Showcasing UVA ECE Research in Machine Learning, AI, Data Mining and Graph Mining

UVA ECE has built a strong group at the confluence of signals, systems, communications, control and machine learning to produce cutting-edge research publications in the top machine learning, artificial intelligence, data and graph mining conferences. In 2020 alone, ECE faculty published 21 papers in the most selective conferences with acceptance rates well under 25%. This is an impressive record that is testament to the agility and vitality of the mathematical and statistical modeling and optimization side of the department.

AAAI Conference on Artificial Intelligence

February 2020

L. Cheng, J. Li, K. S. Candan, H. Liu
Tracking Disaster Footprints with Social Stream Data

N. Kargas, N.D. Sidiropoulos
Nonlinear System Identification via Tensor Completion

T. Jin, P. Xu, Q. Gu, F. Farnoud
Rank Aggregation via Heterogeneous Thurstone Preference Models


ACM International Conference on Information and Knowledge Management

October 2020

K. Ding, J. Wang, J. Li, K. Shu, C. Liu, H. Liu
Graph Prototypical Networks for Few-shot Learning on Attributed Networks

N. Wang, M. Luo, K. Ding, L. Zhang, J. Li, Q. Zheng
Graph Few-shot Learning with Attribute Matching


ACM International Conference on Knolwedge Discovery and Data Mining

August 2020

A. Konar, and N.D. Sidiropoulos
Mining Large Quasi-cliques with Quality Guarantees from Vertex Neighborhoods


ACM International Conference on Web Search and Data Mining

February 2020

R. Guo, J. Li, H. Liu
Learning Individual Causal Effects from Networked Observational Data


Conference on Empirical Methods in Natural Language Processing

November 2020

K. Ding, J. Wang, J. Li, D. Li, H. Liu
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification


Conference on Neural Information Processing Systems

December 2020

H.-S. Lee, Y. Zhang, W. Zame, C. Shen, J.-W. Lee, and M. van der Schaar
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification


European Conference on Machine Learning and Principles and Practice of Knowledge Discovery

September 2020

J. Wang, M. Luo, F. Suya, J. Li, Z. Yang, Q. Zheng
Scalable Attack on Graph Data by Injecting Vicious Nodes


IEEE International Conference on Data Mining

November 2020

A. Konar, and N.D. Sidiropoulos
Soft Graph Matching: Submodular Relaxation and Lovasz Extension


IEEE and Computer Vision Foundation Conference on Computer Vision and Pattern Recognition

June 2020

J. Wang and M. Zhang
DeepFLASH: An Efficient Network for Learning-Based Medical Image Registration


International Conference on Artificial Intelligence and Statistics

August 2020

H. Lee, C. Shen, J. Jordon, and M. van der Schaar
Contextual Constrained Learning for Dose-Finding Clinical Trials

C. Shi, W. Xiong, C. Shen, and J. Yang
Decentralized Multi-player Multi-armed Bandits with No Collision Information

W. Wu, J. Yang, and C. Shen
Stochastic Linear Contextual Bandits with Diverse Contexts


International Conference on Machine Learning

July 2020

C. Shen, Z. Wang, S. Villa, and M. van der Schaar
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints


International Joint Conference on Artificial Intelligence

January 2021

K. Ding, J. Li, N. Agarwal, H. Liu
Inductive Anomaly Detection on Attributed Networks

R. Guo, J. Li, Y. Li, K. S. Candan, A. Raglin, H. Liu
IGNITE: A Minimax Game Toward Learning Individual Treatment Effects from Networked Observational Data


Pacific-Asia Conference on Knowledge Discovery and Data Mining

May 2020

F. Almutairi, C. Kanatsoulis, and N.D. Sidiropoulos
Tendi: Tensor Disaggregation from Multiple Coarse Views


SIAM International Conference on Data Mining

May 2020

R. Guo, J. Li, H. Liu
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data

B. Yang, K. Huang, and N.D. Sidiropoulos
Identifying Potential Investors with Data Driven Approaches