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. [Bests of ICDM]. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. 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. 2022. Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. The AAAI-22 workshop program includes 39 workshops covering a [] ISPRS International Journal of Geo-Information (IJGI), (impact factor: 1.502), 5.10 (2016): 193. Brave new ideas to learn AI models under bias and scarcity. We are interested in a broad range of topics, both foundational and applied. Registration information will be mailed directly to all invited participants in December. The accepted papers will be allocated either a contributed talk or a poster presentation. 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. This cookie is set by GDPR Cookie Consent plugin. 1, 2022: Call For Paper: The Undergraduate Consortium at SIGKDD 2022 is available at, Mar. Fine tuning a neural network is very time consuming and far from optimal. Would you like to mark this message as the new best answer? An example of the latter is theCascade Correlation algorithm, as well as others that incrementally build or modify a neural network during training, as needed for the problem at hand. All these changes require novel solutions, and the AI community is well-positioned to provide both theoretical- and application-based methods and frameworks. Large-scale Cost-aware Classification Using Feature Computational Dependency Graph. Advances in complex engineering systems such as manufacturing and materials synthesis increasingly seek artificial intelligence/machine learning (AI/ML) solutions to enhance their design, development, and production processes. We will also have a panel discussion on the current and future of GNNs on both research and industry. Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. 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. By entering your email, you consent to receive communications from UdeM. Disentangled Dynamic Graph Deep Generation, SIAM International Conference on Data Mining (SDM 2021), (acceptance rate: 21.3%), accepted. Qingzhe Li, Amir A. Fanid, Martin Slawski, Yanfang Ye, Lingfei Wu, Kai Zeng, and Liang Zhao. The deadline for the submissions is July 31st, 2022 11.59 PM (Anywhere on Earth time). Advances in IML promise to make AIs more accessible and controllable, more compatible with the values of their human partners and more trustworthy. Thirty-First AAAI Conference on Artificial Intelligence, pp. 105, no. KDD 2023 August 06-10, 2023. Papers should be up to 4 pages in length (excluding references) formatted using the AAAI template. Published March 4, 2023 4:51 a.m. PST. If these formalities are not completed in time, you will have to file a new application at a later date. 2022. DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. Everyone in the Top-10 leaderboard submissions will have a guaranteed opportunity for an in-person oral/poster presentation. Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. 963-971, Apr-May 2015. This date takes priority over those shown below and could be extended for some programs. Deadlines are shown in America/Los_Angeles time. Online marketplace is a digital platform that connects buyers (demand) and sellers (supply) and provides exposure opportunities that individual participants would not otherwise have access to. 2022. 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). "Multi-Task Learning for Spatio-Temporal Event Forecasting." Attendance is open to all registered participants. Exploring the limits of self-supervised learning approaches for speech and audio processing, for example, adverse environment conditions, multiple languages, or generalization across downstream tasks. 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). The first AAAI Workshop on AI for Design and Manufacturing, ADAM, aims to bring together researchers from core AI/ML, design, manufacturing, scientific computing, and geometric modeling. Objectives of ADAM include outlining the main research challenges in this area, cross-pollinating collaborations between AI researchers and domain experts in engineering design and manufacturing, and sketching open problems of common interest. This policy also applies to papers that overlap substantially in technical content with papers previously published, accepted, or under review. CoRL 2023 97 days 17h 29m 15s November 06-09, 2023. In this workshop we would like to focus on a contrasting approach, to learn the architecture during training. The papers may consist of up to seven pages of technical content plus up to two additional pages for references. SUPERB is a benchmarking platform that allows the community to train, evaluate, and compare the speech representations on diverse downstream speech processing tasks. This workshop aims to bring researchers from these diverse but related fields together and embark on interesting discussions on new challenging applications that require complex system modeling and discovering ingenious reasoning methods. