There is a need for the research community to develop novel solutions for these practical issues. Accepted papers will be given the opportunity to present at the spotlight sessions during the workshop. Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Liang Zhao. The post-lunch session will feature a second keynote talk, two invited talks. This workshop brings together researchers from diverse backgrounds with different perspectives to discuss languages, formalisms and representations that are appropriate for combining learning and reasoning. Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2020), (acceptance rate: 20.6%), accepted. We hope to build upon that success. ACM RecSys 2022 will be held in Seattle, USA, from September 18 - 23, 2022. All papers will be peer reviewed, single-blinded. We especially welcome research from fields including but not limited to AI, human-computer interaction, human-robot interaction, cognitive science, human factors, and philosophy. 40, no. Papers more suited for a poster, rather than a presentation, would be invited for a poster session. Track 2 focuses on the state of the art advances in the computational jobs marketplace. The main objective of the workshop is to bring researchers together to discuss ideas, preliminary results, and ongoing research in the field of reinforcement in games. Complex systems are often characterized by several components that interact in multiple ways among each other. In addition, any other work on dialog research is welcome to the general technical track. 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. Scientists and engineers in diverse domains are increasingly relying on using AI tools to accelerate scientific discovery and engineering design. This workshop seeks to explore new ideas on AI safety with particular focus on addressing the following questions: Contributions are sought in (but are not limited to) the following topics: To deliver a truly memorable event, we will follow a highly interactive format that will include invited talks and thematic sessions. The paper submissions must be in pdf format and use the AAAI official templates. Deadline in . This workshop will encourage researchers from interdisciplinary domains working on multi-modality and/or fact-checking to come together and work on multimodal (images, memes, videos etc.) Proposals of technical talk (up to one-page abstract including short Bio of the main speaker). In recent years, various information theoretic principles have also been applied to different deep learning related AI applications in fruitful and unorthodox ways. Liyan Xu, Xuchao Zhang, Zong Bo, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho Choi. July 22: The workshop Programis up! Inspired by the question, there is a trend in the machine learning community to adopt self-supervised approaches to pre-train deep networks. a tutorial on how to structure data mining papers by Prof. Xindong Wu (University of Louisiana at Lafayette). Hosein Mohammadi Makrani, Farnoud Farahmand, Hossein Sayadi, Sara Bondi, Sai Manoj Pudukotai Dinakarrao, Liang Zhao, Avesta Sasan, Houman Homayoun, and Setareh Rafatirad,. Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data. Make sure your desired study programs are open for admission in the session when you would like to start your studies. Despite rapid recent progress, it has proven to be challenging for Artificial Intelligence (AI) algorithms to be integrated into real-world applications such as autonomous vehicles, industrial robotics, and healthcare.
RecSys 2022 - Important Dates - RecSys The workshop will include original contributions on theory, methods, systems, and applications of data mining, machine learning, databases, network theory, natural language processing, knowledge representation, artificial intelligence, semantic web, and big data analytics in web-based healthcare applications, with a focus on applications in population and personalized health. DOI:https://doi.org/10.1145/3339823. We will also have a video component for remote participation. We will receive the paper on the CMT system. Estimate of the audience size: 400-500 attendees (based on the number of attendees in previous DLG workshops in KDD19, AAAI20, KDD20 and AAAI21). SIGSPATIAL Special (invited paper), vo. As deep learning problems become increasingly complex, network sizes must increase and other architectural decisions become critical to success. 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. 1, 2022: Call For Paper: The Undergraduate Consortium at SIGKDD 2022 is available at, Mar. Share. 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. SIGMOD 2022 adheres to the ACM Policy Against Harassment. The workshop attracted about 100 attendees. [Bests of ICDM], Zheng Zhang and Liang Zhao. : Papers must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template. Moreover, the operational context in which AI systems are deployed necessitates consideration of robustness and its relation to principles of fairness, privacy, and explainability. There were two workshops on similar topics hosted at ICML 2020 and NeurIPS 2020, and both workshops observed positive feedback and overwhelming participation. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. 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.
Long talks (50 mins):Gabriel Peyr, (Mathematics, CNRS Senior Researcher);Yusu Wang, (Mathematics, Professor in CSE, UCSD);Caroline Uhler, (Statistics and CS, Associate Professor in EECS and IDSS, MIT); Short talks (25mins):Titouan Vayer, (Mathematics, Postdoctoral Researcher at ENS Lyon);Tam Le, (Computer Science, Research Scientist at RIKEN);Dixin Luo, (Computer Science, Assistant Professor in CS, Beijing Institute of Technology). Data mining systems and platforms, and their efficiency, scalability, security and privacy. At least one author of each accepted submission must register and present their paper at the workshop. 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. the 33rd Annual Computer Security Applications Conference (ACSAC 2018), (acceptance rate: 20.1%), San Juan, Puerto Rico, USA, Dec 2018, accepted. Please note as per the KDD Call for Workshop Proposals: Note: Workshop papers will not be archived in the ACM Digital Library. At least one author of each accepted submission must be present at the workshop. Transformations in many fields are enabled by rapid advances in our ability to acquire and generate data. We encourage authors to contact the organizers to discuss possible overlap. The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". Submissions are limited to 4 pages, not including references. This website uses cookies to improve your experience while you navigate through the website. Zero Speech challenge is to build language models only based on audio or audio-visual information, without using any textual input. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. The 19th International Conference on Data Mining (ICDM 2019), long paper, (acceptance rate: 9.08%), Beijing, China.
