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ORCIDNitesh V. Chawla
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- affiliation: University of Notre Dame, USA
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2020 – today
- 2023
[j93]Yihong Ma
, Md Nafee Al Islam, Jane Cleland-Huang, Nitesh V. Chawla:
Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows. IEEE Intell. Syst. 38(2): 46-54 (2023)- [j92]Jennifer J. Schnur, Nitesh V. Chawla:
Information fusion via symbolic regression: A tutorial in the context of human health. Inf. Fusion 92: 326-335 (2023) - [j91]Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla:
Introduction to the Special Track on Artificial Intelligence and COVID-19. J. Artif. Intell. Res. 76: 523-525 (2023) - [j90]Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang:
Modeling Co-Evolution of Attributed and Structural Information in Graph Sequence. IEEE Trans. Knowl. Data Eng. 35(2): 1817-1830 (2023) - [i72]Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh V. Chawla:
Knowledge Distillation on Graphs: A Survey. CoRR abs/2302.00219 (2023) - [i71]Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla:
Class-Imbalanced Learning on Graphs: A Survey. CoRR abs/2304.04300 (2023) - [i70]Damien A. Dablain, Nitesh V. Chawla:
Towards Understanding How Data Augmentation Works with Imbalanced Data. CoRR abs/2304.05895 (2023) - 2022
- [j89]Jermaine Marshall, Priscilla Jiménez-Pazmino, Ronald A. Metoyer, Nitesh V. Chawla:
A Survey on Healthy Food Decision Influences Through Technological Innovations. ACM Trans. Comput. Heal. 3(2): 25:1-25:27 (2022) - [j88]Mary Jean Amon, Stephen M. Mattingly, Aaron Necaise, Gloria Mark, Nitesh V. Chawla, Anind K. Dey, Sidney D'Mello:
Flexibility Versus Routineness in Multimodal Health Indicators: A Sensor-based Longitudinal in Situ Study of Information Workers. ACM Trans. Comput. Heal. 3(3): 36:1-36:27 (2022) - [j87]Mandana Saebi, Steven Kreig, Chuxu Zhang, Meng Jiang, Tomasz Kajdanowicz, Nitesh V. Chawla:
Heterogeneous relational reasoning in knowledge graphs with reinforcement learning. Inf. Fusion 88: 12-21 (2022) - [j86]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
AttrE2vec: Unsupervised attributed edge representation learning. Inf. Sci. 592: 82-96 (2022) - [j85]Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz, Nitesh V. Chawla:
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings. Knowl. Based Syst. 236: 107453 (2022) - [j84]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
Graph Barlow Twins: A self-supervised representation learning framework for graphs. Knowl. Based Syst. 256: 109631 (2022) - [j83]Steven J. Krieg, Carolina Avendano, Evan Grantham-Brown, Aaron Lilienfeld Asbun, Jennifer J. Schnur, Marie Lynn Miranda, Nitesh V. Chawla:
Data-driven testing program improves detection of COVID-19 cases and reduces community transmission. npj Digit. Medicine 5 (2022) - [j82]Beenish Moalla Chaudhry, Dipanwita Dasgupta, Nitesh V. Chawla:
Formative Evaluation of a Tablet Application to Support Goal-Oriented Care in Community-Dwelling Older Adults. Proc. ACM Hum. Comput. Interact. 6(MHCI): 1-21 (2022) - [j81]Xian Wu, Chao Huang, Pablo Robles-Granda, Nitesh V. Chawla:
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series. ACM Trans. Intell. Syst. Technol. 13(6): 97:1-97:21 (2022) - [c197]Yihong Ma, Patrick Gérard, Yijun Tian, Zhichun Guo, Nitesh V. Chawla:
Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting. CIKM 2022: 1481-1490 - [c196]Yiyue Qian, Yiming Zhang, Nitesh V. Chawla, Yanfang Ye, Chuxu Zhang:
Malicious Repositories Detection with Adversarial Heterogeneous Graph Contrastive Learning. CIKM 2022: 1645-1654 - [c195]Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang:
Compositional Training for End-to-End Deep AUC Maximization. ICLR 2022 - [c194]Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald A. Metoyer, Nitesh V. Chawla:
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation. IJCAI 2022: 3466-3472 - [c193]Yijun Tian, Chuxu Zhang, Zhichun Guo, Yihong Ma, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks. IJCAI 2022: 3473-3479 - [c192]Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu:
