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ORCIDSDM 2020: Cincinnati, Ohio, USA
- Carlotta Demeniconi, Nitesh V. Chawla:
Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020, Cincinnati, Ohio, USA, May 7-9, 2020. SIAM 2020, ISBN 978-1-61197-623-6The conference was canceled because of the coronavirus pandemic, the reviewed papers are published in this volume. - Akihiro Yamaguchi
, Shigeru Maya, Kohei Maruchi, Ken Ueno:
LTSpAUC: Learning Time-series Shapelets for Optimizing Partial AUC. 1-9 - Erik Scharwächter, Emmanuel Müller:
Two-Sample Testing for Event Impacts in Time Series. 10-18 - Yang Li, Buyue Qian, Xianli Zhang, Hui Liu:
Knowledge guided diagnosis prediction via graph spatial-temporal network. 19-27 - Tyler Wilson, Pang-Ning Tan, Lifeng Luo:
Convolutional Methods for Predictive Modeling of Geospatial Data. 28-36 - Suhas Thejaswi, Aristides Gionis:
Pattern detection in large temporal graphs using algebraic fingerprints. 37-45 - Suwen Lin, Xian Wu, Gonzalo J. Martínez, Nitesh V. Chawla:
Filling Missing Values on Wearable-Sensory Time Series Data. 46-54 - Julia Lasserre, Abdul-Saboor Sheikh, Evgenii Koriagin, Urs Bergmann, Roland Vollgraf, Reza Shirvany:
Meta-Learning for Size and Fit Recommendation in Fashion. 55-63 - Zhiwei Liu, Mengting Wan, Stephen D. Guo, Kannan Achan, Philip S. Yu:
BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network. 64-72 - Hengrui Zhang, Julian J. McAuley:
Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering. 73-81 - Rui Ding, Guibing Guo, Xiaochun Yang, Bowei Chen, Zhirong Liu, Xiuqiang He:
BiGAN: Collaborative Filtering with Bidirectional Generative Adversarial Networks. 82-90 - Xunqiang Jiang, Binbin Hu, Yuan Fang, Chuan Shi:
Multiplex Memory Network for Collaborative Filtering. 91-99 - Buru Chang, Yookyung Koh, Donghyeon Park, Jaewoo Kang:
Content-Aware Successive Point-of-Interest Recommendation. 100-108 - Zahra Ghafoori, Christopher Leckie:
Deep Multi-sphere Support Vector Data Description. 109-117 - Adrian Englhardt, Holger Trittenbach, Dennis Vetter, Klemens Böhm:
Finding the Sweet Spot: Batch Selection for One-Class Active Learning. 118-126 - Vincent Vercruyssen, Wannes Meert, Jesse Davis:
"Now you see it, now you don't!" Detecting Suspicious Pattern Absences in Continuous Time Series. 127-135 - Li Zhang, Yifeng Gao, Jessica Lin:
Semantic Discord: Finding Unusual Local Patterns for Time Series. 136-144 - Adrian Englhardt, Klemens Böhm:
Exploring the Unknown - Query Synthesis in One-Class Active Learning. 145-153 - Mehadi Hassen, Philip K. Chan:
Learning a Neural-network-based Representation for Open Set Recognition. 154-162 - Chi-Cheng Chiu, Pin-Yen Lin, Chih-Jen Lin:
Two-variable Dual Coordinate Descent Methods for Linear SVM with/without the Bias Term. 163-171 - Naoto Ohsaka, Tomoya Sakai, Akihiro Yabe:
A Predictive Optimization Framework for Hierarchical Demand Matching. 172-180 - Hung-Yi Chou, Pin-Yen Lin, Chih-Jen Lin:
Dual Coordinate-Descent Methods for Linear One-Class SVM and SVDD. 181-189 - Xinyan Li, Qilong Gu, Yingxue Zhou, Tiancong Chen, Arindam Banerjee:
Hessian based analysis of SGD for Deep Nets: Dynamics and Generalization. 190-198 - Peng Xu, Fred Roosta, Michael W. Mahoney:
Second-order Optimization for Non-convex Machine Learning: an Empirical Study. 199-207 - Hongyuan You, Furkan Kocayusufoglu, Ambuj K. Singh:
DANR: Discrepancy-aware Network Regularization. 208-216 - Guiliang Liu, Xu Li, Mingming Sun, Ping Li:
An Advantage Actor-Critic Algorithm with Confidence Exploration for Open Information Extraction. 217-225 - Rong Zhang, Qifei Zhou, Bo Wu, Weiping Li, Tong Mo:
What Do Questions Exactly Ask? MFAE: Duplicate Question Identification with Multi-Fusion Asking Emphasis. 226-234 - Bo Yang, Kejun Huang, Nicholas D. Sidiropoulos:
Identifying Potential Investors with Data Driven Approaches. 235-243 - Gianni Costa, Riccardo Ortale:
Document Clustering Meets Topic Modeling with Word Embeddings. 244-252 - Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Varun Mithal, Vipin Kumar:
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data. 253-261 - Tomoki Ito, Kota Tsubouchi, Hiroki Sakaji, Tatsuo Yamashita, Kiyoshi Izumi:
SSNN: Sentiment Shift Neural Network. 262-270 - Ruocheng Guo, Jundong Li, Huan Liu:
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data. 271-279 - Hongjing Zhang, S. S. Ravi, Ian Davidson:
A Graph-Based Approach for Active Learning in Regression. 280-288 - Charu C. Aggarwal, Yao Li, Philip S. Yu:
On Supervised Change Detection in Graph Streams. 289-297 - Aritra Ghosh, Saayan Mitra, Somdeb Sarkhel, Viswanathan Swaminathan:
Optimal Bidding Strategy without Exploration in Real-time Bidding. 298-306 - Wentao Wang, Suhang Wang, Wenqi Fan, Zitao Liu, Jiliang Tang:
Global-and-Local Aware Data Generation for the Class Imbalance Problem. 307-315 - Jay S. Stanley III, Scott Gigante, Guy Wolf, Smita Krishnaswamy:
Harmonic Alignment. 316-324 - Ricky Laishram, Ahmet Erdem Sariyüce, Tina Eliassi-Rad, Ali Pinar, Sucheta Soundarajan:
Residual Core Maximization: An Efficient Algorithm for Maximizing the Size of the k-Core. 325-333 - Yi He, Sheng Chen, Thu Nguyen, Bruce A. Wade, Xindong Wu:
Deep Matrix Tri-Factorization: Mining Vertex-wise Interactions in Multi-Space Attributed Graphs. 334-342 - Christian Bauckhage, Rafet Sifa, Stefan Wrobel:
Adiabatic Quantum Computing for Max-Sum Diversification. 343-351 - Wenqi Fan, Yao Ma, Han Xu, Xiaorui Liu, Jianping Wang, Qing Li, Jiliang Tang:
Deep Adversarial Canonical Correlation Analysis. 352-360 - Yan Zhang, Zhao Zhang, Zheng Zhang, Mingbo Zhao, Li Zhang, Zhengjun Zha, Meng Wang:
Deep Self-representative Concept Factorization Network for Representation Learning. 361-369 - Fan Yang, Ninghao Liu, Mengnan Du, Kaixiong Zhou, Shuiwang Ji, Xia Hu:
Deep Neural Networks with Knowledge Instillation. 370-378 - Xin Dai, Xiangnan Kong, Xinyue Liu, John Boaz Lee, Constance M. Moore:
Dual-Attention Recurrent Networks for Affine Registration of Neuroimaging Data. 379-387 - Siwu Liu, Ji Hwan Park, Shinjae Yoo:
Efficient and Effective Graph Convolution Networks. 388-396 - Rafael Rêgo Drumond, Lukas Brinkmeyer, Josif Grabocka, Lars Schmidt-Thieme:
HIDRA: Head Initialization across Dynamic targets for Robust Architectures. 397-405 - Yuta Saito, Hayato Sakata, Kazuhide Nakata:
Cost-Effective and Stable Policy Optimization Algorithm for Uplift Modeling with Multiple Treatments. 406-414 - Ulf Johansson, Tuwe Löfström:
Well-calibrated and specialized probability estimation trees. 415-423 - James R. Foulds, Rashidul Islam, Kamrun Naher Keya, Shimei Pan:
Bayesian Modeling of Intersectional Fairness: The Variance of Bias. 424-432 - Fattaneh Jabbari, Gregory F. Cooper:
An Instance-Specific Algorithm for Learning the Structure of Causal Bayesian Networks Containing Latent Variables. 433-441 - Sara Alaee, Alireza Abdoli, Christian R. Shelton, Amy C. Murillo, Alec C. Gerry, Eamonn J. Keogh:
Features or Shape? Tackling the False Dichotomy of Time Series Classification. 442-450 - Jingzheng Tu, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang:
Attention-Aware Answers of the Crowd. 451-459 - Timothy LaRock, Vahan Nanumyan, Ingo Scholtes, Giona Casiraghi, Tina Eliassi-Rad, Frank Schweitzer:
HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks. 460-468 - Yinghua Zhang, Yu Zhang, Ying Wei, Kun Bai, Yangqiu Song, Qiang Yang:
Fisher Deep Domain Adaptation. 469-477 - Lu Cheng, Ruocheng Guo, K. Selçuk Candan, Huan Liu:
Representation Learning for Imbalanced Cross-Domain Classification. 478-486 - Anasua Mitra, Priyesh Vijayan, Srinivasan Parthasarathy, Balaraman Ravindran:
A Unified Non-Negative Matrix Factorization Framework for Semi Supervised Learning on Graphs. 487-495 - Lutz Oettershagen, Nils M. Kriege, Christopher Morris, Petra Mutzel:
Temporal Graph Kernels for Classifying Dissemination Processes. 496-504 - Charles H. Martin, Michael W. Mahoney:
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks. 505-513 - Fenglong Ma, Yaqing Wang, Jing Gao, Houping Xiao, Jing Zhou:
Rare Disease Prediction by Generating Quality-Assured Electronic Health Records. 514-522 - Yitao Li, Umar Islambekov, Cuneyt Gurcan Akcora, Ekaterina Smirnova, Yulia R. Gel, Murat Kantarcioglu:
Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of the Ethereum Graph? 523-531 - Arka Daw, R. Quinn Thomas, Cayelan C. Carey, Jordan S. Read, Alison P. Appling, Anuj Karpatne:
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling. 532-540 - Scott Freitas, Andrew Wicker, Duen Horng (Polo) Chau, Joshua Neil:
D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks. 541-549 - Rongrong Tao, Baojian Zhou, Feng Chen, David Mares, Patrick Butler, Naren Ramakrishnan, Ryan Kennedy:
Detecting Media Self-Censorship without Explicit Training Data. 550-558 - Nikhil Muralidhar, Jie Bu, Ze Cao, Long He, Naren Ramakrishnan, Danesh K. Tafti, Anuj Karpatne:
PhyNet: Physics Guided Neural Networks for Particle Drag Force Prediction in Assembly. 559-567 - Ryuta Matsuno, Aristides Gionis:
Improved mixing time for k-subgraph sampling. 568-576 - Ekta Gujral, Georgios Theocharous, Evangelos E. Papalexakis:
SPADE: Streaming PARAFAC2 DEcomposition for Large Datasets. 577-585 - Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie:
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach. 586-594 - Wenyi Xiao, Huan Zhao, Vincent W. Zheng, Yangqiu Song:
Vertex-reinforced Random Walk for Network Embedding. 595-603 - Changgee Chang, Jihwan Oh, Qi Long:
GRIA: Graphical Regularization for Integrative Analysis. 604-612 - Zeinab S. Jalali, Weixiang Wang, Myunghwan Kim, Hema Raghavan, Sucheta Soundarajan:
On the Information Unfairness of Social Networks. 613-521 - Shigeru Maya, Akihiro Yamaguchi, Kaneharu Nishino, Ken Ueno:
Lag-Aware Multivariate Time-Series Segmentation. 622-630 - John Boaz Lee, Xiangnan Kong, Constance M. Moore, Nesreen K. Ahmed:
Deep Parametric Model for Discovering Group-cohesive Functional Brain Regions. 631-639 - Chieh Wu, Zulqarnain Khan, Stratis Ioannidis, Jennifer G. Dy:
Deep Kernel Learning for Clustering. 640-648 - Guangyi Zhang, Aristides Gionis:
Maximizing diversity over clustered data. 649-657 - Xiaoqiang Yan, Yiqiao Mao, Shizhe Hu, Yangdong Ye:
Heterogeneous Dual-Task Clustering with Visual-Textual Information. 658-666 - Yorgos Tsitsikas, Evangelos E. Papalexakis:
NSVD: Normalized Singular Value Deviation Reveals Number of Latent Factors in Tensor Decomposition. 667-675
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