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. Accepted submissions will have the option of being posted online on the workshop website. Submission site:https://easychair.org/conferences/?conf=kdf22, Chair:Xiaomo Liu (J.P. Morgan Chase AI Research, xiaomo.liu@jpmchase.com), Zhiqiang Ma (J.P. Morgan Chase AI Research), Armineh Nourbakhsh (J.P. Morgan Chase AI Research), Sameena Shah (J.P. Morgan Chase AI Research), Gerard de Melo (Hasso Plattner Institute), Le Song (Mohamed bin Zayed University of Artificial Intelligence), Workshop URL:https://aaai-kdf.github.io/kdf2022/. In addition, authors can provide an optional one page supplement at the end of their submitted paper (it needs to be in the same PDF file) focused on reproducibility. Methods for learning network architecture during training, including Incrementally building neural networks during training, new performance benchmarks for the above. You signed in with another tab or window. At the same time, multimodal hate-speech detection is an important problem but has not received much attention. Qingzhe Li, Amir A. Fanid, Martin Slawski, Yanfang Ye, Lingfei Wu, Kai Zeng, and Liang Zhao. 2020. The topics of interest include but are not limited to: Theoretical and Computational Optimal Transport: Optimal Transport-Driven Machine Learning: Optimal Transport-Based Structured Data Modeling: The full-day workshop will start with two long talks and one short talk in the morning. Washington DC, USA. Submission site:https://openreview.net/group?id=AAAI.org/2022/Workshop/AIAFS, Girish Chowdhary (University of Illinois, Urbana Champaign), Baskar Ganapathysubramanian (Iowa State University; contact: baskarg@iastate.edu), George Kantor (Carnegie Mellon University), Soumyashree Kar (Iowa State University), Koushik Nagasubramanian (Iowa State University), Soumik Sarkar (Iowa State University), Katia Sycara (Carnegie Mellon University), Sierra Young (North Carolina State University), Alina Zare (University of Florida, Gainesville), Supplemental workshop site:https://aiafs-aaai2022.github.io/. Accepted papers will be given the opportunity to present at the spotlight sessions during the workshop. KDD 2022 is a dual-track conference that provides distinct programming in research and applied data science. Accepted submissions will be notified latest by August 7th, 2022. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. Mitigating Cache-Based Side-Channel Attacks through Randomization: A Comprehensive System and Architecture Level Analysis. Spatial Auto-regressive Dependency Interpretable Learning Based on Spatial Topological Constraints. December 2020, July 21: Clarified that the workshop this year will be held, June 20: Paper notification is now extended to, Paper reviews are underway! ML-guided rare event modeling and system uncertainty quantification, Development of software, libraries, or benchmark datasets, and. 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. ACM Computing Surveys (CSUR), (impact factor: 10.28), accepted. Identification of information-theoretic quantities relevant for causal inference and discovery. International Journal of Digital Earth, (impact factor: 3.097), 25 Aug 2020, https://doi.org/10.1080/17538947.2020.1809723. Integration of probabilistic inference in training deep models. 1503-1512, Aug 2015. This policy also applies to papers that overlap substantially in technical content with papers previously published, accepted, or under review. Zhiqian Chen, Lei Zhang, Gaurav Kolhe, Hadi Mardani Kamali, Setareh Rafatirad, Sai Manoj Pudukotai Dinakarrao, Houman Homayoun, Chang-Tien Lu, Liang Zhao. ReForm: Static and Dynamic Resource-Aware DNN Reconfiguration Framework for Mobile Devices. The trustworthy issues of clinical AI methods were not discussed. the 56th Design Automation Conference (DAC 2019), accepted, (acceptance rate: 20%), Las Vegas, US, 2019. "STED: semi-supervised targeted-interest event detectionin in twitter." Additional information about formatting and style files is available here: : Full papers are limited to a total of 6 pages, including all content and references. In this workshop we would like to focus on a contrasting approach, to learn the architecture during training. Generative Deep Learning for Macromolecular Structure and Dynamics, Current Opinion in Structural Biology, (impact factor: 7.108), Section on Theory and Simulation/Computational Methods 67: 170-177, 2021 accepted. Yevgeniy Vorobeychik (Washington University in St. Louis), Bruno Sinopoli (Washington University in St. Louis), Jinghan Yang (Washington University in St. Louis), Bo Li (UIUC), Atul Prakash (University of Michigan), Supplemental Workshop site:https://jinghany.github.io/trase2022/. Topics of interest include, but are not limited to: One day, comprising keynote, paper presentations and panel sessions. and deep learning techniques (e.g. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), accepted. Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning. Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. arXiv preprint arXiv:2207.09542 (2022). Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features. We have the following keynote speakers confirmed: Andreas Holzinger (Medical Univ. Their results will be submitted in either a short paper or poster format. 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. Application-specific designs for explainable AI, e.g., healthcare, autonomous driving, etc. A message will appear on your application form if there is a risk that the time required to process the application and to send the answer, in addition to the time you will need to acquire study permits, will be too long for you to arrive for the beginning of the session. Out of these, around 20~30 papers are accepted. A tag already exists with the provided branch name. 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). CVPR 11 deadline . (Depending on the volume of submissions, we may be able to accommodate only a subset of them.). Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. At least one author of each accepted submission must register and present their paper at the workshop. Nowadays, machine learning solutions are widely deployed. Microsoft's Conference Management Toolkit is a hosted academic conference management system. Attendance is open to all; at least one author of each accepted paper must be virtually present at the workshop. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. "Online Spatial Event Forecasting in Microblogs. Chen Ling, Hengning Cao, Liang Zhao. 2020. Deep learning has achieved significant success for artificial intelligence (AI) in multiple fields. Property Controllable Variational Autoencoder via Invertible Mutual Dependence. Algorithms for secure and privacy-aware machine learning for AI. ), Learning with algebraic or combinatorial structure, Link analysis/prediction, node classification, graph classification, clustering for complex graph structures, Theoretical analysis of graph algorithms or models, Optimization methods for graphs/manifolds, Probabilistic and graphical models for structured data, Unsupervised graph/manifold embedding methods. Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. in Proceedings of the IEEE International Conference on Data Mining (ICDM 2018), regular paper (acceptance rate: 8.9%), Singapore, Dec 2018, accepted. We are excited to announce our upcoming workshop at KDD 2022 | Washington DC, U.S.: Decision Intelligence and Analytics for Online Marketplaces - Jobs, Ridesharing, Retail, and Beyond. SIGSPATIAL Special (invited paper), vo. LOG 2022 LOG '22 . First, large data sources, both conventionally used in social sciences (EHRs, health claims, credit card use, college attendance records) and unconventional (social networks, fitness apps), are now available, and are increasingly used to personalize interventions. SIAM International Conference on Data Mining (SDM 2023) (Acceptance Rate: 27.4%), accepted. Submissions will be accepted via the Easychair submission website. Sign-regularized multi-task learning. 2022. Negar Etemadyrad, Qingzhe Li, Liang Zhao. applications: ridesharing, online retail, food delivery, house rental, real estate, and more. Identification of key challenges and opportunities for future research. ; (2) Deep Learning (DL) approaches that can exploit large datasets, particularly Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL); (3) End-to-end learning methodologies that mend the gap between ML model training and downstream optimization problems that use ML predictions as inputs; (4) Datasets and benchmark libraries that enable ML approaches for a particular OR application or challenging combinatorial problems. Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, and Chang-TIen Lu. Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. Submission link:https://easychair.org/cfp/raisa-2022, William Streilein, MIT Lincoln Laboratory, 244 Wood St., Lexington, MA, 02420, (781) 981-7200, wws@ll.mit.edu, Olivia Brown (MIT Lincoln Laboratory, Olivia.Brown@ll.mit.edu), Rajmonda Caceres (MIT Lincoln Laboratory, Rajmonda.Caceres@ll.mit.edu), Tina Eliassi-Rad (Northeastern University, teliassirad@northeastern.edu), David Martinez (MIT Lincoln Laboratory, dmartinez@ll.mit.edu), Sanjeev Mohindra (MIT Lincoln Laboratory, smohindra@ll.mit.edu), Elham Tabassi (National Institute of Standards and Technology, elham.tabassi@nist.gov), Workshop URL:https://sites.google.com/view/raisa-2022/. in Proceedings of the 22st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016), applied data science track, accepted (acceptance rate: 19.9%), pp. 2022. 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. The Thirty-Sixth AAAI Conference on Artificial IntelligenceFebruary 28 and March 1, 2022Vancouver Convention CentreVancouver, BC, Canada AAAI is pleased to present the AAAI-22 Workshop Program. "Robust Regression via Heuristic Hard Thresholding". The submissions must follow the formatting guidelines for AAAI-22. The post-lunch session will feature a second keynote talk, two invited talks. [Best Paper Award]. Videos have become an omnipresent source of knowledge: courses, presentations, conferences, documentaries, live streams, meeting recordings, vlogs. The program of the workshop will include invited talks, paper presentations and a panel discussion. We invite participants to submit papers by the 12th of November, based on but not limited to, the following topics: RL in various formalisms: one-shot games, turn-based, and Markov games, partially-observable games, continuous games, cooperative games; deep RL in games; combining search and RL in games; inverse RL in games; foundations, theory, and game-theoretic algorithms for RL; opponent modeling; analyses of learning dynamics in games; evolutionary methods for RL in games; RL in games without the rules; search and planning; and online learning in games. Wang, Shiyu, Yuanqi Du, Xiaojie Guo, Bo Pan, and Liang Zhao. and Simone Stumpf (Univ. KDD 2022. Guangji Bai, Chen Ling, Yuyang Gao, Liang Zhao. Knowledge Discovery and Data Mining is an interdisciplinary area focusing upon methodologies and applications for extracting useful knowledge from data [1] . Metagraph Aggregated Heterogeneous Graph Neural Network for Illicit Traded Product Identification in Underground Market. "Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning." How can we engineer trustable AI software architectures? We encourage authors to contact the organizers to discuss possible overlap. SL-VAE: Variational Autoencoder for Source Localization in Graph Information Diffusion. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), vol. Comparison or integration of self-supervised learning methods and other semi-supervised and transfer learning methods in speech and audio processing tasks. 76, pp. The 30th International World Wide Web Conference, the Web Conference (WWW 2021), (acceptance rate: 20.6%), accepted. Each paper will be reviewed by three reviewers in double-blind. References will not count towards the page limit. Everyone in the Top-10 leaderboard submissions will have a guaranteed opportunity for an in-person oral/poster presentation. We invite the submission of papers with 4-6 pages. Moreover, to tackle and overcome several issues in personalized healthcare, information technology will need to evolve to improve communication, collaboration, and teamwork among patients, their families, healthcare communities, and care teams involving practitioners from different fields and specialties. Deep Graph Learning for Circuit Deobfuscation. This workshop covers (but not limited to) the following topics: , It is a one day workshop and includes: invited talks, interactive discussions, paper presentations, shared task presentations, poster session etc. CPM: A General Feature Dependency Pattern Mining Framework for Contrast Multivariate Time Series. 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. In nearly all applications, reliability, safety, and security of such systems is a critical consideration. Online. in Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), research track (acceptance rate: 14.2%), accepted, Alaska, USA, Aug 2019. If these formalities are not completed in time, you will have to file a new application at a later date. 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. For previous workshops held physically, each workshop attracts around 150~300 participants. KDD 2022 Brave new ideas to learn AI models under bias and scarcity.

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