Few-Shot Learning on Graphs. IJCAI 2022: 5662-5669 - [c191]Kaize Ding, Chuxu Zhang, Jie Tang, Nitesh V. Chawla, Huan Liu:
Toward Graph Minimally-Supervised Learning. KDD 2022: 4782-4783 - [c190]Senthil Kumar, Leman Akoglu, Nitesh V. Chawla, Saurabh Nagrecha, Vidyut M. Naware, Tanveer A. Faruquie, Hays McCormick:
KDD Workshop on Machine Learning in Finance. KDD 2022: 4882-4883 - [c189]Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla:
FakeEdge: Alleviate Dataset Shift in Link Prediction. LoG 2022: 56 - [c188]Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno, Nitesh V. Chawla, Jane Cleland-Huang:
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers. RE 2022: 153-165 - [c187]Kaize Ding, Jundong Li, Nitesh V. Chawla, Huan Liu:
Graph Minimally-supervised Learning. WSDM 2022: 1620-1622 - [e10]Peipei Li, Kui Yu, Nitesh V. Chawla, Ronen Feldman, Qing Li, Xindong Wu:
IEEE International Conference on Knowledge Graph, ICKG 2022, Orlando, FL, USA, November 30 - Dec. 1, 2022. IEEE 2022, ISBN 978-1-6654-5101-7 [contents] - [i69]Steven J. Krieg, Christian W. Smith, Rusha Chatterjee, Nitesh V. Chawla:
Predicting Terrorist Attacks in the United States using Localized News Data. CoRR abs/2201.04292 (2022) - [i68]Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu:
Few-Shot Learning on Graphs: A Survey. CoRR abs/2203.09308 (2022) - [i67]Zhichun Guo, Jun Tao, Siming Chen, Nitesh V. Chawla, Chaoli Wang:
SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance. CoRR abs/2203.12671 (2022) - [i66]Yijun Tian, Chuxu Zhang, Zhichun Guo, Yihong Ma, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks. CoRR abs/2205.12396 (2022) - [i65]Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla:
Deep Ensembles for Graphs with Higher-order Dependencies. CoRR abs/2205.13988 (2022) - [i64]Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald A. Metoyer, Nitesh V. Chawla:
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation. CoRR abs/2205.14005 (2022) - [i63]Zhichun Guo, Bozhao Nan, Yijun Tian, Olaf Wiest, Chuxu Zhang, Nitesh V. Chawla:
Graph-based Molecular Representation Learning. CoRR abs/2207.04869 (2022) - [i62]Damien Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla:
Efficient Augmentation for Imbalanced Deep Learning. CoRR abs/2207.06080 (2022) - [i61]Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla:
Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning. CoRR abs/2207.06084 (2022) - [i60]Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno, Nitesh V. Chawla, Jane Cleland-Huang:
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers. CoRR abs/2207.08857 (2022) - [i59]Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla:
Heterogeneous Graph Masked Autoencoders. CoRR abs/2208.09957 (2022) - [i58]Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla:
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs. CoRR abs/2208.10010 (2022) - [i57]Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao:
Linkless Link Prediction via Relational Distillation. CoRR abs/2210.05801 (2022) - [i56]Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh V. Chawla:
Boosting Graph Neural Networks via Adaptive Knowledge Distillation. CoRR abs/2210.05920 (2022) - [i55]Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla:
Understanding CNN Fragility When Learning With Imbalanced Data. CoRR abs/2210.09465 (2022) - [i54]Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla:
FakeEdge: Alleviate Dataset Shift in Link Prediction. CoRR abs/2211.15899 (2022) - [i53]Tânia Carvalho, Nuno Moniz, Pedro Faria, Luís Antunes, Nitesh V. Chawla:
Privacy-Preserving Data Synthetisation for Secure Information Sharing. CoRR abs/2212.00484 (2022) - [i52]Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla:
Interpretable ML for Imbalanced Data. CoRR abs/2212.07743 (2022) - 2021
- [j80]Pablo Robles-Granda, Suwen Lin, Xian Wu, Gonzalo J. Martínez, Stephen M. Mattingly, Edward Moskal, Aaron Striegel, Nitesh V. Chawla, Sidney D'Mello, Julie M. Gregg, Kari Nies, Gloria Mark, Ted Grover, Andrew T. Campbell, Shayan Mirjafari, Koustuv Saha, Munmun De Choudhury, Anind K. Dey:
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being. IEEE Comput. Intell. Mag. 16(2): 46-61 (2021) - [j79]Steven J. Krieg, Jennifer J. Schnur, Jermaine D. Marshall, Matthew M. Schoenbauer, Nitesh V. Chawla:
Pandemic Pulse: Unraveling and Modeling Social Signals During the COVID-19 Pandemic. Digit. Gov. Res. Pract. 2(2): 19:1-19:9 (2021) - [j78]Munira Syed, Daheng Wang, Meng Jiang, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Unified Representation of Twitter and Online News Using Graph and Entities. Frontiers Big Data 4: 699070 (2021) - [j77]Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe Recommendation With Hierarchical Graph Attention Network. Frontiers Big Data 4: 778417 (2021) - [j76]Louis Faust, Keith Feldman, Suwen Lin, Stephen M. Mattingly, Sidney D'Mello, Nitesh V. Chawla:
Examining Response to Negative Life Events Through Fitness Tracker Data. Frontiers Digit. Health 3: 659088 (2021) - [j75]Shayan Mirjafari, Hessam Bagherinezhad, Subigya Nepal, Gonzalo J. Martínez, Koustuv Saha, Mikio Obuchi, Pino G. Audia, Nitesh V. Chawla, Anind K. Dey, Aaron Striegel, Andrew T. Campbell:
Predicting Job Performance Using Mobile Sensing. IEEE Pervasive Comput. 20(4): 43-51 (2021) - [j74]Tianwen Jiang, Qingkai Zeng, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Biomedical Knowledge Graphs Construction From Conditional Statements. IEEE ACM Trans. Comput. Biol. Bioinform. 18(3): 823-835 (2021) - [j73]Daheng Wang, Qingkai Zeng, Nitesh V. Chawla, Meng Jiang:
Modeling Complementarity in Behavior Data with Multi-Type Itemset Embedding. ACM Trans. Intell. Syst. Technol. 12(4): 42:1-42:25 (2021) - [j72]Chuxu Zhang, Huaxiu Yao, Lu Yu, Chao Huang, Dongjin Song, Haifeng Chen, Meng Jiang, Nitesh V. Chawla:
Inductive Contextual Relation Learning for Personalization. ACM Trans. Inf. Syst. 39(3): 35:1-35:22 (2021) - [c186]Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe Representation Learning with Networks. CIKM 2021: 1824-1833 - [c185]Suwen Lin, Xian Wu, Nitesh V. Chawla:
motif2vec: Semantic-aware Representation Learning for Wearables' Time Series Data. DSAA 2021: 1-10 - [c184]Beenish M. Chaudhry, Dipanwita Dasgupta, Mona A. Mohamed, Nitesh V. Chawla:
Teaching Tablet Technology to Older Adults. HCI (42) 2021: 168-182 - [c183]Dipanwita Dasgupta, Beenish M. Chaudhry, Nitesh V. Chawla:
A Qualitative Usability Evaluation of Tablets and Accessibility Settings by Older Adults. HCI (42) 2021: 183-204 - [c182]Daheng Wang, Tong Zhao, Nitesh V. Chawla, Meng Jiang:
Dynamic Attributed Graph Prediction with Conditional Normalizing Flows. ICDM 2021: 1385-1390 - [c181]Senthil Kumar, Leman Akoglu, Nitesh V. Chawla, José A. Rodríguez-Serrano, Tanveer A. Faruquie, Saurabh Nagrecha:
Machine Learning in Finance. KDD 2021: 4139-4140 - [c180]Suwen Lin, Louis Faust, Nitesh V. Chawla:
Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels. PerCom 2021: 1-10 - [c179]Kaiwen Dong, Kai Lu, Xin Xia, David A. Cieslak, Nitesh V. Chawla:
An Optimized NL2SQL System for Enterprise Data Mart. ECML/PKDD (5) 2021: 335-350 - [c178]Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla:
Few-Shot Graph Learning for Molecular Property Prediction. WWW 2021: 2559-2567 - [i51]Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla:
Few-Shot Graph Learning for Molecular Property Prediction. CoRR abs/2102.07916 (2021) - [i50]Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla:
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. CoRR abs/2105.02340 (2021) - [i49]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
Graph Barlow Twins: A self-supervised representation learning framework for graphs. CoRR abs/2106.02466 (2021) - 2020
- [j71]Suman Kundu, Tomasz Kajdanowicz, Przemyslaw Kazienko, Nitesh V. Chawla:
Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network. IEEE Access 8: 186653-186662 (2020) - [j70]Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla:
