETA (expected time-of-arrival) prediction. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Data mining systems and platforms, and their efficiency, scalability, security and privacy. Time Series Clustering in Linear Time Complexity. Algorithms and theories for explainable and interpretable AI models. Deadlines are shown in America/Los_Angeles time. ), 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). Integration of Deep Learning and Relational Learning. Sathappan Muthiah, Patrick Butler, Rupinder Paul Khandpur, Parang Saraf, Nathan Self, Alla Rozovskaya, Liang Zhao, Jose Cadena et al. Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao. Submissions tackling new problems or more than one of the aforementioned topics simultaneously are encouraged. The first achievements in playing these games at super-human level were attained with methods that relied on and exploited domain expertise that was designed manually (e.g. This workshop wants to emphasize on the importance of integrative paradigms for solving the new wave of AI applications. Researchers from related domains are invited to submit papers on recent advanced technologies, resources, tools and challenges for VTU. of Graz), Cynthia Rudin (Duke Univ.) IEEE Transactions on Knowledge and Data Engineerings (TKDE), (impact factor: 6.977), accepted. Self-supervised learning utilizes proxy supervised learning tasks, for example, distinguishing parts of the input signal from distractors, or generating masked input segments conditioned on the unmasked ones, to obtain training data from unlabeled corpora. Each accepted paper presentation will be allocated between 15 and 20 minutes. Submission instructions will be available at the workshop web page. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. Registration in each workshop is required by all active participants, and is also open to all interested individuals. The topics of interest include, but are not limited to: The papers will be presented in poster format and some will be selected for oral presentation. 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. Causality has received significant interest in ML in recent years in part due to its utility for generalization and robustness. SL-VAE: Variational Autoencoder for Source Localization in Graph Information Diffusion. How can we engineer trustable AI software architectures? 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. 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. The 9th International Conference on Learning Representations (ICLR 2021), (acceptance rate: 28.7%), accepted. Checklist for Revising a SIGKDD Data Mining Paper, How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering, https://researcher.watson.ibm.com/researcher/view_group.php?id=144, IEEE International Conference on Big Data (, AAAI Conference on Artificial Intelligence (, IEEE International Conference on Data Engineering (, SIAM International Conference on Data Mining (, Pacific-Asia Conference on Knowledge Discovery and Data Mining (, ACM SIGKDD International Conference on Knowledge discovery and data mining (, European Conference on Machine learning and knowledge discovery in databases (, ACM International Conference on Information and Knowledge Management (, IEEE International Conference on Data Mining (, ACM International Conference on Web Search and Data Mining (, 18.4% (181/983, research track), 22.5% (112/497, applied data science track), 59.1% (107/181, research track), 35.7% (40/112, applied data science track), 17.4% (130/748, research track), 22.0% (86/390, applied data science track), 49.2% (64/130, research track), 41.9% (36/86, applied data science track), 18.1% (142/784, research track), 19.9% (66/331, applied data science track), 49.3% (70/142, research track), 60.1% (40/66, applied data science track), 18.5% (194/1046, overall), 9.1% (95/?, regular paper), ?% (99/?, short paper), 19.8% (188/948, overall), 8.9% (84/?, regular paper), ?% (104/?, short paper), 19.9% (155/778, overall), 9.3% (72/?, regular paper), ?% (83/?, short paper), 19.6% (178/904, overall), 8.6% (78/?, regular paper), ?