4. It is a forum to bring attention towards collecting, measuring, managing, mining, and understanding multimodal disinformation, misinformation, and malinformation data from social media. 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. This workshop has no archival proceedings. ML-guided rare event modeling and system uncertainty quantification, Development of software, libraries, or benchmark datasets, and. 41-50, New Orleans, US, Dec 2017. Amir A. Fanid, Monireh Dabaghchian, Ning Wang, Pu Wang, Liang Zhao, Kai Zeng. 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. 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. Additionally, adversaries continue to develop new attacks. 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. Feng Chen, Baojian Zhou, Adil Alim, Liang Zhao. Online and Distributed Robust Regressions with Extremely Noisy Labels. Adverse event detection by integrating Twitter data and VAERS. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), (Acceptance Rate: 15%), accepted. Integration of AI-based approaches with engineering prototyping and manufacturing. Theoretical understanding of adversarial ML and its connection to other areas. Use Compass, the interactive checklist designed exclusively for the Universit de Montral, to carefully prepare your application and to avoid common pitfalls along the way. 76, pp. Eliminating the need to guess the right topology in advance of training is a prominent benefit of learning network architecture during training. It further combines academia and industry in a quest for well-founded practical solutions. Two types of submissions will be considered: full papers (6-8 pages + references), and short papers (2-4 pages + references). Information extraction from text and semi-structured documents. How to Write and Publish Research Papers for the Premier Forums in Knowledge & Data Engineering: Deep Classifier Cascades for Open World Recognition. We invite a long research paper (8 pages) and a demo paper (4 pages) (including references). Workshops will be held Monday and Tuesday, February 28 and March 1, 2022. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 9.77%), to appear, 2022. The workshop on Robust Artificial Intelligence System Assurance (RAISA) will focus on research, development and application of robust artificial intelligence (AI) and machine learning (ML) systems. 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. Whats more, various AI based models are trained on massive student behavioral and exercise data to have the ability to take note of a students strengths and weaknesses, identifying where they may be struggling. 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. After the submission deadline, the names and order of authors cannot be changed. Deep learning and statistical methods for data mining. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. KDD 2022. 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. The availability of massive amounts of data, coupled with high-performance cloud computing platforms, has driven significant progress in artificial intelligence and, in particular, machine learning and optimization. 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. Neurocomputing (Impact Factor: 5.719), accepted. Frontiers in Neurorobotics, (impact factor: 2.574), accepted. 11, 2022: We have posted the list of accepted Workshops at, Apr. We expect 50-65 people in the workshop. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. Graph Neural Networks: Foundations, Frontiers, and Applications. The deadline for the submissions is July 31st, 2022 11.59 PM (Anywhere on Earth time). anomaly detection, and ensemble learning. As Artificial Intelligence (AI) begins to impact our everyday lives, industry, government, and society with tangible consequences, it becomes increasingly important for a user to understand the reasons and models underlying an AI-enabled systems decisions and recommendations. Pattern Recognition, (impact factor: 7.196),112 (2021): 107711. The acceptance decisions will take in account novelty, technical depth and quality, insightfulness, depth, elegance, practical or theoretical impact, reproducibility and presentation. 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. Would you like to mark this message as the new best answer? 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. Property Controllable Variational Autoencoder via Invertible Mutual Dependence. Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. 2022. While most work on XAI has focused on opaque learned models, this workshop also highlights the need for interactive AI-enabled agents to explain their decisions and models. 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. fact-checking. Please keep your paper format according to AAAI Formatting Instructions (two-column format). Causal inference is one of the main areas of focus in artificial intelligence (AI) and machine learning (ML) communities. 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. An increasing world population, coupled with finite arable land, changing diets, and the growing expense of agricultural inputs, is poised to stretch our agricultural systems to their limits. The aim of this workshop is to focus on both original research and review articles on various disciplines of ITS applications, including particularly AI techniques for ITS time-series data analyses, ITS spatio-temporal data analyses, advanced traffic management systems, advanced traveler information systems, commercial vehicle operation systems, advanced vehicle control and safety systems, advanced public transportation services, advanced information management services, etc. ADMM for Efficient Deep Learning with Global Convergence. Key obstacles include lack of high-quality data, difficulty in embedding complex scientific and engineering knowledge in learning, and the need for high-dimensional design space exploration under constrained budgets. What safety engineering considerations are required to develop safe human-machine interaction? Continuous refinement of AI models using active/online learning. At least three research trends are informing insights in this field. We welcome attendance from individuals who do not have something theyd like to submit but who are interested in RL4ED research. 