Liang Gou, Bosch Research (IEEE VIS liaison), Claudia Plant, University of Vienna (KDD liaison), Alvitta Ottley, Washington University, St. Louis, Junming Shao, University of Electronic Science and Technology of China, Visualization in Data Science (VDS at ACM KDD and IEEE VIS), Visualization in Data Science (VDS at ACM KDD and IEEE VIS). 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. 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/. Submissions can be original research contributions, or abstracts of papers previously submitted to top-tier venues, but not currently under review in other venues and not yet published. However, theoreticians and practitioners of AI and Safety are confronted with different levels of safety, different ethical standards and values, and different degrees of liability, that force them to examine a multitude of trade-offs and alternative solutions. to protect data owner privacy in FL. Please use vds@ieeevis.org to get in touch with us, or follow us on Twitter at @VisualDataSci. Design, Automation and Test in Europe Conference (DATE 2020), long paper, (acceptance rate: 26%), accepted. Kaiqun Fu, Taoran Ji, Liang Zhao, and Chang-Tien Lu. "The EMBERS architecture for streaming predictive analytics." Accepted contributions will be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals. The papers may consist of up to seven pages of technical content plus up to two additional pages for references. Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universit de Montral), Elias B. Khalil (University of Toronto), Pashootan Vaezipoor (University of Toronto). The goal of this workshop is to bring together the causal inference, artificial intelligence, and behavior science communities, gathering insights from each of these fields to facilitate collaboration and adaptation of theoretical and domain-specific knowledge amongst them. 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.
KDD 2022 Reveals Schedule of Data Mining and Knowledge Discovery Papers RL4ED is intended to facilitate tighter connections between researchers and practitioners interested in the broad areas of reinforcement learning (RL) and education (ED).
KDD 2022 : 28th ACM SIGKDD Conference on Knowledge Discovery - WikiCFP STGEN: Deep Continuous-time Spatiotemporal Graph Generation. 1953-1970, Oct. 2017. Jos Miguel Hernndez-Lobato, University of CambridgeProf.
KDD 2022 | Washington DC, U.S. We have the following keynote speakers confirmed: Andreas Holzinger (Medical Univ. Videos have become an omnipresent source of knowledge: courses, presentations, conferences, documentaries, live streams, meeting recordings, vlogs. The workshop organizers invite paper submissions on the following (and related) topics: This workshop will be a one-day workshop, featuring invited speakers, poster presentations, and short oral presentations of selected accepted papers. Scott E. Fahlman, School of Computer Science, Carnegie Mellon University (sef@cs.cmu.edu), Edouard Oyallon, Sorbonne Universit LIP6 (Edouard.oyallon@lip6.fr), Dean Alderucci, School of Computer Science, Carnegie Mellon University, (dalderuc@cs.cmu.edu). 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/. 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. We would especially like to highlight approaches that are qualitatively different from some popular but computationally intensive NAS methods. Zishan Gu, Ke Zhang, Guangji Bai, Liang Chen, Liang Zhao, Carl Yang.
