In Proceedings of the 20th International Conference on Data Mining (ICDM 2020), (acceptance rate: 9.8%), November 17-20, 2020, Virtual Event, Sorrento, Italy, 10 pages. Rupinder Khandpur, Taoran Ji, Yue Ning, Liang Zhao, Chang-Tien Lu, Erik Smith, Christopher Adams and Naren Ramakrishnan. Transfer learning methods for business document reading and understanding. The 33rd European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databasesg (ECML-PKDD 2022) (Acceptance Rate: 26%), accepted, 2022. Ranking, acceptance rate, deadline, and publication tips. KDD 2022 KDD . Web applications along with text processing programs are increasingly being used to harness online data and information to discover meaningful patterns identifying emerging health threats. Universit de MontralOffice of Admissions and RecruitmentC. 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 . Poster session: One poster session of all accepted papers which leads for interaction and personal feedback to the research. 1059-1072, May 1 2017. The 21st Web Conference (WWW 2022), (Acceptance Rate: 17.7%), accepted. 32, no. At the AAAI-22 Workshop on Scientific Document Understanding (SDU@AAAI-22), we aim to gather insights into the recent advances and remaining challenges on scientific document understanding. Novel approaches and works in progress are encouraged. All submissions must be anonymous and conform to AAAI standards for double-blind review. We aim to bring together researchers in AI, healthcare, medicine, NLP, social science, etc. We invite workshop participants to submit their original contributions following the AAAI format through EasyChair. Introduction: SIGKDD aims to provide the premier forum for advancement and adoption of the "science" of knowledge discovery and data mining.SIGKDD will encourage: basic research in KDD (through annual research conferences, newsletter and other related activities . IEEE Computer (impact factor: 3.564), vo. information bottleneck principle). The workshop will focus on the application of AI to problems in cyber-security. Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. All papers must be submitted in PDF format, using the AAAI-21 author kit and anonymized. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. Authors are strongly encouraged to make data and code publicly available whenever possible. Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu, "Robust Regression via Heuristic Corruption Thresholding and Its Adaptive Estimation Variation", ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 1.98), accepted, 2019. The full-day workshop will start with an opening remark followed by long research paper presentations in the morning. Frontiers in Big Data, accepted, 2021. DOI:https://doi.org/10.1145/3339823. Submissions tackling new problems or more than one of the aforementioned topics simultaneously are encouraged. International Journal of Digital Earth, (impact factor: 3.097), 25 Aug 2020, https://doi.org/10.1080/17538947.2020.1809723. Analytical cookies are used to understand how visitors interact with the website. Modern surveillance systems employ tools and techniques from artificial intelligence and machine learning to monitor direct and indirect signals and indicators of disease activities for early, automatic detection of emerging outbreaks and other health-relevant patterns. Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye. Taking the pulse of COVID-19: a spatiotemporal perspective. Knowledge Discovery and Data Mining is an interdisciplinary area focusing 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. BEAN: Interpretable and Efficient Learning with Biologically-Enhanced Artificial Neuronal Assembly. Published March 4, 2023 4:51 a.m. PST. While there have been extensive independent research threads on the subject of safety and reliability of specific sub-problems in autonomy, such as the problem of robust control, as well as recent considerations of robust AI-based perception, there has been considerably less research on investigating robustness and trust in end-to-end autonomy, where AI-based perception is integrated with planning and control in an open loop. We encourage long papers, short papers and demo papers. 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. We especially welcome research from fields including but not limited to AI, human-computer interaction, human-robot interaction, cognitive science, human factors, and philosophy. Novel ML methods in the computational material and physical sciences. Papers must be between 4-8 pages in the AAAI submission format, with the eighth page containing only references. Deep Generative Model for Periodic Graphs. 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. Association for the Advancement of Artificial Intelligence, The Thirty-Sixth AAAI Conference on Artificial IntelligenceFebruary 28 and March 1, 2022Vancouver Convention CentreVancouver, BC, Canada. Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting.Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), (acceptance rate: 16.2%), Hawaii, USA, Feb 2019, accepted. The official dates for submitting an application are detailed below, but see the exact deadline posted on the Description Page for the program of study. Ting Hua, Chandan Reddy, Lijing Wang, Liang Zhao, Lei Zhang, Chang-Tien Lu, and Naren Ramakrishnan. However, most models and AI systems are built with conservative operating environment assumptions due to regulatory compliance concerns. We invite submissions to the AAAI-22 workshop on Graphs and more Complex structures for Learning and Reasoning to be held virtually on February 28 or March 1, 2022. The goal of this workshop is to focus on creating and refining AI-based approaches that (1) process personalized data, (2) help patients (and families) participate in the care process, (3) improve patient participation, (4) help physicians utilize this participation to provide high quality and efficient personalized care, and (5) connect patients with information beyond that available within their care setting. The submission website ishttps://cmt3.research.microsoft.com/PracticalDL2022. 