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. These challenges and issues call for robust artificial intelligence (AI) algorithms and systems to help. of London). Like other systems, ML systems must meet quality requirements. Using a social media account will simply make the application process easier: none of your activities on this site will be posted to your profile. We will also have a video component for remote participation. Federated learning (FL) is one promising machine learning approach that trains a collective machine learning model using sharing data owned by various parties. "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. It further combines academia and industry in a quest for well-founded practical solutions. The invited speakers, who are well-recognized experts of the field, will give a 30 minute talk. The bottleneck to discovery is now our ability to analyze and make sense of heterogeneous, noisy, streaming, and often massive datasets. This topic encompasses forms of Neural Architecture Search (NAS) in which the performance properties of each architecture, after some training, are used to guide the selection of the next architecture to be tried. The cookie is used to store the user consent for the cookies in the category "Other. Guangji Bai, Johnny Torres, Junxiang Wang, Liang Zhao, Carmen Vaca, Cristina Abad. Optimal transport theory, including statistical and geometric aspects; Gromov-Wasserstein distance and its variants; Bayesian inference for/with optimal transport; Gromovization of machine learning methods; Optimal transport-based generative modeling. IBM Research, 2018. Check the deadlines for submitting your application. 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. Yiming Zhang, Yujie Fan, Wei Song, Shifu Hou, Yanfang Ye, Xin Li, Liang Zhao, Chuan Shi, Jiabin Wang, Qi Xiong. All submissions will be peer-reviewed. algorithms applied to the above topics: deep learning, reinforcement learning, multi-armed bandits, causal inference, mathematical programming, and stochastic optimization. Qingzhe Li, Liang Zhao, Yi-Ching Lee, Yanfang Ye, Jessica Lin, and Lingfei Wu. 27, 2022: Please check out Speical Days at, Apr. Submissions that are already accepted or under review for another conference or already accepted for a journal are not accepted. We also use third-party cookies that help us analyze and understand how you use this website. Attendance is open to all. 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. 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. The post-lunch session will feature a second keynote talk, two invited talks. Balaraman Ravindran (Indian Institute of Technology Madras, India ravi@cse.iitm.ac.in), Balaraman Ravindran (Indian Institute of Technology Madras, India Primary contact (ravi@cse.iitm.ac.in), Kristian Kersting (TU Darmstadt, Germany, kersting@cs.tu-darmstadt.de), Sriraam Natarajan (Univ of Texas Dallas, USA, Sriraam.Natarajan@utdallas.edu), Ginestra Bianconi (Queen Mary University of London, UK, ginestra.bianconi@gmail.com), Philip S. Chodrow (University of California, Los Angeles, USA, phil@math.ucla.edu) Tarun Kumar (Indian Institute of Technology Madras, India, tkumar@cse.iitm.ac.in), Deepak Maurya (Purdue University, India, maurya@cse.iitm.ac.in), Shreya Goyal (Indian Institute of Technology Madras, India, Goyal.3@iitj.ac.in), Workshop URL:https://sites.google.com/view/gclr2022/. ), 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). It highlights the importance of declarative languages that enable such integration for covering multiple formalisms at a high-level and points to the need for building a new generation of ML tools to help domain experts in designing complex models where they can declare their knowledge about the domain and use data-driven learning models based on various underlying formalisms. 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. The submissions need to be anonymized. KDD 2022. "Online Spatial Event Forecasting in Microblogs. This thread already has a best answer. in the proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), (acceptance rate: 26%), pp. The financial services industry relies heavily on AI and Machine Learning solutions across all business functions and services. Algorithms and theories for trustworthy AI models. We invite the submission of papers with 4-6 pages. Please note that foreign students must allow for 3 to 6 months to complete all the formalities required to study in Canada. Researchers from related fields are invited to submit papers on the recent advances, resources, tools, and upcoming challenges for SDU. Research track papers reporting the results of ongoing or new research, which have not been published before. Current rates of progress are insufficient, making it impossible to meet this goal without a technological paradigm shift. Authors are invited to send a contribution in the AAAI-22 proceedings format. 