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ORCID21st ICDM 2021: Auckland, New Zealand
- James Bailey, Pauli Miettinen, Yun Sing Koh, Dacheng Tao, Xindong Wu:
IEEE International Conference on Data Mining, ICDM 2021, Auckland, New Zealand, December 7-10, 2021. IEEE 2021, ISBN 978-1-6654-2398-4 - Francesco Alesiani
, Shujian Yu, Xi Yu:
Gated Information Bottleneck for Generalization in Sequential Environments. 1-10 - Tianshu Bao, Xiaowei Jia, Jacob Zwart, Jeffrey M. Sadler, Alison P. Appling, Samantha Oliver, Taylor T. Johnson:
Partial Differential Equation Driven Dynamic Graph Networks for Predicting Stream Water Temperature. 11-20 - Lodewijk Brand, Lauren Zoe Baker, Carla Ellefsen, Jackson Sargent, Hua Wang:
A Linear Primal-Dual Multi-Instance SVM for Big Data Classifications. 21-30 - Wennan Chang, Pengdao Dang, Changlin Wan, Xiaoyu Lu, Yue Fang, Tong Zhao, Yong Zang, Bo Li, Chi Zhang, Sha Cao:
Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence. 31-40 - Huiping Chen, Changyu Dong, Liyue Fan, Grigorios Loukides, Solon P. Pissis, Leen Stougie:
Differentially Private String Sanitization for Frequency-Based Mining Tasks. 41-50 - Mingyue Cheng, Fajie Yuan, Qi Liu, Xin Xin, Enhong Chen:
Learning Transferable User Representations with Sequential Behaviors via Contrastive Pre-training. 51-60 - Eli Chien, Chao Pan, Puoya Tabaghi, Olgica Milenkovic:
Highly Scalable and Provably Accurate Classification in Poincaré Balls. 61-70 - Rob Churchill, Lisa Singh:
Topic-Noise Models: Modeling Topic and Noise Distributions in Social Media Post Collections. 71-80 - Noy Cohen-Shapira, Lior Rokach:
TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection. 81-90 - Cazamere Comrie, Jon M. Kleinberg:
Hypergraph Ego-networks and Their Temporal Evolution. 91-100 - Manqing Dong, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu:
MetaGB: A Gradient Boosting Framework for Efficient Task Adaptive Meta Learning. 101-110 - Xingcheng Fu, Jianxin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu:
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network. 111-120 - Yangcheng Gao, Zhao Zhang, Haijun Zhang, Mingbo Zhao, Yi Yang, Meng Wang:
Dictionary Pair-based Data-Free Fast Deep Neural Network Compression. 121-130 - Yuyang Gao, Tong Steven Sun, Rishab Bhatt, Dazhou Yu, Sungsoo Ray Hong, Liang Zhao:
GNES: Learning to Explain Graph Neural Networks. 131-140 - Andrey Gritsenko, Yuan Guo, Kimia Shayestehfard, Armin Moharrer, Jennifer G. Dy, Stratis Ioannidis:
Graph Transfer Learning. 141-150 - Bin Gu, Zhou Zhai, Xiang Li, Heng Huang:
Finding Age Path of Self-Paced Learning. 151-160 - Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff:
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions. 161-170 - Ido Hakimi, Rotem Zamir Aviv, Kfir Y. Levy, Assaf Schuster:
LAGA: Lagged AllReduce with Gradient Accumulation for Minimal Idle Time. 171-180 - Yishuo Zhang, Nayyar Abbas Zaidi, Jiahui Zhou, Gang Li:
GANBLR: A Tabular Data Generation Model. 181-190 - Yi He, Jiaxian Dong, Bo-Jian Hou, Yu Wang, Fei Wang:
Online Learning in Variable Feature Spaces with Mixed Data. 181-190 - Yilin Hou, Guangming Zhao, Chuanren Liu, Zhonglin Zu, Xiaoqiang Zhu:
Conversion Prediction with Delayed Feedback: A Multi-task Learning Approach. 191-199 - Chen Huang, Liangxu Pan, Qinli Yang, Hongliang Wang, Junming Shao:
Flexible, Robust, Scalable Semi-supervised Learning via Reliability Propagation. 200-209 - Jie Huang, Qi Liu, Fei Wang, Zhenya Huang, Songtao Fang, Runze Wu, Enhong Chen, Yu Su, Shijin Wang:
Group-Level Cognitive Diagnosis: A Multi-Task Learning Perspective. 210-219 - Ye Huang, Wei Huang, Shiwei Tong, Zhenya Huang, Qi Liu, Enhong Chen, Jianhui Ma, Liang Wan, Shijin Wang:
STAN: Adversarial Network for Cross-domain Question Difficulty Prediction. 220-229 - Jeehyun Hwang, Jeongwhan Choi, Hwangyong Choi, Kookjin Lee, Dongeun Lee, Noseong Park:
Climate Modeling with Neural Diffusion Equations. 230-239 - Hankyu Jang, Shreyas Pai, Bijaya Adhikari, Sriram V. Pemmaraju:
Risk-aware Temporal Cascade Reconstruction to Detect Asymptomatic Cases : For the CDC MInD Healthcare Network. 240-249 - Sheo Yon Jhin, Heejoo Shin, Seoyoung Hong, Minju Jo, Solhee Park, Noseong Park, Seungbeom Lee, Hwiyoung Maeng, Seungmin Jeon:
Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting. 250-259 - Renqi Jia, Xiaofei Zhou, Linhua Dong, Shirui Pan:
Hypergraph Convolutional Network for Group Recommendation. 260-269 - Xiaowei Jia, Yiqun Xie, Sheng Li, Shengyu Chen, Jacob Zwart, Jeffrey M. Sadler, Alison P. Appling, Samantha Oliver, Jordan S. Read:
Physics-Guided Machine Learning from Simulation Data: An Application in Modeling Lake and River Systems. 270-279 - Xiangping Kang, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Wei Guo, Yazhou Ren, Lizhen Cui:
Crowdsourcing with Self-paced Workers. 280-289 - Yun-Yong Ko, Jae-Seo Yu, Hong-Kyun Bae, Yongjun Park, Dongwon Lee, Sang-Wook Kim:
MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems. 290-299 - Yuandu Lai, Yahong Han, Yaowei Wang:
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings. 300-309 - Geon Lee, Kijung Shin:
THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting. 310-319 - Xiaoyu Li, Chen Li, Yuhua Wei, Yuyao Sun, Jishang Wei, Xiang Li, Buyue Qian:
BaT: Beat-aligned Transformer for Electrocardiogram Classification. 320-329 - Xujia Li, Yanyan Shen, Lei Chen:
Mcore: Multi-Agent Collaborative Learning for Knowledge-Graph-Enhanced Recommendation. 330-339 - Yang Li, Xianli Zhang, Buyue Qian, Zeyu Gao, Chong Guan, Yefeng Zheng, Hansen Zheng, Fenglang Wu, Chen Li:
Towards Interpretability and Personalization: A Predictive Framework for Clinical Time-series Analysis. 340-349 - Yunchuan Li, Yan Zhao, Kai Zheng:
Preference-aware Group Task Assignment in Spatial Crowdsourcing: A Mutual Information-based Approach. 350-359 - Xixun Lin, Jiangxia Cao, Peng Zhang, Chuan Zhou, Zhao Li, Jia Wu, Bin Wang:
Disentangled Deep Multivariate Hawkes Process for Learning Event Sequences. 360-369 - Yang Lin, Irena Koprinska, Mashud Rana:
SSDNet: State Space Decomposition Neural Network for Time Series Forecasting. 370-378 - Chen Ling, Carl Yang, Liang Zhao:
Deep Generation of Heterogeneous Networks. 379-388 - Huijie Liu, Han Wu, Le Zhang, Runlong Yu, Ye Liu, Chunli Liu, Qi Liu, Enhong Chen:
Technological Knowledge Flow Forecasting through A Hierarchical Interactive Graph Neural Network. 389-398 - Kunpeng Liu, Pengfei Wang, Dongjie Wang, Wan Du, Dapeng Oliver Wu, Yanjie Fu:
Efficient Reinforced Feature Selection via Early Stopping Traverse Strategy. 399-408 - Yifei Liu, Chao Chen, Yazheng Liu, Xi Zhang, Sihong Xie:
Multi-objective Explanations of GNN Predictions. 409-418 - Tomas Martin, Petko Valtchev, Louis-Romain Roux:
FGC-Stream: A novel joint miner for frequent generators and closed itemsets in data streams. 419-428 - Md. Parvez Mollah, Vinícius M. A. de Souza, Abdullah Mueen:
Multi-way Time Series Join on Multi-length Patterns. 429-438 - Mehrnaz Najafi, Lifang He, Philip S. Yu:
Outlier-Robust Multi-View Subspace Clustering with Prior Constraints. 439-448 - Shuai Niu, Qing Yin, Yunya Song, Yike Guo, Xian Yang:
Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records. 449-458 - Bastian Oetomo, R. Malinga Perera, Renata Borovica-Gajic, Benjamin I. P. Rubinstein:
Cutting to the Chase with Warm-Start Contextual Bandits. 459-468 - Hengzhi Pei, Kan Ren, Yuqing Yang, Chang Liu, Tao Qin, Dongsheng Li:
Towards Generating Real-World Time Series Data. 469-478 - Jingshu Peng, Yanyan Shen, Lei Chen:
GraphANGEL: Adaptive aNd Structure-Aware Sampling on Graph NEuraL Networks. 479-488 - Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang:
Sequential Diagnosis Prediction with Transformer and Ontological Representation. 489-498 - Thai-Hoang Pham, Changchang Yin, Laxmi Mehta, Xueru Zhang, Ping Zhang:
Cardiac Complication Risk Profiling for Cancer Survivors via Multi-View Multi-Task Learning. 499-508 - Gaël Poux-Médard, Julien Velcin, Sabine Loudcher:
Powered Hawkes-Dirichlet Process: Challenging Textual Clustering using a Flexible Temporal Prior. 509-518 - Ruihong Qiu, Zi Huang, Hongzhi Yin:
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation. 519-528 - Jiahuan Ren, Zhao Zhang, Jicong Fan, Haijun Zhang, Mingliang Xu, Meng Wang:
Robust Low-rank Deep Feature Recovery in CNNs: Toward Low Information Loss and Fast Convergence. 529-538 - Nasim Sabetpour, Adithya Kulkarni, Sihong Xie, Qi Li:
Truth Discovery in Sequence Labels from Crowds. 539-548 - Dhruv Sahnan, Snehil Dahiya, Vasu Goel, Anil Bandhakavi, Tanmoy Chakraborty:
Better Prevent than React: Deep Stratified Learning to Predict Hate Intensity of Twitter Reply Chains. 549-558 - Saurabh Sawlani, Lingxiao Zhao, Leman Akoglu:
Fast Attributed Graph Embedding via Density of States. 559-568 - Dev Yashpal Sheth, Arun Rajkumar:
PARWiS: Winner determination from Active Pairwise Comparisons under a Shoestring Budget. 569-578 - Tongtong Su, Qiyu Liang, Jinsong Zhang, Zhaoyang Yu, Gang Wang, Xiaoguang Liu:
Attention-based Feature Interaction for Efficient Online Knowledge Distillation. 579-588 - Chang Wei Tan, Matthieu Herrmann, Geoffrey I. Webb:
Ultra fast warping window optimization for Dynamic Time Warping. 589-598 - Qiao Tang, Hong Xie:
A Robust Algorithm to Unifying Offline Causal Inference and Online Multi-armed Bandit Learning. 599-608 - Nikolaj Tatti:
Fast computation of distance-generalized cores using sampling. 609-618 - Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang:
Isolation Kernel Density Estimation. 619-628 - Joshua Tobin, Mimi Zhang:
DCF: An Efficient and Robust Density-Based Clustering Method. 629-638 - Andrea Tonon, Fabio Vandin:
CASPITA: Mining Statistically Significant Paths in Time Series Data from an Unknown Network. 639-648 - Amin Vahedian, Xun Zhou:
Precise Bayes Classifier: Summary of Results. 649-658 - Arthur Vervaet, Raja Chiky, Mar Callau-Zori:
USTEP: Unfixed Search Tree for Efficient Log Parsing. 659-668 - Dingrong Wang, Hitesh Sapkota, Xumin Liu, Qi Yu:
Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval. 669-678 - Dongjie Wang, Kunpeng Liu, Pauline Johnson, Leilei Sun, Bowen Du, Yanjie Fu:
Deep Human-guided Conditional Variational Generative Modeling for Automated Urban Planning. 679-688 - Lu Wang, Yan Li, Mark H. Chignell:
Combining Ranking and Point-wise Losses for Training Deep Survival Analysis Models. 689-698 - Xuesong Wang, Lina Yao, Xianzhi Wang, Hye-Young Paik, Sen Wang:
Global Convolutional Neural Processes. 699-708 - Wenjuan Wei, Lu Feng:
Nonlinear Causal Structure Learning for Mixed Data. 709-718 - Yuhua Wei, Xiaoyu Li, Jishang Wei, Buyue Qian, Chen Li:
Learning to Reweight Samples with Offline Loss Sequence. 719-728 - Jing Wen, Bi-Yi Chen, Chang-Dong Wang, Zhihong Tian:
PRGAN: Personalized Recommendation with Conditional Generative Adversarial Networks. 729-738 - Asiri Wijesinghe, Qing Wang, Stephen Gould:
A Regularized Wasserstein Framework for Graph Kernels. 739-748 - Di Wu, Cheng Chen, Xiujun Chen, Junwei Pan, Xun Yang, Qing Tan, Jian Xu, Kuang-Chih Lee:
Impression Allocation and Policy Search in Display Advertising. 749-756 - Meng Xiao, Ziyue Qiao, Yanjie Fu, Yi Du, Pengyang Wang, Yuanchun Zhou:
Expert Knowledge-Guided Length-Variant Hierarchical Label Generation for Proposal Classification. 757-766 - Yiqun Xie, Erhu He, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh, Praveen Ravirathinam:
A Statistically-Guided Deep Network Transformation and Moderation Framework for Data with Spatial Heterogeneity. 767-776 - Xiao Xu, Xian Xu, Yuyao Sun, Xiaoshuang Liu, Xiang Li, Guotong Xie, Fei Wang:
Predictive Modeling of Clinical Events with Mutual Enhancement Between Longitudinal Patient Records and Medical Knowledge Graph. 777-786 - Haoran Yang, Hongxu Chen, Lin Li, Philip S. Yu, Guandong Xu:
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation. 787-796 - Peng Yang, Xiaoyun Li, Ping Li:
Graph-based Adversarial Online Kernel Learning with Adaptive Embedding. 797-806 - Shuo Yang, Zeyu Feng, Pei Du, Bo Du, Chang Xu:
Structure-Aware Stabilization of Adversarial Robustness with Massive Contrastive Adversaries. 807-816 - Song Yang, Jiamou Liu, Kaiqi Zhao:
Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting. 817-826 - Jaemin Yoo, Junghun Kim, Hoyoung Yoon, Geonsoo Kim, Changwon Jang, U Kang:
Accurate Graph-Based PU Learning without Class Prior. 827-836 - Zhizhi Yu, Di Jin, Ziyang Liu, Dongxiao He, Xiao Wang, Hanghang Tong, Jiawei Han:
AS-GCN: Adaptive Semantic Architecture of Graph Convolutional Networks for Text-Rich Networks. 837-846 - Jingyi Yuan, Yang Weng:
Physics Interpretable Shallow-Deep Neural Networks for Physical System Identification with Unobservability. 847-856 - Chengxi Zang, Fei Wang:
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records. 857-866 - Ge Zhang, Jia Wu, Jian Yang, Amin Beheshti, Shan Xue, Chuan Zhou, Quan Z. Sheng:
FRAUDRE: Fraud Detection Dual-Resistant to Graph Inconsistency and Imbalance. 867-876 - Jiuling Zhang, Zhiming Ding:
Robustifying DARTS by Eliminating Information Bypass Leakage via Explicit Sparse Regularization. 877-885 - Wenbin Zhang, Jeremy C. Weiss:
Fair Decision-making Under Uncertainty. 886-895 - Xiangyu Zhao, Haochen Liu, Wenqi Fan, Hui Liu, Jiliang Tang, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, Xiwang Yang:
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations. 896-905 - Xin Zhang, Yanhua Li, Xun Zhou, Oren Mangoubi, Ziming Zhang, Vincent Filardi, Jun Luo:
DAC-ML: Domain Adaptable Continuous Meta-Learning for Urban Dynamics Prediction. 906-915 - Yunfeng Zhao, Guoxian Yu, Lei Liu, Zhongmin Yan, Carlotta Domeniconi, Lizhen Cui:
Few-Shot Partial Multi-Label Learning. 926-935 - Zhao Zhang, Weiming Jiang, Yang Wang, Qiaolin Ye, Mingbo Zhao, Mingliang Xu, Meng Wang:
Discriminative Additive Scale Loss for Deep Imbalanced Classification and Embedding. 936-945 - Zhao Zhang, Xianzhen Li, Haijun Zhang, Yi Yang, Shuicheng Yan, Meng Wang:
Triplet Deep Subspace Clustering via Self-Supervised Data Augmentation. 946-955 - Zicong Zhang, Changchang Yin, Ping Zhang:
Temporal Clustering with External Memory Network for Disease Progression Modeling. 956-965 - Ziming Zhang, Guojun Wu, Yanhua Li, Yun Yue, Xun Zhou:
Deep Incremental RNN for Learning Sequential Data: A Lyapunov Stable Dynamical System. 966-975 - Futoon M. Abushaqra, Hao Xue, Yongli Ren, Flora D. Salim:
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series. 976-981 - Muhammad Afif Ali, Suriya Venkatesan, Victor C. Liang, Hannes Kruppa:
TEST-GCN: Topologically Enhanced Spatial-Temporal Graph Convolutional Networks for Traffic Forecasting. 982-987 - Jonathan Amazon, Khurram Shafique, Zeeshan Rasheed, Aaron Reite:
DIVINIA: Rare Object Localization and Search in Overhead Imagery. 988-993 - Oren Barkan, Roy Hirsch, Ori Katz, Avi Caciularu, Jonathan Weill, Noam Koenigstein:
Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates. 994-999 - Fabian Berns, Jan David Hüwel, Christian Beecks:
LOGIC: Probabilistic Machine Learning for Time Series Classification. 1000-1005 - Arkaitz Bidaurrazaga, Aritz Pérez, Marco Capó:
K-means for Evolving Data Streams. 1006-1011 - Zerui Cai:
Generating Explanations for Recommendation Systems via Injective VAE. 1012-1017 - Chao Chen, Yifan Shen, Guixiang Ma, Xiangnan Kong, Srinivas Rangarajan, Xi Zhang, Sihong Xie:
Self-learn to Explain Siamese Networks Robustly. 1018-1023 - Shengyu Chen, Alison P. Appling, Samantha Oliver, Hayley Corson-Dosch, Jordan S. Read, Jeffrey M. Sadler, Jacob Zwart, Xiaowei Jia:
Heterogeneous Stream-reservoir Graph Networks with Data Assimilation. 1024-1029 - Yi-He Chen, Shen-Huan Lyu, Yuan Jiang:
Improving Deep Forest by Exploiting High-order Interactions. 1030-1035 - André Ferreira Cruz, Pedro Saleiro, Catarina G. Belém, Carlos Soares, Pedro Bizarro:
Promoting Fairness through Hyperparameter Optimization. 1036-1041 - Shaojie Dai, Jinshuai Wang, Chao Huang, Yanwei Yu, Junyu Dong:
Temporal Multi-view Graph Convolutional Networks for Citywide Traffic Volume Inference. 1042-1047 - Benjamin Denham, Edmund M.-K. Lai, Roopak Sinha, M. Asif Naeem:
Gain-Some-Lose-Some: Reliable Quantification Under General Dataset Shift. 1048-1053 - Wei Fan, Kunpeng Liu, Rui Xie, Hao Liu, Hui Xiong, Yanjie Fu:
Fair Graph Auto-Encoder for Unbiased Graph Representations with Wasserstein Distance. 1054-1059 - Yucai Fan, Yuhang Yao, Carlee Joe-Wong:
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs. 1060-1065 - Yang Gao, Peng Zhang, Zhao Li, Chuan Zhou, Yongchao Liu, Yue Hu:
Heterogeneous Graph Neural Architecture Search.