Rex Ying's Personal Website Deep Generative Model for Periodic Graphs. The positive/negative social impacts and ethical issues related to adversarial ML. Qingzhe Li, Liang Zhao, Yi-Ching Lee, Yanfang Ye, Jessica Lin, and Lingfei Wu. Hua, Ting, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. Extended abstract up to 2 pages are also welcome. "TITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction", the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems 2019 (SIGSPATIAL 2019), long paper, (acceptance rate: 21.7%), Chicago, Illinois, USA, accepted. The goal of this workshop is to connect researchers in self-supervision inside and outside the speech and audio fields to discuss cutting-edge technology, inspire ideas and collaborations, and drive the research frontier. At least one author of each accepted submission must be present at the workshop. Unsupervised Deep Subgraph Anomaly Detection. a fantastic tutorial on SIGKDD'09 by Prof. Eamonn Keogh (UC Riverside). Long Beach, California, USA . However, ML systems may be non-deterministic; they may re-use high-quality implementations of ML algorithms; and, the semantics of models they produce may be incomprehensible. Papers will be submitted electronically using Easychair. KDD 2022. 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. The workshop is being organized by application area or other, panels, invited speakers, interactive, small groups, discussions, presentations. May 8, 2022: Student Travel Awards announcement is, Apr. Disease Contact Network. 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. Toward Model Parallelism for Deep Neural Network based on Gradient-free ADMM Framework. It has gained popularity in some domains such as image classification, speech recognition, smart city, and healthcare. Connor Coley, Massachusetts Institute of TechnologyProf. We plan to invite 2-4 keynote speakers from prestigious universities and leading industrial companies. Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, Xiang Chen. Submissions are due by 12 November 2021. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2014), industrial track, pp. KDD is the premier Data Science conference. 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. of Graz), Cynthia Rudin (Duke Univ.) Some will be selected for spotlight talks, and some for the poster session. Regarding efficiency, it is impractical to train a neural network containing billions of parameters and then deploy it to an edge device in practice. However, workshop organizers may set up any archived publication mechanism that best suits their workshop. Its capabilities have expanded from processing structured data (e.g. The consideration and experience of adversarial ML from industry and policy making. Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. RLG is a full-day workshop. GNES: Learning to Explain Graph Neural Networks. Novel approaches and works in progress are encouraged. We invite thought-provoking submissions on a range of topics in fields including, but not limited to, the following areas: The full-day workshop will start with a keynote talk, followed by an invited talk and contributed paper presentations in the morning. 8 pages), short (max. By registering, you agree to receive emails from UdeM. Extended abstracts should not exceed 2 pages, excluding references. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. ReForm: Static and Dynamic Resource-Aware DNN Reconfiguration Framework for Mobile Devices. iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow. Universit de MontralOffice of Admissions and RecruitmentC. Hierarchical Incomplete Multisource Feature Learning for Spatiotemporal Event Forecasting. in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), research track (acceptance rate: 18.2%), San Francisco, California, pp. 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. Junxiang Wang, Hongyi Li, Liang Zhao. You can optionally export all deadlines to Google Calendar or .ics . Deep Generative Models for Spatial Networks. The format is the standard double-column AAAI Proceedings Style. 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. 32, no. 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 Poster/short/position papers submission deadline: Nov 5, 2021Full paper submission deadline: Nov 5, 2021Paper notification: Dec 3, 2021. Proceedings of the IEEE (impact factor: 9.237), vol. Papers will be peer-reviewed and selected for spotlight and/or poster presentation at the workshop. Attendance is open to any interested participants at AAAI-22. Data science draws from methodology developed in such fields as applied mathematics, statistics, machine learning, data mining, data management, visualization, and HCI. Online and Distributed Robust Regressions with Extremely
Noisy Labels. Submissions will undergo double blind review. Liang Zhao, Junxiang Wang, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. Attendance is open to all. Application fees are not refundable. And with particular focuses but not limited to these application domains: Our program consists of two sessions: academic session and industry session. a concise checklist by Prof. Eamonn Keogh (UC Riverside). Thirty-First AAAI Conference on Artificial Intelligence, pp. Held in conjunction with KDD'22 Aug 15, 2022 - Washington DC, USA. Spatiotemporal Innovation Center Team. How can we make AI-based systems more ethically aligned? Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. Conference stats are visualized below for a straightforward comparison. Deep Multi-attributed Graph Translation with Node-Edge Co-evolution. Performance characterization of AI algorithms and systems under bias and scarcity. 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. For instance, advanced driver assistance systems and autonomous cars have been developed based on AI techniques to perform forward collision warning, blind spot monitoring, lane departure warning systems, traffic sign recognition, traffic safety, infrastructure management and congestion, and so on. Second, psychological experiments in laboratories and in the field, in partnership with technology companies (e.g., using apps), to measure behavioral outcomes are being increasingly used for informing intervention design. "Multi-Task Learning for Spatio-Temporal Event Forecasting." By entering your email, you consent to receive communications from UdeM. Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. We will instead host the accepted papers on this website (https://aka.ms/di-2022) indefinitely. It provides an international forum . Can AI achieve the same goal without much low-level supervision? The trained models are intended to assign scores to novel utterances, assessing whether they are possible or likely utterances in the training language. Realizing the vision of Document Intelligence remains a research challenge that requires a multi-disciplinary perspective spanning not only natural language processing and understanding, but also computer vision, layout understanding, knowledge representation and reasoning, data mining, knowledge discovery, information retrieval, and more all of which have been profoundly impacted and advanced by deep learning in the last few years. Large-scale Cost-aware Classification Using Feature Computational Dependency Graph. Aryan Deshwal (Washington State University, aryan.deshwal@wsu.edu), Syrine Belakaria (Washington State University, syrine.belakaria@wsu.edu), Cory Simon (Oregon State University, cory.simon@oregonstate.edu), Jana Doppa (Washington State University, jana.doppa@wsu.edu), Yolanda Gil (University of Southern California, gil@isi.edu), Supplemental workshop site:https://ai-2-ase.github.io/. KDD 2022 | Washington DC, U.S. SIGKDD CONFERENCE Latest News Aug 12, 2022: Please check out the proceedings access information.