% (100/?, short paper), 19.6% (202/1031, long paper), 22.7% (107/471, short paper), 21.8% (38/174m applied research), 17% (147/826, long paper), 23% (96/413, short paper), 25% (demo), 34% (industry paper), Short papers are presented at poster sessions, 20% (171/855, long paper), 28% (119/419, short paper), 38% (30/80, demo paper), 23% (160/701, long paper), 24% (55/234, short paper), 54 extended short papers (6 pages), 26% (94/354, research track), 26% (37/143, applied ds track), 15% (23/151, journal track), 27.8% (164/592, overall), 9.8% (58/592, long presentation), 18.1% (107/592, regular), 28.2% (129/458, overall), 9.8% (45/458, long presentation), 18.3% (84/458, regular), 29.6% (91/307, overall), 12.7% (39/307, long presentation), 16.9% (52/307, regular), 40.4% (34/84, long presentation), 59.5% (50/84, short presentation)^, 16.3% (84/514 in which 3 papers are withdrawn/rejected after the acceptance), 28.4% (23/81, long presentation), 71.6% (58/81, short presentation)^, 30% (24/80, long presentation), 70% (56/80, short presentation)^, 29.8% (20/67, long presentation), 70.2% (47/67, short presentation)^, 53.8% (21/39, long presentation), 46.2% (18/39, short presentation)^. In addition, several invited speakers with distinguished professional background will give talks related the frontier topics of GNN. . Atlanta, Georgia, USA . and facilitate discussions and collaborations in developing trustworthy AI methods that are reliable and more acceptable to physicians. The deadline for the submissions is July 31st, 2022 11.59 PM (Anywhere on Earth time). We accept two types of submissions full research papers no longer than 8 pages (including references) and short/poster papers with 2-4 pages. Linguistic analysis of business documents. Moreover, the operational context in which AI systems are deployed necessitates consideration of robustness and its relation to principles of fairness, privacy, and explainability. We are excited to announce our upcoming workshop at KDD 2022 | Washington DC, U.S.: Decision Intelligence and Analytics for Online Marketplaces - Jobs, Ridesharing, Retail, and Beyond. While we are planning an in-person workshop to be held at AAAI-22, we aim to accommodate attendees who may not be able to travel to Vancouver by allowing participation via live virtual invited talks and virtual poster sessions. Transformations in many fields are enabled by rapid advances in our ability to acquire and generate data. The annual ACM SIGMOD/PODS Conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and . Your Style Your Identity: LeveragingWriting and Photography Styles for Drug Trafficker Identification in Darknet Markets over Attributed Heterogeneous Information Network, The Web Conference (WWW 2019), short paper, (acceptance rate: 20%), accepted, 2019. 4701-4707, San Francisco, California, USA, Feb 2017. CS Conference Deadlines - Yanlin Aug 14-18. Autonomous vehicles can share their detected information (e.g., traffic signs, collision events, etc.) "A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks", in Proceedings of the IEEE International Conference on Data Mining (ICDM 2017) , regular paper; (acceptance rate: 9.25%), pp. International Journal of Digital Earth, (impact factor: 3.097), 25 Aug 2020, https://doi.org/10.1080/17538947.2020.1809723. The scope of the workshop includes, but is not limited to, the following areas: We also invite participants to an interactive hack-a-thon. 4. Data Mining and Knowledge Discovery (DMKD), (impact factor: 3.670), accepted. How can we make AI-based systems more ethically aligned? the 33rd Annual Computer Security Applications Conference (ACSAC 2018), (acceptance rate: 20.1%), San Juan, Puerto Rico, USA, Dec 2018, accepted. "Automatic Targeted-Domain Spatiotemporal Event Detection in Twitter." 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. Submissions may consist of up to 4 pages plus one additional page solely for references. 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. 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. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), accepted. Andrew White, University of RochesterDr. Like other systems, ML systems must meet quality requirements. "STED: semi-supervised targeted-interest event detectionin in twitter." Please use ACM Conference templates (two column format). Positive applications of adversarial ML, i.e., adversarial for good. Published March 4, 2023 4:51 a.m. PST. Deep Learning models are at the core of research in Artificial Intelligence research today. Examples of the datasets which may be considered are the DBTex Radiology Mammogram dataset and the Johns Hopkins COVID-19 case reports. How can we characterize or evaluate AI systems according to their potential risks and vulnerabilities? 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. Papers will be peer-reviewed and selected for spotlight and/or poster presentation at the workshop. Some existing research also presents that there is a trade-off between the robustness and accuracy of deep learning models.

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