Adversarial attacking deep learning systems, Robust architectures against adversarial attacks, Hardware implementation and on-device deployment, Benchmark for evaluating model robustness, New methodologies and architectures for efficient and robust deep learning, December 3, 2021 Acceptance Notification, Applications of privacy-preserving AI systems, Differential privacy: theory and applications, Distributed privacy-preserving algorithms, Privacy preserving optimization and machine learning, Privacy preserving test cases and benchmarks. Distributed Self-Paced Learning in Alternating Direction Method of Multipliers. All deadlines are at 11:59 PM anytime in the world. The adversarial ML could also result in potential data privacy and ethical issues when deploying ML techniques in real-world applications. Novel methods to learn from scarce/sparse, or heterogenous, or multimodal data. Fang Jin, Wei Wang, Liang Zhao, Edward Dougherty, Yang Cao, Chang-Tien Lu, and Naren Ramakrishnan. This is especially the case for non-traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data. Attendance is virtual and open to all. DI@KDD2022 Call for Papers Organization Program Keynote Talk Accepted Papers Call for Papers Document Intelligence Workshop @ KDD 2022 UPDATES August 6: Final versions of the papersare posted! information bottleneck principle). In some programs, spots may be available after the deadlines. Tips for Doing Good DM Research & Get it Published! Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. It is well-known that deep learning techniques that were disruptive for Euclidean data such as images or sequence data such as text are not immediately applicable to graph-structured data. 4, Roosevelt Rd., Taipei, TaiwanAffiliation: National Taiwan UniversityPhone: +1-412-465-0130Email: yvchen@csie.ntu.edu.tw, Paul CrookAddress: 1 Hacker Way, Menlo Park, CA, USAAffiliation: FacebookPhone: +1-650-885-0094Email: pacrook@fb.com, DSTC 10 home:https://dstc10.dstc.community/homeDSTC 10 CFPs:https://dstc10.dstc.community/calls_1/call-for-workshop-papers. It leverages many emerging privacy-preserving technologies (SMC, Homomorphic Encryption, differential privacy, etc.) Advances in AI technology, particularly perception and planning, have enabled unprecedented advances in autonomy, with autonomous systems playing an increasingly important role in day-to-day lives, with applications including IoT, drones, and autonomous vehicles. In the Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (acceptance rate: 17.9%), accepted, Macao, China, Aug 2019. However, you may visit "Cookie Settings" to provide a controlled consent. Martin Michalowski, PhD, FAMIA (Co-chair), University of Minnesota; Arash Shaban-Nejad, PhD, MPH (Co-chair), The University of Tennessee Health Science Center Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics; Simone Bianco, PhD (Co-chair), IBM Almaden Research Center; Szymon Wilk, PhD, Poznan University of Technology; David L. Buckeridge, MD, PhD, McGill University; John S. Brownstein, PhD, Boston Childrens Hospital, Workshop URL:http://w3phiai2022.w3phi.com/. 1953-1970, Oct. 2017. and facilitate discussions and collaborations in developing trustworthy AI methods that are reliable and more acceptable to physicians. Knowledge Discovery and Data Mining. Both the research papers track and the applied data science papers track will take . 15, pp. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. Functional Connectivity Prediction with Deep Learning for Graph Transformation. Note: Mandatory abstract deadline on May 16, 2022 Deadline: ISMIR 2022 ISMIR '22 ​ . The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. All papers must be submitted in PDF format, using the AAAI-22 author kit. Dazhou Yu, Guangji Bai, Yun Li, and Liang Zhao. "Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design", 28th International Conference on Field Programmable Logic and Applications (FPL 2019), (acceptance rate: 18%), Barcelona, Spain, accepted. ASPLOS 2023 will be moving to three submission deadlines. In particular, we encourage papers covering late-breaking results and work-in-progress research. TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints. [Best Paper Award Shortlist]. Some of the key questions to be explored include: The workshop will take place in person and will span over one day. "Spatiotemporal Event Forecasting in Social Media." Spatiotemporal Innovation Center Team. the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018) (acceptance rate: 20.6%), Stockholm, Sweden, Jul 2018, accepted. Disease Contact Network. In nearly all applications, reliability, safety, and security of such systems is a critical consideration. Some will be selected for spotlight talks, and some for the poster session. [Bests of ICDM]. We plan to invite 2-4 keynote speakers from prestigious universities and leading industrial companies. We invite submission of papers describing innovative research and applications around the following topics. Their results will be submitted in either a short paper or poster format. Junxiang Wang, Junji Jiang, Liang Zhao. Counter-intuitive behaviors of ML models will largely affect the public trust on AI techniques, while a revolution of machine learning/deep learning methods may be an urgent need. Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. 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. 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. The challenge requires participants to build competitive models for diverse downstream tasks with limited labeled data and trainable parameters, by reusing self-supervised pre-trained networks. The full-day workshop will start with an opening remark followed by long research paper presentations in the morning. 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. The workshop invites contribution to novel methods, innovations, applications, and broader implications of SSL for processing human-related data, including (but not limited to): In addition to the above, papers that consider the following are also invited: Manuscripts that fit only certain aspects of the workshop are also invited. Send this CFP to us by mail: cfp@ourglocal.org. Registration in each workshop is required by all active participants, and is also open to all interested individuals. You can optionally export all deadlines to Google Calendar or .ics . 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. Trade-Off between Privacy-Preserving and Explainable Federated Learning Federated Learning Multi-Party Computation, Federated Learning Homomorphic Encryption, Federated Learning Personalization Techniques, Federated Learning Meets Mean-Field Game Theory, Federated Learning-based Corporate Social Responsibility. Knowledge Discovery and Data Mining is an interdisciplinary area focusing If it turns out that the architecture is not appropriate for the task, the user must repeatedly adjust the architecture and retrain the network until an acceptable architecture has been obtained. Business documents are central to the operation of all organizations, and they come in all shapes and sizes: project reports, planning documents, technical specifications, financial statements, meeting minutes, legal agreements, contracts, resumes, purchase orders, invoices, and many more. Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain are encouraged. Zhiqian Chen, Lei Zhang, Gaurav Kolhe, Hadi Mardani Kamali, Setareh Rafatirad, Sai Manoj Pudukotai Dinakarrao, Houman Homayoun, Chang-Tien Lu, Liang Zhao. Zero Speech challenge is to build language models only based on audio or audio-visual information, without using any textual input. ICLR 2022 Meeting Dates The Tenth annual conference is held Mon. 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 are submitted through the CMT portal for this workshop: Please select the track for your submission in Primary Subject Area and indicate if your submission is a full paper or an extended abstract in Secondary Subject Area. 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. While a variety of research has advanced the fundamentals of document understanding, the majority have focused on documents found on the web which fail to capture the complexity of analysis and types of understanding needed across business documents. Some specific topics in the context of scientific discovery and engineering design include (but not limited to): This will be a one day workshop with a number of paper presentations and poster spotlights, a poster session, several invited talks, and a panel discussion. Robust Regression via Online Feature Selection under Adversarial Data Corruption. Programming Languages, Domain specific languages, Libraries and software tools for integration of various learning and reasoning paradigms. Adverse event detection by integrating Twitter data and VAERS. The goal of the inaugural HC-SSL workshop is to highlight and facilitate discussions in this area and expose the attendees to emerging potentials of SSL for human-centric representation learning, and promote responsible AI within the context of SSL. 4701-4707, San Francisco, California, USA, Feb 2017. SIGMOD 2022 adheres to the ACM Policy Against Harassment. IEEE Computer (impact factor: 3.564), vo. A final tribute was paid on Saturday to former Coalition Avenir Qubec (CAQ) minister Nadine Girault, who died of lung cancer last month at age 63 . "STED: semi-supervised targeted-interest event detectionin in twitter." 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. OARS-KDD2022: KDD 2022 Workshop on Online and Adaptive Recommender Systems Washington DC, DC, United States, August 15, 2022 Topics: data science artificial intelligence recommender system recommendation KDD 2022 Workshop on Online and Adaptive Recommender Systems (OARS) Call For Papers ================== Automatic fact/claim verification has recently become a topic of interest among diverse research communities. Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. We are in a conversation with some publishers once they confirm, we will announce accordingly. The accelerated developments in the field of Artificial Intelligence (AI) hint at the need for considering Safety as a design principle rather than an option. Qingzhe Li, Liang Zhao, Jessica Lin and Yi-ching Lee. P. 6205, succursale Centre-villeMontral, (Qubec) H3C 3T5Canada. The main interest of the proposed workshop is to look at a new perspective of system engineering where multiple disciplines such as AI and safety engineering are viewed as a larger whole, while considering ethical and legal issues, in order to build trustable intelligent autonomy. 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. At least three research trends are informing insights in this field. The invited speakers, who are well-recognized experts of the field, will give a 30 minute talk. Benchmarks to reliably evaluate attacks/defenses and measure the real progress of the field. 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. Data science is the practice of deriving insights from data, enabled by statistical modeling, computational methods, interactive visual analysis, and domain-driven problem solving. If you are interested, please send a short email to rl4edorg@gmail.com and we can add you to the invitee list. This topic also encompasses techniques that augment or alter the network as the network is trained. Methods for learning network architecture during training, including Incrementally building neural networks during training, new performance benchmarks for the above. ), 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 growing popularity of NAS methods demonstrates the communitys hunger for better ways of choosing or evolving network architectures that are well-matched to the problem at hand. Share. : Papers must be in PDF format, and formatted according to the new Standard ACM Conference Proceedings Template. ACM Computing Surveys (CSUR), (impact factor: 10.28), accepted. All extended abstracts and full papers are to be presented at the poster sessions. DOI:https://doi.org/10.1145/3339823. GNES: Learning to Explain Graph Neural Networks. The academic session will focus on most recent research developments on GNNs in various application domains. Dr. Emotion: Disentangled Representation Learning for Emotion Analysis on Social Media to Improve Community Resilience in the COVID-19 Era and Beyond. 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. DB transactions) to unstructured data (e.g. Submissions must be formatted in the AAAI submission format (https://www.aaai.org/Publications/Templates/AuthorKit22.zip) All submissions should be done electronically via EasyChair. Submissions are limited to a maximum of four (4) pages, including all content and references, and must be in PDF format. Poster/short/position papers submission deadline: Nov 5, 2021Full paper submission deadline: Nov 5, 2021Paper notification: Dec 3, 2021. Oral Paper (Top 5% among the accepted papers). The scope of the workshop includes, but is not limited to, the following areas: We also invite participants to an interactive hack-a-thon. Lastly, learning joint modalities is of interest to both Natural Language Processing (NLP) and Computer Vision (CV) forums. The workshop will include several technical sessions, a virtual poster session where presenters can discuss their work, to further foster collaborations, multiple invited speakers covering crucial aspects for the practical deep learning in the wild, especially the efficient and robust deep learning, some tutorial talks, the challenge for efficient deep learning and solution presentations, and will conclude with a panel discussion. At AAAI 2021, we successfully organized this workshop (https://taih20.github.io/). Submissions tackling new problems or more than one of the aforementioned topics simultaneously are encouraged.

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