40, no. 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. Big data Journal (impact factor: 1.489), vo. 2022. 4 pages) papers describing research at the intersection of AI and science/engineering domains including chemistry, physics, power systems, materials, catalysis, health sciences, computing systems design and optimization, epidemiology, agriculture, transportation, earth and environmental sciences, genomics and bioinformatics, civil and mechanical engineering etc. [Best Paper Award]. Authors are invited to send a contribution in the AAAI-22 proceedings format. Bioinformatics (Impact Factor: 6.937), accepted, 2022. Different from machine learning, Knowledge Discovery and Data Mining (KDD) is considered to be more practical and more related with real-world applications. Submission Site:https://cmt3.research.microsoft.com/SAS2022, Abdelrahman Mohamed (Facebook, [email protected]), Hung-yi Lee (NTU, [email protected]), Shinji Watanabe (CMU, [email protected]), Tara Sainath (Google, [email protected]), Karen Livescu (TTIC, [email protected]), Shang-Wen Li (Facebook, [email protected]), Ewan Dunbar (University of Toronto, [email protected]) Emmanuel Dupoux (EHESS/Facebook, [email protected]), Workshop URL:https://aaai-sas-2022.github.io/. 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. All these changes require novel solutions, and the AI community is well-positioned to provide both theoretical- and application-based methods and frameworks. [materials][data]. The papers have to be submitted through EasyChair. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. Papers will be peer-reviewed and selected for oral and/or poster presentations at the workshop. Integration of Deep learning and Constraint programming. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. for causal estimation in behavioral science. 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. Chen Ling, Tanmoy Chowdhury, Junji Jiang, Junxiang Wang, Xuchao Zhang, Haifeng Chen, and Liang Zhao. Conference Management Toolkit - Login 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. Springer, Singapore. The adversarial ML could also result in potential data privacy and ethical issues when deploying ML techniques in real-world applications. In the coronavirus era, requiring many schools to move to online learning, the ability to give feedback at scale could provide needed support to teachers. In particular, we encourage papers covering late-breaking results and work-in-progress research. 1-39, November 2016. If you are interested, please send a short email to [email protected] and we can add you to the invitee list. 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. We will use double-blind reviewing. In recent years, we have seen examples of general approaches that learn to play these games via self-play reinforcement learning (RL), as first demonstrated in Backgammon. 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. 14, 2022: The information of Keynote Speakers is available at, Apr. Junxiang Wang, Junji Jiang, Liang Zhao. 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. Online marketplaces exist in a diverse set of domains and industries, for example, rideshare (Lyft, DiDi, Uber), house rental (Airbnb), real estate (Beke), online retail (Amazon, Ebay), job search (LinkedIn, Indeed.com, CareerBuilder), and food ordering and delivery (Doordash, Meituan). Full papers: Submissions must represent original material that has not appeared elsewhere for publication and that is not under review for another refereed publication. 4. Liang Zhao, Jiangzhuo Chen, Feng Chen, Wei Wang, Chang-Tien Lu, and Naren Ramakrishnan. What is the status of existing approaches in ensuring AI and Machine Learning (ML) safety, and what are the gaps? What safety engineering considerations are required to develop safe human-machine interaction? This one-day workshop will consist of: (1) an ice-breaking session, (2) paper presentations, (3) a poster session, and (4) an ideation brainstorming session. Xiaojie Guo, Lingfei Wu, Liang Zhao. [Best Paper Candidate]. It drives discoveries in business, economy, biology, medicine, environmental science, the physical sciences, the humanities and social sciences, and beyond. Integration of AI-based approaches with engineering prototyping and manufacturing. . ECoST: Energy-Efficient Co-Locating and Self-Tuning MapReduce Applications. Some examples of the success of information theory in causal inference are: the use of directed information, minimum entropy couplings and common entropy for bivariate causal discovery; the use of the information bottleneck principle with applications in the generalization of machine learning models; analyzing causal structures of deep neural networks with information theory; among others. The 19th International Conference on Data Mining (ICDM 2019), short paper, (acceptance rate: 18.05%), Beijing, China, accepted. Registration information will be mailed directly to all invited participants in December. 