kdd 2022 deadline

SIAM International Conference on Data Mining (SDM 2022), (Acceptance Rate: 26%), accepted. Submissions may consist of up to 7 pages of technical content plus up to two additional pages solely for references. 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). Zirui Xu, Fuxun Xu, Liang Zhao, and Xiang Chen. Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. Welcome to the home of the 2023 ACM SIGMOD/PODS Conference, to be held in the Seattle metropolitan area, Washington, USA, on June 18 - June 23, 2023. Please keep your paper format according to AAAI Formatting Instructions (two-column format). Integrated syntax and semantic approaches for document understanding. This topic also encompasses techniques that augment or alter the network as the network is trained. ACM Computing Surveys (CSUR), (impact factor: 10.28), accepted. The review process will be single blind. 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. Deep Graph Transformation for Attributed, Directed, and Signed Networks. At the AAAI 2022 Workshop on Video Transcript Understanding (VTU @ AAAI 2022), we aim to bring together researchers from various domains to make the best of the knowledge that all these videos contain. RAISAs systems-level perspective will be emphasized via three main thrusts: AI threat modeling, AI system robustness, explainable AI, system lifecycle attacks, system verification and validation, robustness benchmarks and standards, robustness to black-box and white-box adversarial attacks, defenses against training, operational and inversion attacks, AI system confidentiality, integrity, and availability, AI system fairness and bias. Self-supervised learning (SSL) has shown great promise in problems involving natural language and vision 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/. 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. Topics include, but our not limited to: learning optimization models from data, constraint and objective learning, AutoAI, especially if combined with decision optimization models or environments, AutoRL, incorporating the inaccuracy of the automatically learnt models in the decision making process, and using machine learning to efficiently solve combinatorial optimization models. Participation of researchers from a wide variety of areas is encouraged, including Data Science, Machine Learning, Symbolic AI, Mathematical programming, Constraint Optimization, Reinforcement Learning, Dynamic control and Operations Research. algorithms applied to the above topics: deep learning, reinforcement learning, multi-armed bandits, causal inference, mathematical programming, and stochastic optimization. Linear Time Complexity Time Series Clustering with Symbolic Pattern Forest. Multi-objective Deep Data Generation with Correlated Property Control. IBM Research, 2018. We expect 50~75 participants and potentially more according to our past experiences. CPM: A General Feature Dependency Pattern Mining Framework for Contrast Multivariate Time Series. The ability to read, understand and interpret these documents, referred to here as Document Intelligence (DI), is challenging due to their complex formats and structures, internal and external cross references deployed, quality of scans and OCR performed, and many domains of knowledge involved. 2999-3006, New Orleans, US, Feb 2018. The review process is double-blind, and we follow the Conflict of Interest Policy for ACM Publications. 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. KDD 2022 : 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Conference Series : Knowledge Discovery and Data Mining Link: https://kdd.org/kdd2022/ Call For Papers [Empty] Related Resources KDD 2023 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING 2022. SL-VAE: Variational Autoencoder for Source Localization in Graph Information Diffusion. Award for Artificial Intelligence for the Benefit of Humanity, Patrick Henry Winston Outstanding Educator Award, A Report to ARPA on Twenty-First Century Intelligent Systems, The Role of Intelligent Systems in the National Information Infrastructure, Code of Conduct for Conferences and Events, Request to Reproduce Copyrighted Materials, AAAI Conference on Artificial Intelligence, W1: Adversarial Machine Learning and Beyond, W2: AI for Agriculture and Food Systems (AIAFS), W6: AI in Financial Services: Adaptiveness, Resilience & Governance, W7: AI to Accelerate Science and Engineering (AI2ASE), W8: AI-Based Design and Manufacturing (ADAM) (Half-Day), W9: Artificial Intelligence for Cyber Security (AICS)(2-Day), W10: Artificial Intelligence for Education (AI4EDU), W11: Artificial Intelligence Safety (SafeAI 2022)(1.5-Day), W12: Artificial Intelligence with Biased or Scarce Data, W13: Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations (CLeaR), W14: Deep Learning on Graphs: Methods and Applications (DLG-AAAI22), W15: DE-FACTIFY :Multi-Modal Fake News and Hate-Speech Detection, W16: Dialog System Technology Challenge (DSTC10), W17: Engineering Dependable and Secure Machine Learning Systems (EDSMLS 2022) (Half-Day), W18: Explainable Agency in Artificial Intelligence, W19: Graphs and More Complex Structures for Learning and Reasoning (GCLR), W21: Human-Centric Self-Supervised Learning (HC-SSL), W22: Information-Theoretic Methods for Causal Inference and Discovery (ITCI22), W23: Information Theory for Deep Learning (IT4DL), W25: Knowledge Discovery from Unstructured Data in Financial Services (Half-Day), W26: Learning Network Architecture during Training, W27: Machine Learning for Operations Research (ML4OR) (Half-Day), W28: Optimal Transport and Structured Data Modeling (OTSDM), W29: Practical Deep Learning in the Wild (PracticalDL2022), W30: Privacy-Preserving Artificial Intelligence, W31: Reinforcement Learning for Education: Opportunities and Challenges, W32: Reinforcement Learning in Games (RLG), W33: Robust Artificial Intelligence System Assurance (RAISA) (Half-Day), W34: Scientific Document Understanding (SDU) (Half-Day), W35: Self-Supervised Learning for Audio and Speech Processing, W36: Trustable, Verifiable and Auditable Federated Learning, W38: Trustworthy Autonomous Systems Engineering (TRASE-22), W39: Video Transcript Understanding (Half-Day), https://openreview.net/group?id=AAAI.org/2022/Workshop/AdvML, https://openreview.net/group?id=AAAI.org/2022/Workshop/AIAFS, https://easychair.org/conferences/?conf=aaai-2022-workshop, https://rail.fzu.edu.cn/info/1014/1064.htm, https://aaai.org/Conferences/AAAI-22/aaai22call/, https://sites.google.com/view/aaaiwfs2022, https://www.aaai.org/Publications/Templates/AuthorKit22.zip, https://openreview.net/group?id=AAAI.org/2022/Workshop/ADAM, https://easychair.org/conferences/?conf=aics22, https://cmt3.research.microsoft.com/AIBSD2022, https://aibsdworkshop.github.io/2022/index.html, https://openreview.net/forum?id=6uMNTvU-akO, https://easychair.org/conferences/?conf=dlg22, https://deep-learning-graphs.bitbucket.io/dlg-aaai22/, https://cmt3.research.microsoft.com/DSTC102022, https://dstc10.dstc.community/calls_1/call-for-workshop-papers, https://easychair.org/my/conference?conf=edsmls2022, https://sites.google.com/view/edsmls-2022/home, https://sites.google.com/view/eaai-ws-2022/call, https://sites.google.com/view/eaai-ws-2022/topic, https://sites.google.com/view/gclr2022/submissions, https://cmt3.research.microsoft.com/AAAI2022HCSSL/Submission/Index, https://cmt3.research.microsoft.com/ITCI2022, https://easychair.org/conferences/?conf=it4dl, https://easychair.org/conferences/?conf=imlaaai22, https://sites.google.com/view/aaai22-imlw, https://easychair.org/conferences/?conf=kdf22, Learning Network Architecture During Training, https://cmt3.research.microsoft.com/OTSDM2022, https://cmt3.research.microsoft.com/PracticalDL2022, https://cmt3.research.microsoft.com/PPAI2022, https://easychair.org/conferences/?conf=rl4edaaai22, https://sites.google.com/view/raisa-2022/, https://sites.google.com/view/sdu-aaai22/home, https://cmt3.research.microsoft.com/SAS2022, https://easychair.org/conferences/?conf=fl-aaai-22, http://federated-learning.org/fl-aaai-2022/, https://cmt3.research.microsoft.com/TAIH2022, https://easychair.org/conferences/?conf=trase2022, https://easychair.org/my/conference?conf=vtuaaai2022, Symposium on Educational Advances in Artificial Intelligence (EAAI-22), Conference on Innovative Applications of Artificial Intelligence (IAAI-22). 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. Liang Zhao, Junxiang Wang, and Xiaojie Guo. Conference Deadlines - I.timyang.vip In addition, any other work on dialog research is welcome to the general technical track. Submissions tackling new problems or more than one of the aforementioned topics simultaneously are encouraged. AI is now shaping the way businesses, governments, and educational institutions do things and is making its way into classrooms, schools and districts across many countries. Deadlines are shown in America/Los_Angeles time. November 11-17, 2023. We invite the submission of original and high-quality research papers in the topics related to biased or scarce data. 10 (2014): e110206. Contrast Pattern Mining in Paired Multivariate Time Series of Controlled Driving Behavior Experiment. Deadline in . ACM Transactions on Spatial Algorithms and Systems (TSAS), 5, 3, Article 19 (September 2019), 28 pages. Creative Commons Attribution-Share Alike 3.0 License, 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 25TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, Knowledge Discovery and Data Mining Conference, 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 21th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 18th ACM SIGKDD Knowledge Discovery and Data Mining, The 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. It is a forum to bring attention towards collecting, measuring, managing, mining, and understanding multimodal disinformation, misinformation, and malinformation data from social media. All papers must be submitted in PDF format, using the AAAI-22 author kit. Please submit the papers and system reports toEasyChair, Thien Huu Nguyen (University of Oregon, thien@cs.uoregon.edu), Walter Chang (Adobe Research, wachang@adobe.com), Amir Pouran Ben Veyseh (University of Oregon, apouranb@uoregon.edu), Viet Dac Lai (University of Oregon, viet@uoregon.edu), Franck Dernoncourt (Adobe Research, franck.dernoncourt@adobe.com), Workshop URL:https://sites.google.com/view/sdu-aaai22/home. AAAI, specifically, is a great venue for our workshop because its audience spans many ML and AI communities. Respect official deadlines - Universit de Montral The accepted papers will be posted on the workshop website and will not appear in the AAAI proceedings. Advances in IML promise to make AIs more accessible and controllable, more compatible with the values of their human partners and more trustworthy. Like other systems, ML systems must meet quality requirements. We will also select some of the best posters for spotlight talks (2 minutes each). Papers more suited for a poster, rather than a presentation, would be invited for a poster session. 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. The workshop also welcomes participants of SUPERB and Zero Speech challenge to submit their results. In nearly all applications, reliability, safety, and security of such systems is a critical consideration. CFP - EasyChair The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. Neil T. Heffernan, Worcester Polytechnic Institute (Worcester, MA, USA), Andrew S. Lan, University of Massachusetts Amherst (Amherst, MA, USA), Anna N. Rafferty, Carleton College (Northfield, MN, USA), Adish Singla, Max Planck Institute for Software Systems (Saarbrucken, Germany). 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. 