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ORCIDSDM 2021: Virtual Event
- Carlotta Demeniconi, Ian Davidson:
Proceedings of the 2021 SIAM International Conference on Data Mining, SDM 2021, Virtual Event, April 29 - May 1, 2021. SIAM 2021, ISBN 978-1-61197-670-0 - Kailash Budhathoki, Mario Boley, Jilles Vreeken:
Discovering Reliable Causal Rules. 1-9 - Fabian Berns
, Christian Beecks:
Stochastic Time Series Representation for Interval Pattern Mining via Gaussian Processes. 10-18 - Sam Pinxteren, Toon Calders:
Efficient Permutation Testing for Significant Sequential Patterns. 19-27 - Dimitrios I. Diochnos, Theodore B. Trafalis:
Learning Reliable Rules under Class Imbalance. 28-36 - Christopher Hagedorn, Johannes Huegle:
GPU-Accelerated Constraint-Based Causal Structure Learning for Discrete Data. 37-45 - Chenghao Liu, Tao Lu, Zhiyong Cheng, Xin Wang, Jianling Sun, Steven C. H. Hoi:
Discrete Listwise Collaborative Filtering for Fast Recommendation. 46-54 - Guoyuan An, Sung Eui Yoon, Jae Yoon Kim, Lin Wang, Myoung Ho Kim:
GraphShop: Graph-based Approach for Shop-type Recommendation. 55-63 - Chen Li, Linmei Hu, Chuan Shi, Guojie Song, Yuanfu Lu:
Sequence-aware Heterogeneous Graph Neural Collaborative Filtering. 64-72 - Min Liu, Zhaohui Peng, Xiaohui Yu, Senzhang Wang, Qiao Song:
LDFeRR: A Fuel-efficient Route Recommendation Approach for Long-distance Driving Based on Historical Trajectories. 73-81 - Jianling Wang, Kaize Ding, Ziwei Zhu, James Caverlee:
Session-based Recommendation with Hypergraph Attention Networks. 82-90 - Dongkuan Xu, Wei Cheng, Jingchao Ni, Dongsheng Luo, Masanao Natsumeda, Dongjin Song, Bo Zong, Haifeng Chen, Xiang Zhang:
Deep Multi-Instance Contrastive Learning with Dual Attention for Anomaly Precursor Detection. 91-99 - N. Benjamin Erichson, Dane Taylor, Qixuan Wu, Michael W. Mahoney:
Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware. 100-108 - Charu C. Aggarwal, Yao Li, Philip S. Yu:
Signature-Based Anomaly Detection in Networks. 109-117 - Qizhou Wang, Sarah M. Erfani, Christopher Leckie, Michael E. Houle:
A Dimensionality-Driven Approach for Unsupervised Out-of-distribution Detection. 118-126 - Yi-Xuan Xu, Ming Pang, Ji Feng, Kai Ming Ting, Yuan Jiang, Zhi-Hua Zhou:
Reconstruction-based Anomaly Detection with Completely Random Forest. 127-135 - Alexander van der Grinten, Eugenio Angriman, Maria Predari, Henning Meyerhenke:
New Approximation Algorithms for Forest Closeness Centrality - for Individual Vertices and Vertex Groups. 136-144 - Ilie Sarpe, Fabio Vandin:
PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts. 145-153 - Sixie Yu, Leo Torres, Scott Alfeld, Tina Eliassi-Rad, Yevgeniy Vorobeychik:
POTION : Optimizing Graph Structure for Targeted Diffusion. 154-162 - Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra:
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding. 163-171 - Mark Heimann, Xiyuan Chen, Fatemeh Vahedian, Danai Koutra:
Refining Network Alignment to Improve Matched Neighborhood Consistency. 172-180 - Wei Du, Depeng Xu, Xintao Wu, Hanghang Tong:
Fairness-aware Agnostic Federated Learning. 181-189 - Kamrun Naher Keya, Rashidul Islam, Shimei Pan, Ian Stockwell, James R. Foulds:
Equitable Allocation of Healthcare Resources with Fair Survival Models. 190-198 - Haoyu Wang, Hengtong Zhang, Yaqing Wang, Jing Gao:
Fair Classification Under Strict Unawareness. 199-207 - Xiaoting Li, Lingwei Chen, Dinghao Wu:
Turning Attacks into Protection: Social Media Privacy Protection Using Adversarial Attacks. 208-216 - Aria Rezaei, Jie Gao, Anand D. Sarwate:
Influencers and the Giant Component: The Fundamental Hardness in Privacy Protection for Socially Contagious Attributes. 217-225 - Chandrashekhar Lavania, Kai Wei, Rishabh K. Iyer, Jeff A. Bilmes:
A Practical Online Framework for Extracting Running Video Summaries under a Fixed Memory Budget. 226-234 - Kaixin Wang, Cheng Long, Yongxin Tong, Jie Zhang, Yi Xu:
Adaptive Holding for Online Bottleneck Matching with Delays. 235-243 - Boris Wiegand, Dietrich Klakow, Jilles Vreeken:
