ODD Workshop
@odd_workshop
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6th Outlier Detection and Description Workshop, co-located with #KDD2021 https://t.co/L5jbnaF4Eb
Joined April 2021
Interesting benchmark for Fraud created by @awscloud @groverpr4
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Estimating out-of-distribution (OOD) performance is hard because labeled data is expensive. Can we predict OOD performance w/ only _unlabeled data_? In our work ( https://t.co/1OyQyGdjKe), we show this can be done using models’ agreement. w/ @yidingjiang, Aditi R., @zicokolter
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#CFP Our #KDD2022 workshop on anomaly and novelty detection is seeking for paper submissions. We commit to a non-archival workshop. Dual submission is allowed! Submission deadline: May 26th, 2022(23:59 UTC-12) Website: https://t.co/aLUmk56VVK In conjunction with @kdd_news
sites.google.com
Topics This workshop will feature the most recent artificial intelligence advances for detection, explanation and accommodation of anomalies and novelties. It targets both academic researchers and...
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Seeing a lot of great OSS forecasting packages taking shape lately! Greykite (brand new from LinkedIn): https://t.co/79dbjdIqSZ Orbit (released last year by Uber): https://t.co/GnXxFKTMom
https://t.co/4jLomE2Pfv
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While we wait for NSDI's sessions, we had a cool presentation today form Davide Sanvito, about our early results in rethinking system monitoring: "Learning What to Monitor for Efficient Anomaly Detection" just happened at #EuroMLSys @EuroSys_conf @robegs @sharan_7f000001
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Excited about our accepted paper at @TheWebConf "MemStream: Memory-Based Streaming Anomaly Detection" Preprint: https://t.co/91si9Lp6zL Code: https://t.co/Csv3kIJU8X
#TheWebConf #WWW2022 #TheWebConf2022
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Outlier Detection? Anomaly Detection? Novelty Detection? Open Set Recognition? OOD Detection? 🤨 What are they?🤔 Are they different?🧐 How to solve them?😕 Check out our latest survey "Generalized OOD Detection" to answer them all! https://t.co/7v4B8wBWol
https://t.co/yKWpXhNmlF
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Looking to hire a post doc researcher at CMU, for a project on graph anomaly detection with neural networks. Job posting and details at https://t.co/3cmHtMXkHA Contact me at lakoglu@andrew.cmu.edu Please feel free to forward to whom you think would be interested.
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ODD Workshop at #KDD2021 starts from 8am Pacific time. There are 6 exciting keynote talks and a panel discussion on fairness in outlier detection. https://t.co/hFcMsoOZn7
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Glad to announce our #KDD2021 Workshop on Outlier Detection and Description @odd_workshop. Jointly organized with Bryan Hooi, @leman_akoglu, @souravc83, Xiaodong Jiang, @ManishGuptaMG1 Deadline: May 20, 2021 Details: https://t.co/52G8dvw8Lh
@kdd_news
#Anomaly #DataScience
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8. Anomaly Detection and Automated Labeling for Voter Registration File Changes Sam F Royston, Courtenay Cotton
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6. Anomaly Alignment Across Multiple Attributed Networks Jie Zhang, Nannan Wu, Wenjun Wang, ying sun, Siddharth Bhatia 7. CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System Huiling Qin, Xianyuan Zhan, Yu Zheng
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4. Scrutinizing Shipment Records To Thwart Illegal Timber Trade @devDdata, Sathappan Muthiah, John Simeone, Amelia Meadows, @profnaren 5. Choosing Effective Projections for Fast and Accurate Anomaly Detection Chen Almagor, Yedid Hoshen
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3. The Effect of Hyperparameter Tuning on Comparative Evaluation of Anomaly Detection Methods Jonas Soenen, Elia Van Wolputte, @LorenzoPerini95, @VercruyssenV, @wannesm, @jessejdavis1, Hendrik Blockeel
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Accepted Papers at ODD 2021: 1. Scalable Change Point Detection for Dynamic Graphs @shenyangHuang, @grwip, @ReiRabb 2. Out-of-Distribution Detection and Fairness Assessment in Dermatology Hannah H Kim, Girmaw Abebe Tadesse, @RTFMCelia, @PhonesDrones, @krvarshney
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8. Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering https://t.co/VWURXiBBDG Cheng Feng, Pengwei Tian
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7. Practical Approach to Asynchronous Multivariate Time Series Anomaly Detection and Localization Ahmed Abdulaal, Zhuanghua Liu, Tomer Lancewicki
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6. Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding Zhihan Li, Youjian Zhao, Jiaqi Han, Ya Su, Rui Jiao, Xidao Wen, Dan Pei
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5. Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection https://t.co/f95bEZWlzd Zhiwei Wang, @zhengzhang, Jingchao Ni, Hui Liu, Haifeng Chen, @tangjiliang
arxiv.org
Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed increasing efforts...
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4. Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies Lev Faivishevsky, Adi Szeskin, Ashwin k Muppalla, Ravid Ziv, Ronen Laperdon, Benjamin Melloul, Tahi Hollander, Tom Hope, Amitai Armon
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