40 attendees including: invited speakers, authors of accepted papers and shared task participants. Shuo Lei, Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu. 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. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. Previous healthcare-related workshops focus on how to develop AI methods to improve the accuracy and efficiency of clinical decision-making, including diagnosis, treatment, triage. A primary reason for this is the inherent long-tailed nature of our world, and the need for algorithms to be trained with large amounts of data that includes as many rare events as possible. Submissions should follow the AAAI 2022 formatting guidelines and the AAAI 2022 standards for double-blind review including anonymous submission. 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. NOTE: Mandatory abstract deadline: 2022-08-08 Deadline: AAAI 157. FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers. Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. Virtual . Attendance is open to all registered participants. 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. 2022. Research track papers reporting the results of ongoing or new research, which have not been published before. 2020. World Wide Web Conference (WWW 2018), (acceptance rate: 14.8%), Lyon, FR, Apr 2018, accepted. We will also organize 3 shared tasks in this workshop: punctuation restoration, domain adaptation for punctuation restoration, and chitchat detection. We consider submissions that havent been published in any peer-reviewed venue (except those under review). Deadline: FSE 2023. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. All the submissions must follow the AAAI-22 formatting guidelines, camera-ready style. How can we engineer trustable AI software architectures? Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! Given the ever-increasing role of the World Wide Web as a source of information in many domains including healthcare, accessing, managing, and analyzing its content has brought new opportunities and challenges. 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. arXiv preprint arXiv:2002.11867 (2021), Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao. Pakdd 2022 Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang. Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data. The KDD 2022 program promises to be the most robust and diverse to date, with keynote presentations, industry-led sessions, workshops, and tutorials spanning a wide range of topics - from data-driven humanitarian mapping and applied data science in healthcare to the uses of artificial intelligence (AI) for climate mitigation and decision . Full papers: Submissions must represent original material that has not appeared elsewhere for publication and that is not under review for another refereed publication. This workshop wants to emphasize on the importance of integrative paradigms for solving the new wave of AI applications. Visualization is an integral part of data science, and essential to enable sophisticated analysis of data. 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." Although machine learning (ML) approaches have demonstrated impressive performance on various applications and made significant progress for AI, the potential vulnerabilities of ML models to malicious attacks (e.g., adversarial/poisoning attacks) have raised severe concerns in safety-critical applications. "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. Papers will be peer-reviewed by the Program Committee (2-3 reviewers per paper). Optimal transport-based analysis of structured data, such as networks, meshes, sequences, and so on; The applications of optimal transport in molecule analysis, network analysis, natural language processing, computer vision, and bioinformatics. "Knowledge-enhanced Neural Machine Reasoning: A Review." 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. ), Graduate (master's, specialized graduate diploma (DESS), etc. 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/. ICONF Han Wang, Hossein Sayadi, Avesta Sasan, Houman Homayoun, Liang Zhao, Tinoosh Mohsenin, Setareh Rafatirad. 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. Please note as per the KDD Call for Workshop Proposals: Note: Workshop papers will not be archived in the ACM Digital Library. The automated processing of unstructured data to discover knowledge from complex financial documents requires a series of techniques such as linguistic processing, semantic analysis, and knowledge representation & reasoning. 2020. Inspired by the question, there is a trend in the machine learning community to adopt self-supervised approaches to pre-train deep networks. Programming Languages, Domain specific languages, Libraries and software tools for integration of various learning and reasoning paradigms. 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. The consideration and experience of adversarial ML from industry and policy making. Babies learn their first language through listening, talking, and interacting with adults. 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. The goal of ITCI22 is to bring together researchers working at the intersection of information theory, causal inference and machine learning in order to foster new collaborations and provide a venue to brainstorm new ideas, exemplify to the information theory community causal inference and discovery as an application area and highlight important technical challenges motivated by practical ML problems, draw the attention of the wider machine learning community to the problems at the intersection of causal inference and information theory, and demonstrate to the community the utility of information-theoretic tools to tackle causal ML problems. 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