HONEM: Learning Embedding for Higher Order Networks. Big Data 8(4): 255-269 (2020) - [j69]Mandana Saebi, Jian Xu, Lance M. Kaplan, Bruno Ribeiro, Nitesh V. Chawla:
Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection. EPJ Data Sci. 9(1): 15 (2020) - [j68]Louis Faust, Keith Feldman, Stephen M. Mattingly, David Hachen, Nitesh V. Chawla:
Deviations from normal bedtimes are associated with short-term increases in resting heart rate. npj Digit. Medicine 3 (2020) - [j67]Chao Huang, Dong Wang, Nitesh V. Chawla:
Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems. IEEE Trans. Big Data 6(4): 702-713 (2020) - [c177]Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla:
Few-Shot Knowledge Graph Completion. AAAI 2020: 3041-3048 - [c176]Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li:
Graph Few-Shot Learning via Knowledge Transfer. AAAI 2020: 6656-6663 - [c175]Pingjie Tang, Meng Jiang, Bryan (Ning) Xia, Jed W. Pitera, Jeffrey Welser, Nitesh V. Chawla:
Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network. AAAI 2020: 9024-9031 - [c174]Munira Syed, Daheng Wang, Meng Jiang, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Overcoming Data Sparsity in Predicting User Characteristics from Behavior through Graph Embeddings. ASONAM 2020: 32-36 - [c173]Jennifer J. Schnur, Ryan Karl, Angélica García-Martínez, Meng Jiang, Nitesh V. Chawla:
Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach. BIBM 2020: 729-734 - [c172]Suwen Lin, Louis Faust, Sidney D'Mello, Gonzalo J. Martínez, Nitesh V. Chawla:
MBead: Semi-supervised Multilabel Behaviour Anomaly Detection on Multivariate Temporal Sensory Data. IEEE BigData 2020: 1089-1096 - [c171]Zhichun Guo, Wenhao Yu, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla:
GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. CIKM 2020: 435-443 - [c170]Xian Wu, Stephen M. Mattingly, Shayan Mirjafari, Chao Huang, Nitesh V. Chawla:
Personalized Imputation on Wearable-Sensory Time Series via Knowledge Transfer. CIKM 2020: 1625-1634 - [c169]Steven J. Krieg, Peter M. Kogge, Nitesh V. Chawla:
GrowHON: A Scalable Algorithm for Growing Higher-order Networks of Sequences. COMPLEX NETWORKS (2) 2020: 485-496 - [c168]Chuxu Zhang, Lu Yu, Mandana Saebi, Meng Jiang, Nitesh V. Chawla:
Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases. EMNLP (Findings) 2020: 580-585 - [c167]Priscilla Jiménez-Pazmino, Trenton Ford, Ronald A. Metoyer, Nitesh V. Chawla:
Identifying Bridge Users: the Knowledge Transfer Agents in Enterprise Collaboration Systems. HICSS 2020: 1-10 - [c166]Steven J. Krieg, Daniel H. Robertson, Meeta P. Pradhan, Nitesh V. Chawla:
Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter. ICHI 2020: 1-11 - [c165]Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors. KDD 2020: 2581-2589 - [c164]Tina Eliassi-Rad, Nitesh V. Chawla, Vittoria Colizza, Lauren Gardner, Marcel Salathé, Samuel V. Scarpino, Joseph T. Wu:
Fighting a Pandemic: Convergence of Expertise, Data Science and Policy. KDD 2020: 3493-3494 - [c163]Chuxu Zhang, Meng Jiang, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla:
Multi-modal Network Representation Learning. KDD 2020: 3557-3558 - [c162]Suwen Lin, Xian Wu, Gonzalo J. Martínez, Nitesh V. Chawla:
Filling Missing Values on Wearable-Sensory Time Series Data. SDM 2020: 46-54 - [c161]Poorna Talkad Sukumar, Gonzalo J. Martínez, Ted Grover, Gloria Mark, Sidney K. D'Mello, Nitesh V. Chawla, Stephen M. Mattingly, Aaron D. Striegel:
Characterizing Exploratory Behaviors on a Personal Visualization Interface Using Interaction Logs. EuroVis (Short Papers) 2020: 79-83 - [c160]Xian Wu, Suleyman Cetintas, Deguang Kong, Miao Lu, Jian Yang, Nitesh V. Chawla:
Learning from Cross-Modal Behavior Dynamics with Graph-Regularized Neural Contextual Bandit. WWW 2020: 995-1005 - [c159]Xian Wu, Chao Huang, Chuxu Zhang, Nitesh V. Chawla:
Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting. WWW 2020: 2320-2330 - [e9]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 978-1-61197-623-6 [contents]The conference was canceled because of the coronavirus pandemic, the reviewed papers are published in this volume. - [i48]Xian Wu, Chao Huang, Pablo Robles-Granda, Nitesh V. Chawla:
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series. CoRR abs/2002.03595 (2020) - [i47]Mandana Saebi, Steven J. Krieg, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla:
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning. CoRR abs/2003.06050 (2020) - [i46]Steven J. Krieg, Jennifer J. Schnur, Jermaine D. Marshall, Matthew M. Schoenbauer, Nitesh V. Chawla:
Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic. CoRR abs/2006.05983 (2020) - [i45]Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors. CoRR abs/2006.06820 (2020) - [i44]Pablo Robles-Granda, Suwen Lin, Xian Wu, Sidney D'Mello, Gonzalo J. Martínez, Koustuv Saha, Kari Nies, Gloria Mark, Andrew T. Campbell, Munmun De Choudhury, Anind K. Dey, Julie M. Gregg, Ted Grover, Stephen M. Mattingly, Shayan Mirjafari, Edward Moskal, Aaron Striegel, Nitesh V. Chawla:
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being. CoRR abs/2006.08364 (2020) - [i43]Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network. CoRR abs/2006.09610 (2020) - [i42]Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang:
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs. CoRR abs/2007.13004 (2020) - [i41]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
AttrE2vec: Unsupervised Attributed Edge Representation Learning. CoRR abs/2012.14727 (2020)
2010 – 2019
- 2019
- [j66]Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao:
Learning from data streams and class imbalance. Connect. Sci. 31(2): 103-104 (2019) - [j65]Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao:
Learning in the presence of class imbalance and concept drift. Neurocomputing 343: 1-2 (2019) - [j64]Shayan Mirjafari, Kizito Masaba, Ted Grover, Weichen Wang, Pino G. Audia, Andrew T. Campbell, Nitesh V. Chawla, Vedant Das Swain, Munmun De Choudhury, Anind K. Dey, Sidney K. D'Mello, Ge Gao, Julie M. Gregg, Krithika Jagannath, Kaifeng Jiang, Suwen Lin, Qiang Liu, Gloria Mark, Gonzalo J. Martínez, Stephen M. Mattingly, Edward Moskal, Raghu Mulukutla, Subigya Nepal, Kari Nies, Manikanta D. Reddy, Pablo Robles-Granda, Koustuv Saha, Anusha Sirigiri, Aaron Striegel:
Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(2): 37:1-37:24 (2019) - [j63]Vedant Das Swain, Koustuv Saha, Hemang Rajvanshy, Anusha Sirigiri, Julie M. Gregg, Suwen Lin, Gonzalo J. Martínez, Stephen M. Mattingly, Shayan Mirjafari, Raghu Mulukutla, Subigya Nepal, Kari Nies, Manikanta D. Reddy, Pablo Robles-Granda, Andrew T. Campbell, Nitesh V. Chawla, Sidney D'Mello, Anind K. Dey, Kaifeng Jiang, Qiang Liu, Gloria Mark, Edward Moskal, Aaron Striegel, Louis Tay, Gregory D. Abowd, Munmun De Choudhury:
A Multisensor Person-Centered Approach to Understand the Role of Daily Activities in Job Performance with Organizational Personas. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(4): 130:1-130:27 (2019) - [j62]Aastha Nigam, Reid A. Johnson, Dong Wang, Nitesh V. Chawla:
Characterizing online health and wellness information consumption: A study. Inf. Fusion 46: 33-43 (2019) - [j61]Shao-Yuan Li, Yuan Jiang, Nitesh V. Chawla, Zhi-Hua Zhou:
Multi-Label Learning from Crowds. IEEE Trans. Knowl. Data Eng. 31(7): 1369-1382 (2019) - [j60]Jun Tao, Martin Imre, Chaoli Wang, Nitesh V. Chawla, Hanqi Guo, Gokhan Sever, Seung Hyun Kim:
Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps. IEEE Trans. Vis. Comput. Graph. 25(1): 1236-1245 (2019) - [c158]Frederick Nwanganga, Nitesh V. Chawla:
Using Structural Similarity to Predict Future Workload Behavior in the Cloud. CLOUD 2019: 132-136 - [c157]Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla:
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. AAAI 2019: 1409-1416 - [c156]Koustuv Saha, Raghu Mulukutla, Kari Nies, Pablo Robles-Granda, Anusha Sirigiri, Dong Whi Yoo, Pino G. Audia, Andrew T. Campbell, Nitesh V. Chawla, Sidney K. D'Mello, Anind K. Dey, Manikanta D. Reddy, Kaifeng Jiang, Qiang Liu, Gloria Mark, Edward Moskal, Aaron Striegel, Munmun De Choudhury, Vedant Das Swain, Julie M. Gregg, Ted Grover, Suwen Lin, Gonzalo J. Martínez,