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. How can we engineer trustable AI software architectures? Hosein Mohammadi Makrani, Farnoud Farahmand, Hossein Sayadi, Sara Bondi, Sai Manoj Pudukotai Dinakarrao, Liang Zhao, Avesta Sasan, Houman Homayoun, and Setareh Rafatirad,. "A Topic-focused Trust Model for Twitter." Registration in each workshop is required by all active participants, and is also open to all interested individuals. All questions about submissions should be emailed to nurendra@vt.edu, AmazonKDDCup2022: KDD Cup 2022 Workshop: ESCI Challenge for Improving Product Search, Washington DC, DC, United States, August 17, 2022, https://easychair.org/conferences/?conf=amazonkddcup2022, https://www.acm.org/publications/proceedings-template. Prediction-time Efficient Classification Using Feature Computational Dependencies. 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. 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. 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. and deep learning techniques (e.g. We plan to invite 2-4 keynote speakers from prestigious universities and leading industrial companies. 19-25, 2016. The VTU workshops accepts both short paper (4 pages) and long paper (8 pages). The trustworthy issues of clinical AI methods were not discussed. 2022. Small Molecule Generation via Disentangled Representation Learning. 2022. Taseef Rahman, Yuanqi Du, Liang Zhao, Amarda Shehu. "GA-based principal component selection for production performance estimation in mineral processing." We would especially like to highlight approaches that are qualitatively different from some popular but computationally intensive NAS methods. 2020. Submissions will be assessed based on their novelty, technical quality, significance of impact, interest, clarity, relevance, and reproducibility. However, the performance and efficiency of these techniques are big challenges for performing real-time applications. Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. An Invertible Graph Diffusion Model for Source Localization. Call for Participation The 3rd KDD Workshop on Data-driven Humanitarian Mapping and Policymaking solicits research papers, case studies, vision papers, software demos, and extended abstracts. 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. Algorithms and theories for explainable and interpretable AI models. iDetective: An Intelligent System for Automatic Identification of Key Actors in Online Hack Forums. Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet. 1-11, Feb 2016. Authors of accepted papers will be invited to participate. KDD 2022 : Chen Ling, Junji Jiang, Junxiang Wang, Liang Zhao. 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 39th IEEE International Conference on Data Engineering (ICDE 2023), accepted. The current research in this area is focused on extending existing ML algorithms as well as network science measures to these complex structures. "Robust Regression via Heuristic Hard Thresholding". Government day with NSF, NIH, DARPA, NIST, and IARPA, Local industries in the DC Metro Area, including the Amazons second headquarter, New initiatives at KDD 2022: undergraduate research and poster session, Early career research day with postdoctoral scholars and assistant professors in a mentoring workshop and panel, Workshops and hands-on tutorials on emerging topics. If the admission deadline for international applicants is past, we suggest that you choose another session to begin your studies. Disentangled Spatiotemporal Graph Generative Model. 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. The workshop organizers invite paper submissions on the following (and related) topics: This workshop will be a one-day workshop, featuring invited speakers, poster presentations, and short oral presentations of selected accepted papers. Characterization of fundamental limits of causal quantities using information theory. Accepted papers will be given the opportunity to present at the spotlight sessions during the workshop. Submissions are limited to 4 pages, not including references. Optimal transport-based machine learning paradigms; Trustworthy machine learning from the perspective of optimal transport. This is a 1-day workshop involving talks by pioneer researchers from respective areas, poster presentations, and short talks of accepted papers. These abrupt changes impacted the environmental assumptions used by AI/ML systems and their corresponding input data patterns. Topics of interest include, but are not limited to: Paper submissions will be in two formats: full paper (8 pages) and position paper (4 pages): The submission website ishttps://easychair.org/conferences/?conf=trase2022. ), Graduate (master's, specialized graduate diploma (DESS), etc. Furthermore, leveraging AI to connect disparate social networks amongst teachers \\cite{karimi2020towards}, we may be able to provide greater resources for their planning, which have been shown to significantly affect students achievement. 