2022. The papers may consist of up to seven pages of technical content plus up to two additional pages for references. 2085-2094, Aug 2016. Manuscripts must be submitted as PDF files viaEasyChair online submission system. About 7-8 invited speakers who are distinguished professional in Deep learning on graph will present the frontier research topics. Deadline in . 15, pp. For each accepted paper, at least one author must attend the workshop and present the paper. Online . KDD: Knowledge Discovery and Data Mining 2024 2023 2022 - WikiCFP Generative Adversarial Learning of Protein Tertiary Structures. Workshops are one day unless otherwise noted in the individual descriptions. Incomplete Label Uncertainty Estimation for Petition Victory Prediction with Dynamic Features. AAAI-22 Workshop Program - AAAI 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. By clicking Accept All, you consent to the use of ALL the cookies. 205-214, San Francisco, California, Aug 2016. The IEEE International Conference on Data Mining (ICDM 2022), full paper, (Acceptance Rate: 20%=174/870), short paper, to appear, 2022. MLG 2022 - 17th International Workshop on Mining and Learning with Graphs The program of the workshop will include invited talks, paper presentations and a panel discussion. 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. The AAAI template https://aaai.org/Conferences/AAAI-22/aaai22call/ should be used for all submissions. Motif-guided Heterogeneous Graph Deep Generation. ^All accepted WSDM papers are associated with an interactive poster presentation in addition to oral presentations. 1503-1512, Aug 2015. 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). Merge remote-tracking branch 'origin/master', 2. We expect 50~75 participants and potentially more according to our past experiences. Multi-instance Domain Adaptation for Vaccine Adverse Event Detection.27th International Short papers 10m presentation and 5m discussion. System reports will be presented during poster sessions. Please email to Lingfei Wu: [email protected] for any query. Yuyang Gao, Tong Sun, Sungsoo Hong, and Liang Zhao. Chen Ling, Hengning Cao, Liang Zhao. However, research in the AI field also shows that their performance in the wild is far from practical due to the lack of model efficiency and robustness towards open-world data and scenarios. Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Yanfang Ye, Liang Zhao. Recently developed tools and cutting-edge methodologies coming from the theory of optimal transport have proved to be particularly successful for these tasks. All papers will be peer reviewed, single-blinded. Our preliminary plan for the schedule is as following , DEFACTIFY@AAAI-22 Program [tentative]9:00AM-9:15AMInaugurationA brief summary of the shared tasks number of participants, best results, Session 1 multimodal fact checkingWorkshop papers 9:30AM 10:30AM, 11:00AM 12:00pmInvited talk 1 Prof. Rada Mihalcea, University of Michigan, Session 2 Best 4/5 papers from FACTIFY & MEMOTION shared taskWorkshop papers 1:00PM 2:00PM, 2:00PM 3:30PMInvited talk 2 Prof. LOUIS-PHILIPPE MORENCY, CMU, Session 2 multimodal hate speechWorkshop papers 4:00PM 5:00PM. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2013), demo track, pp. Topics of interest in the biomedical space include: Topics of general interest to cyber-security include: Submission site:https://easychair.org/conferences/?conf=aics22, Tamara Broderick (MIT CSAIL, [email protected]), James Holt (Laboratory for Physical Sciences, USA, [email protected]), Edward Raff (Booz Allen Hamilton, USA, [email protected]), Ahmad Ridley (National Security Agency), Dennis Ross (MIT Lincoln Laboratory, USA, [email protected]), Arunesh Sinha (Singapore Management University, Singapore, [email protected]), Diane Staheli (MIT Lincoln Laboratory, USA, [email protected]), William W. Streilein (MIT Lincoln Laboratory, USA, [email protected]), Milind Tambe (Harvard University, USA, [email protected]), Yevgeniy Vorobeychik (Washington University in Saint Louis, USA, [email protected]) Allan Wollaber (MIT Lincoln Laboratory, USA, [email protected]), Supplemental workshop site:http://aics.site/. ReForm: Static and Dynamic Resource-Aware DNN Reconfiguration Framework for Mobile Devices. Short or position papers of up to 4 pages are also welcome. "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. Yuyang Gao and Liang Zhao. Submission Site: See the webpagehttps://sites.google.com/view/gclr2022/submissions; for detailed instructions and submission link. Andy Doyle, Graham Katz, Kristen Summers, Chris Ackermann, Ilya Zavorin, Zunsik Lim, Sathappan Muthiah, Liang Zhao, Chang-Tien Lu, Patrick Butler, Rupinder Paul Khandpur. Reasons include: (1) a lack of certification of AI for security, (2) a lack of formal study of the implications of practical constraints (e.g., power, memory, storage) for AI systems in the cyber domain, (3) known vulnerabilities such as evasion, poisoning attacks, (4) lack of meaningful explanations for security analysts, and (5) lack of analyst trust in AI solutions. Following this AAAI conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed. 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). Online and Distributed Robust Regressions with Extremely Noisy Labels. This workshop aims to bring together FL researchers and practitioners to address the additional security and privacy threats and challenges in FL to make its mass adoption and widespread acceptance in the community. 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.
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