Liang Zhao, Qian Sun, Jieping Ye, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. 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! We expect 60-70 participants. iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow. "Online and Distributed Robust Regressions under Adversarial Data Corruption", in Proceedings of the IEEE International Conference on Data Mining (ICDM 2017) , regular paper; (acceptance rate: 9.25%), pp. The submission website ishttps://cmt3.research.microsoft.com/PPAI2022. 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. "Spatiotemporal Event Forecasting in Social Media." ADMM for Efficient Deep Learning with Global Convergence. Our goal is to build a stronger community of researchers exploring these methods, and to find synergies among these related approaches and alternatives. Comparison or integration of self-supervised learning methods and other semi-supervised and transfer learning methods in speech and audio processing tasks. We invite paper submission with a focus that aligns with the goals of this workshop. This manual extraction process is usually inefficient, error-prone, and inconsistent. CoRL 2023 97 days 17h 29m 15s November 06-09, 2023. Yuyang Gao, Tanmoy Chowdhury (co-first author), Lingfei Wu, Liang Zhao. FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers. There is increasing evidence that enabling AI technology has the potential to aid in the aforementioned paradigm shift. How can we engineer trustable AI software architectures? The 19th International Conference on Data Mining (ICDM 2019), short paper, (acceptance rate: 18.05%), Beijing, China, accepted. Iclr 2022 Hosein Mohammadi Makrani, Farnoud Farahmand, Hossein Sayadi, Sara Bondi, Sai Manoj Pudukotai Dinakarrao, Liang Zhao, Avesta Sasan, Houman Homayoun, and Setareh Rafatirad,. The workshop will be a one-day meeting and will include a number of technical sessions, a virtual poster session where presenters can discuss their work, with the aim of further fostering collaborations, multiple invited speakers covering crucial challenges for the field of privacy-preserving AI applications, including policy and societal impacts, a tutorial talk, and will conclude with a panel discussion. At least one author of each accepted submission must be present at the workshop. The cookie is used to store the user consent for the cookies in the category "Performance". text, images, and videos). Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Share. All the workshop chairs, most of the Committees, and the authors of the accepted papers will attend the workshop also. Options include pruning a trained network or training many networks automatically. 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. Graph Neural Networks: Foundations, Frontiers, and Applications. It has gained popularity in some domains such as image classification, speech recognition, smart city, and healthcare. Contrast Feature Dependency Pattern Mining for Controlled Experiments with Application to Driving Behavior. [Submission deadline extended, June 3] KDD 2022 Workshop on - INFORMS We will end the workshop with a panel discussion by top researchers in the field. Liang Zhao, Junxiang Wang, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. TG-GAN: Continuous-time Temporal Graph Deep Generative Models with Time-Validity Constraints. 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. Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, and Sai Dinakarrao. System reports will be presented during poster sessions. Each paper will be reviewed by three reviewers in double-blind. We encourage long papers, short papers and demo papers. Causality has received significant interest in ML in recent years in part due to its utility for generalization and robustness. Registration Opens: Feb 02 '22 02:00 PM UTC: Registration Cancellation Refund Deadline: Apr 18 '22(Anywhere on Earth) Paper Submissions Abstract Submission Deadline: Sep 29 '21 12:00 AM UTC: Paper Submission deadline: Oct 06 '21 12:00 AM . Yuyang Gao, Tong Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Hong, and Liang Zhao. 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. The consideration and experience of adversarial ML from industry and policy making. A primary reason for this is the inherent long-tailed nature of our world, and the need for algorithms to be trained with large amounts of data that includes as many rare events as possible. Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. Yujie Fan, Yanfang (Fanny) Ye, Qian Peng, Jianfei Zhang, Yiming Zhang, Xusheng Xiao, Chuan Shi, Qi Xiong, Fudong Shao, and Liang Zhao. System reports should also follow the AAAI 2022 formatting guidelines and have 4-6 pages including references. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. The cookie is used to store the user consent for the cookies in the category "Analytics". 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. 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. 105, no. In addition, authors can provide an optional two (2) page supplement at the end of their submitted paper (it needs to be in the same PDF file) focused on reproducibility. Out of these, around 20~30 papers are accepted. 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. Welcome to DLG-KDD'22! - Bitbucket The submissions need to be anonymized. Apr 11-14, 2022. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. Benchmarks to reliably evaluate attacks/defenses and measure the real progress of the field. 2022. 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. There will be live Q&A sessions at the end of each talk and oral presentation. arXiv preprint arXiv:2207.09542 (2022). Topics of interest include but are not limited to: Acronyms, i.e., short forms of long phrases, are common in scientific writing. Submissions are limited to a maximum of four (4) pages, including all content and references, and must be in PDF format. 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. ; (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. For example, AI tools are built to ease the workload for teachers. 2022. 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. Besides academia, many companies and institutions are researching on topics specific to their particular domains. Microsoft's Conference Management Toolkit is a hosted academic conference management system. The academic session will focus on most recent research developments on GNNs in various application domains. Xuchao Zhang, Liang Zhao, Arnold Boedihardjo, and Chang-Tien Lu. 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. Online . Algorithms and theories for trustworthy AI models. 10 (2014): e110206. Both the research papers track and the applied data science papers track will take . Integration of probabilistic inference in training deep models. Authors are invited to send a contribution in the AAAI-22 proceedings format. Given the ever-increasing role of the World Wide Web as a source of information in many domains including healthcare, accessing, managing, and analyzing its content has brought new opportunities and challenges. While the research community is converging on robust solutions for individual AI models in specific scenarios, the problem of evaluating and assuring the robustness of an AI system across its entire life cycle is much more complex.
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