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. Deadline: Fri Jun 09 2023 04:59:00 GMT-0700 Yahoo! Integration of AI-based approaches with engineering prototyping and manufacturing. 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. A Systematic Survey on Deep Generative Models for Graph Generation. Transfer learning methods for business document reading and understanding. Yanfang Ye, Yiming Zhang, Yujie Fan, Chuan Shi and Liang Zhao. 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. Distributed Self-Paced Learning in Alternating Direction Method of Multipliers. The theme of the hack-a-thon will be decided before submission is closed and will be focused around finding creative solutions to novel problems in health. Recently developed tools and cutting-edge methodologies coming from the theory of optimal transport have proved to be particularly successful for these tasks. ACM Transactions on Knowledge Discovery from Data (TKDD), (impact factor: 3.089), accepted. Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, and Chang-TIen Lu. Submission site:https://cmt3.research.microsoft.com/ITCI2022, Murat Kocaoglu, Chair (Purdue University, mkocaoglu@purdue.edu), Negar Kiyavash (EPFL, negar.kiyavash@epfl.ch), Todd Coleman (UCSD, tpcoleman@ucsd.edu), Supplemental workshop site:https://sites.google.com/view/itci22. ), 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. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2013), demo track, pp. The adversarial ML could also result in potential data privacy and ethical issues when deploying ML techniques in real-world applications. Poster/short/position papers: We encourage participants to submit preliminary but interesting ideas that have not been published before as short papers. We will also organize 3 shared tasks in this workshop: punctuation restoration, domain adaptation for punctuation restoration, and chitchat detection. Incomplete Label Multi-Task Ordinal Regression for Spatial Event Scale Forecasting. The final schedule will be available in November. 5 (2014): 1447-1459. Jinliang Ding, Liang Zhao, Changxin Liu, and Tianyou Chai. Short or position papers of up to 4 pages are also welcome. Workshops will be held Monday and Tuesday, February 28 and March 1, 2022. Papers will be peer-reviewed and selected for spotlight and/or poster presentation. IEEE Transactions on Knowledge and Data Engineering (TKDE), (impact factor: 6.977), accepted. Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework. Abstracts of the following flavors will be sought: (1) research ideas, (2) case studies (or deployed projects), (3) review papers, (4) best practice papers, and (5) lessons learned. Welcome to the home of the 2023 ACM SIGMOD/PODS Conference, to be held in the Seattle metropolitan area, Washington, USA, on June 18 - June 23, 2023. Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, and Chang-Tien Lu. 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. Submissions should be formatted using the AAAI-2022 Author Kit. Liang Zhao, Amir Alipour-Fanid, Martin Slawski and Kai Zeng. 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. Accepted papers will be published in the workshop proceedings. All the submissions must follow the AAAI-22 formatting guidelines, camera-ready style. We will use double-blind reviewing. Nowadays, machine learning solutions are widely deployed. Such systems are better modeled by complex graph structures such as edge and vertex labeled graphs (e.g., knowledge graphs), attributed graphs, multilayer graphs, hypergraphs, temporal/dynamic graphs, etc. How do metrics of capability and generality, and the trade-offs with performance affect safety? 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. 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/. Consequently, standard notions of software quality and reliability such as deterministic functional correctness, black box testing, code coverage, and traditional software debugging become practically irrelevant for ML systems. Winter. Information theoretic quantities (entropy, mutual information, divergence) estimation, Information theoretic methods for out-of-domain generalization and relevant problems (such as robust transfer learning and lifelong learning), Information theoretic methods for learning from limited labelled data, such as few-shot learning, zero-shot learning, self-supervised learning, and unsupervised learning, Information theoretic methods for the robustness of DNNs in AI systems, The explanation of deep learning models (in AI systems) with information-theoretic methods, Information theoretic methods in different AI applications (e.g., NLP, healthcare, robotics, finance).