Mining Easily Understandable Models from Complex Event Logs. 244-252 - Wenchong He, Arpan Man Sainju, Zhe Jiang, Da Yan:
Deep Neural Network for 3D Surface Segmentation based on Contour Tree Hierarchy. 253-261 - Akihiro Yamaguchi, Ken Ueno:
Learning Time-series Shapelets via Supervised Feature Selection. 262-270 - M. Maruf, Anuj Karpatne:
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling Approach. 271-279 - Houquan Zhou, Shenghua Liu, Kyuhan Lee, Kijung Shin, Huawei Shen, Xueqi Cheng:
DPGS: Degree-Preserving Graph Summarization. 280-288 - Leo Huang, Andrew J. Graven, David Bindel:
Density of States Graph Kernels. 289-297 - Janis Kalofolias, Pascal Welke, Jilles Vreeken:
SUSAN: The Structural Similarity Random Walk Kernel. 298-306 - Jinsung Jeon, Jing Liu, Jayoung Kim, Jaehoon Lee, Noseong Park, Jamie Jooyeon Lee, Özlem Uzuner, Sushil Jajodia:
Scalable Graph Synthesis with Adj and 1 - Adj. 307-315 - Kunpeng Liu, Haibo Huang, Wei Zhang, Ahmad Hariri, Yanjie Fu, Kien A. Hua:
Multi-Armed Bandit Based Feature Selection. 316-323 - Leonor Silva, Helena Galhardas, Vasco Manquinho, Rui Henriques:
UNIANO: robust and efficient anomaly consensus in time series sensitive to cross-correlated anomaly profiles. 324-332 - Ryoma Sato, Makoto Yamada, Hisashi Kashima:
Random Features Strengthen Graph Neural Networks. 333-341 - Wei Fan, Kunpeng Liu, Hao Liu, Ahmad Hariri, Dejing Dou, Yanjie Fu:
AutoGFS: Automated Group-based Feature Selection via Interactive Reinforcement Learning. 342-350 - Mario Boley, Simon Teshuva, Pierre Le Bodic, Geoffrey I. Webb:
Better Short than Greedy: Interpretable Models through Optimal Rule Boosting. 351-359 - Fabian Berns, Christian Beecks:
Complexity-Adaptive Gaussian Process Model Inference for Large-Scale Data. 360-368 - Tomoya Sakai, Naoto Ohsaka:
Predictive Optimization with Zero-Shot Domain Adaptation. 369-377 - Alexandre Millot, Rémy Cazabet, Jean-François Boulicaut:
Exceptional Model Mining meets Multi-objective Optimization. 378-386 - Alexander Marx, Lincen Yang, Matthijs van Leeuwen:
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. 387-395 - Zhongnian Li, Tao Zhang, Wei Shao, Songcan Chen, Daoqiang Zhang:
Sign-aware Perturbations Regression. 396-404 - Hongchang Gao, Heng Huang:
Faster Stochastic Second Order Method for Large-Scale Machine Learning Models. 405-413 - Leqian Zheng, Hau Chan, Grigorios Loukides, Minming Li:
Maximizing Approximately k-Submodular Functions. 414-422 - Gözde Özcan, Armin Moharrer, Stratis Ioannidis:
Submodular Maximization via Taylor Series Approximation. 423-431 - Kenya Tajima, Yoshihiro Hirohashi, Esmeraldo Ronnie Rey Zara, Tsuyoshi Kato:
Frank-Wolfe algorithm for learning SVM-type multi-category classifiers. 432-440 - Hongchang Gao, Gang Wu, Ryan A. Rossi:
Provable Distributed Stochastic Gradient Descent with Delayed Updates. 441-449 - Laurens Devos, Wannes Meert, Jesse Davis:
Verifying Tree Ensembles by Reasoning about Potential Instances. 450-458 - Xin Dai, Xiangnan Kong, Tian Guo, Yixian Huang:
CiNet: Redesigning Deep Neural Networks for Efficient Mobile-Cloud Collaborative Inference. 459-467 - Peiran Li, Fang Guo, Jingbo Shang:
"Misc"-Aware Weakly Supervised Aspect Classification. 468-476 - Qi Zhu, Fang Guo, Jingjing Tian, Yuning Mao, Jiawei Han:
SUMDocS: Surrounding-aware Unsupervised Multi-Document Summarization. 477-485 - Yuanlin Yang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang:
Deep Multi-type Objects Muli-view Multi-instance Multi-label Learning. 486-494 - Xiaoyong Jin, Yu-Xiang Wang, Xifeng Yan:
Inter-Series Attention Model for COVID-19 Forecasting. 495-503 - Senzhang Wang, Meiyue Zhang, Hao Miao, Philip S. Yu:
MT-STNets: Multi-Task Spatial-Temporal Networks for Multi-Scale Traffic Prediction. 504-512 - Peiyu Yi, Feihu Huang, Jian Peng:
A Fine-grained Graph-based Spatiotemporal Network for Bike Flow Prediction in Bike-sharing Systems. 513-521 - Farbod Taymouri, Marcello La Rosa, Sarah M. Erfani:
A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences. 522-530 - Jaemin Yoo, U Kang:
Attention-Based Autoregression for Accurate and Efficient Multivariate Time Series Forecasting. 531-539 - Han Xu, Yaxin Li, Xiaorui Liu, Hui Liu, Jiliang Tang:
Yet Meta Learning Can Adapt Fast, it Can Also Break Easily. 540-548 - Kathrin Grosse, Michael Backes:
Do winning tickets exist before DNN training? 549-557 - Skyler Seto, Martin T. Wells, Wenyu Zhang:
HALO: Learning to Prune Neural Networks with Shrinkage. 558-566 - Lu Jiang, Pengyang Wang, Ke Cheng, Kunpeng Liu, Minghao Yin, Bo Jin, Yanjie Fu:
EduHawkes: A Neural Hawkes Process Approach for Online Study Behavior Modeling. 567-575 - Can Qin, Lichen Wang, Qianqian Ma, Yu Yin, Huan Wang, Yun Fu:
Contradictory Structure Learning for Semi-supervised Domain Adaptation. 576-584 - Xianli Zhang, Buyue Qian, Yang Li, Yang Liu, Xi Chen, Chong Guan, Chen Li:
Learning Robust Patient Representations from Multi-modal Electronic Health Records: A Supervised Deep Learning Approach. 585-593 - Kejing Yin, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon:
TedPar: Temporally Dependent PARAFAC2 Factorization for Phenotype-based Disease Progression Modeling. 594-602 - Charilaos I. Kanatsoulis, Nicholas D. Sidiropoulos:
TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing. 603-611 - Xiaowei Jia, Jacob Zwart, Jeffrey M. Sadler, Alison P. Appling, Samantha Oliver, Steven Markstrom, Jared Willard, Shaoming Xu, Michael S. Steinbach, Jordan S. Read, Vipin Kumar:
Physics-Guided Recurrent Graph Model for Predicting Flow and Temperature in River Networks. 612-620 - Xiaowei Jia, Beiyu Lin, Jacob Zwart, Jeffrey M. Sadler, Alison P. Appling, Samantha Oliver, Jordan S. Read:
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks. 621-629 - Hu Ding, Tan Chen, Fan Yang, Mingyue Wang:
A Data-Dependent Algorithm for Querying Earth Mover's Distance with Low Doubling Dimensions. 630-638 - Omer Anjum, Mohammad Almasri, Jinjun Xiong, Wen-Mei W. Hwu:
PhraseScope: An Effective and Unsupervised Framework for Mining High Quality Phrases. 639-647 - Federico Altieri, Andrea Pietracaprina, Geppino Pucci, Fabio Vandin:
Scalable Distributed Approximation of Internal Measures for Clustering Evaluation. 648-656 - Priya Mani, Carlotta Domeniconi, Igor Griva:
Unsupervised Selective Manifold Regularized Matrix Factorization. 657-665 - Tsung-Yu Hsieh, Yiwei Sun, Suhang Wang, Vasant G. Honavar:
Functional Autoencoders for Functional Data Representation Learning. 666-674 - Jie Bu, Anuj Karpatne:
Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs. 675-683 - Bonny Banerjee, Masoumeh Heidari Kapourchali, Murchana Baruah, Mousumi Deb, Kenneth Sakauye, Mette S. Olufsen:
Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions. 684-692 - Tianqi Wang, Fenglong Ma, Yaqing Wang, Tang Tang, Longfei Zhang, Jing Gao:
Towards Learning Outcome Prediction via Modeling Question Explanations and Student Responses. 693-701 - Daixin Wang, Zhiqiang Zhang, Jun Zhou, Peng Cui, Jingli Fang, Quanhui Jia, Yanming Fang, Yuan Qi:
Temporal-Aware Graph Neural Network for Credit Risk Prediction. 702-710 - Jinsung Jeon, Dongeun Lee, Seunghyun Hwang, Soyoung Kang, Noseong Park, Duanshun Li, Kookjin Lee, Jing Liu:
Large-Scale Flight Frequency Optimization with Global Convergence in the US Domestic Air Passenger Markets. 711-719 - Jing Ya, Tingwen Liu, Jiangxia Cao, Li Guo:
Heterogeneous Graph Neural Networks for Query-focused Summarization. 720-728 - Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron:
Dynamic Graph Convolutional Networks Using the Tensor M-Product. 729-737 - Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao:
Disentangled Dynamic Graph Deep Generation. 738-746 - Jiayu He, Matloob Khushi, Nguyen Hoang Tran, Tongliang Liu:
Robust Dual Recurrent Neural Networks for Financial Time Series Prediction. 747-755 - Balasubramaniam Srinivasan, Da Zheng, George Karypis:
Learning over Families of Sets - Hypergraph Representation Learning for Higher Order Tasks. 756-764
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