Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. 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. 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. For example, AI tools are built to ease the workload for teachers. The biomedical space has seen a flurry of activity recently, and cyber criminals have amplified their efforts with health-related phishing attacks, spreading misinformation, and intruding into health infrastructure. See ICDM Acceptance Rates for more information. a tutorial on how to structure data mining papers by Prof. Xindong Wu (University of Louisiana at Lafayette). This thread already has a best answer. 1-11, Feb 2016. The submission website ishttps://cmt3.research.microsoft.com/OTSDM2022. 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. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. Public health authorities and researchers collect data from many sources and analyze these data together to estimate the incidence and prevalence of different health conditions, as well as related risk factors. The submissions must be in PDF format, written in English, and formatted according to the AAAI camera-ready style. Event Prediction in the Big Data Era: A Systematic Survey. 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. This half day workshop will focus on research into the use of AI techniques to extract knowledge from unstructured data in financial services. However, the performance and efficiency of these techniques are big challenges for performing real-time applications. However, FL also faces multiple challenges that may potentially limit its applications in real-world use scenarios. Submission URL:https://easychair.org/conferences/?conf=rl4edaaai22. Detailed information could be found on the website of the workshop. 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. Papers will be peer-reviewed and selected for spotlight and/or poster presentation at the workshop. KDD 2022 is a dual-track conference that provides distinct programming in research and applied data science. System reports will be presented during poster sessions. Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. 2022. In particular, we encourage papers covering late-breaking results and work-in-progress research. [materials][data]. SIGMOD 2022 adheres to the ACM Policy Against Harassment. ), 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). Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. 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. Graph Neural Networks: Foundations, Frontiers, and Applications. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc. "A Uniform Representation for Trajectory Learning Tasks", 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2017), short paper, DOI=10.1145/3139958.3140017, Redondo Beach, CA, USA, Nov 2017. The eligibility criteria for attending the workshop will be registration in the conference/workshop as per AAAI norms. 1145/3394486.3403221. : 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. Workshops will be held Monday and Tuesday, February 28 and March 1, 2022. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), short paper (acceptance rate: 19.9%), Singapore, Dec 2018, accepted. Extended abstracts should not exceed 2 pages, excluding references. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. A tag already exists with the provided branch name. The study of complex graphs is a highly interdisciplinary field that aims to study complex systems by using mathematical models, physical laws, inference and learning algorithms, etc. Knowledge and Information Systems (KAIS), (Impact Factor: 2.531), to appear, 2022. 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. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), New Orleans, US, Feb 2018, pp. ML4OR will place particular emphasis on: (1) ML methodologies for enhancing traditional OR algorithms for integer programming, combinatorial optimization, stochastic programming, multi-objective optimization, location and routing problems, etc. Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI 2021), (acceptance rate: 21.0%), accepted. 20, 2022: We have announced Call for Nominations: , Jan. 25, 2022: Sponsorship Opportunities is available at, Jan. 6, 2022: Call for KDD Cup Proposals is available at, Dec. 26, 2021: Call for Workshop Proposals is available at, Dec. 26, 2021: Call for Tutorials is available at, Nov. 24, 2021: Those who are interested in serving as a PC, please feel free to fill in this, Nov. 12, 2021: Call for Research Track Papers is available at, Nov. 12, 2021: Call for Applied Data Science Track Papers is available at. With the rapid development of advanced techniques on the intersection between information theory and machine learning, such as neural network-based or matrix-based mutual information estimator, tighter generalization bounds by information theory, deep generative models and causal representation learning, information theoretic methods can provide new perspectives and methods to deep learning on the central issues of generalization, robustness, explainability, and offer new solutions to different deep learning related AI applications.This workshop aims to bring together both academic researchers and industrial practitioners to share visions on the intersection between information theory and deep learning, and their practical usages in different AI applications. Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. 76, pp. We welcome full research papers, position papers, and extended abstracts. GraphGT: Machine Learning Datasets for Deep Graph Generation and Transformation. The aim of the hack-a-thon is not only to foster innovation and potentially provide answers to outstanding research problems, but rather to engage the community and create new collaborations. If these formalities are not completed in time, you will have to file a new application at a later date. Gabriel Pedroza (CEA LIST), Jos Hernndez-Orallo (Universitat Politcnica de Valncia, Spain), Xin Cynthia Chen (University of Hong Kong, China), Xiaowei Huang (University of Liverpool, UK), Huascar Espinoza (KDT JU, Belgium), Mauricio Castillo-Effen (Lockheed Martin, USA), Sen higeartaigh (University of Cambridge, UK), Richard Mallah (Future of Life Institute, USA), John McDermid (University of York, UK), Supplemental workshop site:http://safeaiw.org/. Even in cases where one is able to collect data, there are inherently many kinds of biases in this process, leading to biased models. Please note that the KDD Cup workshop will haveno proceedingsand the authors retainfull rightsto submit or post the paper at any other venue. Winter. arXiv preprint arXiv:2302.02093 (2023). We are soliciting submissions of short papers in PDF format and formatted according to the Standard ACM Conference Proceedings Template. 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. Cleansing and image enhancement techniques for scanned documents. 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. 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. To push forward the research on acronym understanding in scientific text, we propose two shared tasks on acronym extraction (i.e., recognizing acronyms and phrases in text) and disambiguation (i.e., finding the correct expansion for an ambiguous acronym). Interactive Machine Learning (IML) is concerned with the development of algorithms for enabling machines to cooperate with human agents. The Conference. 1503-1512, Aug 2015. The robust development and assured deployment of AI systems: Participants will discuss how to leverage and update common software development paradigms, e.g., DevSecOps, to incorporate relevant aspects of system-level AI assurance. Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), Oral presentation (acceptance rate: 11.0%), pp. 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). The AAAI author kit can be downloaded from:https://www.aaai.org/Publications/Templates/AuthorKit22.zip. Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao. All papers will be peer reviewed, single-blinded. Shuo Lei, Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu. Accepted papers will not be archived, and we explicitly allow papers that are concurrently submitted to, currently under review at, or recently accepted in other conferences / venues. At least one author of each accepted submission must be present at the workshop. Accepted submissions will be notified latest by August 7th, 2022. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. ML4OR will serve as an interdisciplinary forum for researchers in both fields to discuss technical issues at this interface and present ML approaches that apply to basic OR building blocks (e.g., integer programming solvers) or specific applications. 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. Deadline: FSE 2023. However, most models and AI systems are built with conservative operating environment assumptions due to regulatory compliance concerns. In addition to the keynote and presentations of accepted works, the workshop will include both a general discussion session on defining and addressing the key challenges in this area , and a lightning tutorial session that will include brief overviews and demos of relevant tools, including open source frameworks such as Ecole. Submissions will be peer reviewed, single-blinded. Adaptive Kernel Graph Neural Network. KDD is the premier Data Science conference. Full (8 pages) and short (4 pages, work in progress) papers, AAAI style. All the submissions should be anonymous. By entering your email, you consent to receive communications from UdeM. We also use third-party cookies that help us analyze and understand how you use this website. We collaborate with Saudi Aramco to use machine learning for simulating oil and water flows, . "Forecasting Significant Societal Events Using The Embers Streaming Predictive Analytics System." 4498-4505, New Orleans, US, Feb 2018. Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. By registering, you agree to receive emails from UdeM. Xiaosheng Li, Jessica Lin, and Liang Zhao. 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. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains. All the submissions must follow the AAAI-22 formatting guidelines, camera-ready style. Authors of accepted papers will be invited to participate. Causal inference is one of the main areas of focus in artificial intelligence (AI) and machine learning (ML) communities. Papers should be up to 4 pages in length (excluding references) formatted using the AAAI template. Paper Submission Deadline: May 26, 2022 Author Notification: June 20, 2022 Camera Ready: July 9, 2022 Workshop: August . We also welcome submissions that are currently under consideration in such archival venues. Submission site:https://cmt3.research.microsoft.com/AAAI2022HCSSL/Submission/Index, Ali Etemad (Queens University, ali.etemad@queensu.ca), Ali Etemad (Queens University, ali.etemad@queensu.ca), Ahmad Beirami (Facebook AI, ahmad.beirami@gmail.com), Akane Sano (Rice University, akane.sano@rice.edu), Aaqib Saeed (Philips Research & University of Cambridge, aqibsaeed@protonmail.com), Alireza Sepas-Moghaddam (Socure, alireza.sepasm@socure.com), Mathilde Caron (Inria & Facebook AI, mathilde@fb.com), Pritam Sarkar (Queens University & Vector Institute, pritam.sarkar@queensu.ca), Huiyuan Yang (Rice University, hy48@rice.edu), Supplemental website:https://hcssl.github.io/AAAI-22/. Bioinformatics (Impact Factor: 6.937), accepted, 2022. Large-scale Cost-aware Classification Using Feature Computational Dependency Graph. Novel approaches and works in progress are encouraged. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. In general, AI techniques are still not widely adopted in the real world. to protect data owner privacy in FL. [Best Paper Candidate], Minxing Zhang, Dazhou Yu, Yun Li, Liang Zhao. Representation learning, distributed representations learning and encoding in natural language processing for financial documents; Synthetic or genuine financial datasets and benchmarking baseline models; Transfer learning application on financial data, knowledge distillation as a method for compression of pre-trained models or adaptation to financial datasets; Search and question answering systems designed for financial corpora; Named-entity disambiguation, recognition, relationship discovery, ontology learning and extraction in financial documents; Knowledge alignment and integration from heterogeneous data; Using multi-modal data in knowledge discovery for financial applications; Data acquisition, augmentation, feature engineering, and analysis for investment and risk management; Automatic data extraction from financial fillings and quality verification; Event discovery from alternative data and impact on organization equity price; AI systems for relationship extraction and risk assessment from legal documents; Accounting for Black-Swan events in knowledge discovery methods. Information extraction and information retrieval for scientific documents; Question answering and question generation for scholarly documents; Word sense disambiguation, acronym identification and expansion, and definition extraction; Document summarization, text mining, document topic classification, and machine reading comprehension for scientific documents; Graph analysis applications including knowledge graph construction and representation, graph reasoning and query knowledge graphs; Biomedical image processing, scientific image plagiarism detection, and data visualization; Code/Pseudo-code generation from text and im-age/diagram captioning, New language understanding resources such as new syn-tactic/semantic parsers, language models or techniques to encode scholarly text; Survey or analysis papers on scientific document under-standing and new tasks and challenges related to each scientific domain; Factuality, data verification, and anti-science detection. Geoinformatica, (impact factor: 2.392), Volume 20, Issue 4, pp 765-795, Oct 2016. The role of adjacent fields of study (e.g, computational social science) in mitigating issues of bias and trust in AI. We hope this will help bring the communities of data mining and visualization more closely connected. We allow papers that are concurrently submitted to or currently under review at other conferences or venues. This cookie is set by GDPR Cookie Consent plugin. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. Countdowns to conference deadlines in the field of autonomous driving. An Invertible Graph Diffusion Model for Source Localization. 2020. Interesting challenges in this domain include the drastic increase of work from home or remote work, the imbalance between the demand and supply of the job market, the popularity of independent workers, the capability of helping job seekers on their whole job seeking journey and career development, the different objectives and behaviors of all major stakeholders in the ecosystem, e.g. What techniques and approaches can be used to detect and effectively manage similar scenarios in the future? The accepted papers will be posted on the workshop website and will not appear in the AAAI proceedings. Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. And with particular focuses but not limited to these application domains: Our program consists of two sessions: academic session and industry session. We will accept both original papers up to 8 pages in length (including references) as well as position papers and papers covering work in progress up to 4 pages in length (not including references).Submission will be through Easychair at the AAAI-22 Workshop AI4DO submission site, Professor Bistra Dilkina (dilkina@usc.edu), USC and Dr. Segev Wasserkrug, (segevw@il.ibm.com), IBM Research, Prof. Andrea Lodi (andrea.lodi@cornell.edu), Jacobs Technion-Cornell Institute IIT and Dr. Dharmashankar Subrmanian (dharmash@us.ibm.com), IBM Research. P. 6205, succursale Centre-villeMontral, (Qubec) H3C 3T5Canada. "Spatiotemporal Event Forecasting in Social Media." 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.