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. 689-698, Barcelona, Spain, Dec 2016. The objective of this workshop is to discuss the winning submissions of the Submissions to the Amazon KDD Cup 2022 issingle-blind (author names and affiliations should be listed). The review process will be single blind. Oral presentations: 10 minute presentation for oral papers. July 21: Clarified that the workshop this year will be held in-person. The main research questions and topics of interest include, but are not limited to: This will be a one day workshop, including four invited speakers, one panel session, a number of oral presentations of the accepted long papers and two poster sessions for all accepted papers including short and long. Chen Ling, Hengning Cao, Liang Zhao. Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! Submissions are limited to a total of 5 pages for initial submission (up to 6 pages for final camera-ready submission), excluding references or supplementary materials, and authors should only rely on the supplementary material to include minor details that do not fit in the 5 pages. This workshop aims to provide a premier interdisciplinary forum for researchers in different communities to discuss the most recent trends, innovations, applications, and challenges of optimal transport and structured data modeling. It is important to learn how to use AI effectively in these areas in order to be able to motivate and help people to take actions that maximize their welfare. Yuyang Gao, Tong Sun, Sungsoo Hong, and Liang Zhao. Dataset(s) will be provided to hack-a-thon participants. FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. Our intent is to facilitate new AI/ML advances for core engineering design, simulation, and manufacturing. 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. Methods for learning network architecture during training, including Incrementally building neural networks during training, new performance benchmarks for the above. [Best Paper Candidate], Minxing Zhang, Dazhou Yu, Yun Li, Liang Zhao. 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! 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. Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, and Chang-Tien Lu. 5 (2014): 1447-1459. 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. Extended abstracts should not exceed 2 pages, excluding references. Novel algorithmic solutions to causal inference or discovery problems using information-theoretic tools or assumptions. "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 fundamental mechanism of an online marketplace is to match supply and demand to generate transactions, with objectives considering service quality, participants experience, financial and operational efficiency. Current rates of progress are insufficient, making it impossible to meet this goal without a technological paradigm shift. 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. Yuanqi Du*, Shiyu Wang* (co-first author), Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao. By registering, you agree to receive emails from UdeM. In nearly all applications, reliability, safety, and security of such systems is a critical consideration. 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. in Proceedings of the SIAM International Conference on Data Mining (SDM 2015), (acceptance rate: 22%), Vancouver, BC, pp. KDD 2022 : Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. For each accepted paper, at least one author must attend the workshop and present the paper. in the proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), (acceptance rate: 26%), pp. SDU accepts both long (8 pages including references) and short (4 pages including references) papers. Researchers from related domains are invited to submit papers on recent advanced technologies, resources, tools and challenges for VTU. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. All submissions must be original contributions and will be peer reviewed, single-blinded. 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. Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. 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. Welcome to PAKDD2022. How can we develop solid technical visions and new paradigms about AI Safety? 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. World Wide Web Conference (WWW 2018), (acceptance rate: 14.8%), Lyon, FR, Apr 2018, accepted. At least one author of each accepted submission must register and present the paper at the workshop. Qingzhe Li, Liang Zhao, Yi-Ching Lee, Avesta Sassan, and Jessica Lin. Full papers: Submissions must represent original material that has not appeared elsewhere for publication and that is not under review for another refereed publication. References will not count towards the page limit. Papers more suited for a poster, rather than a presentation, would be invited for a poster session. We are in a conversation with some publishers once they confirm, we will announce accordingly. We collaborate with Saudi Aramco to use machine learning for simulating oil and water flows, . We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. Meta-learning models from various existing task-specific AI models. This cookie is set by GDPR Cookie Consent plugin. Self-supervised learning (SSL) has shown great promise in problems involving natural language and vision modalities. ACM Computing Surveys (CSUR), (impact factor: 10.28), accepted. We are excited to continue promoting innovation in self-supervision for the speech/audio processing fields and inspiring the fields to contribute to the general machine learning community. 639-648, Nov 2015. Registration in each workshop is required by all active participants, and is also open to all interested individuals.

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