113 citations · 385 across the 19 of their papers we have counts for
19 papers
Learning Temporal Rules from Noisy Timeseries Data
Karan Samel, Zelin Zhao, Binghong Chen +4
Events across a timeline are a common data representation, seen in different temporal modalities. Individual atomic events can occur in a certain temporal ordering to compose highe…
Efficient Learning and Decoding of the Continuous-Time Hidden Markov Model for Disease Progression Modeling
Yu-Ying Liu, Alexander Moreno, Maxwell A. Xu +7
The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in…
A Note on General Statistics of Publicly Accessible Knowledge Bases
Feixiang Wang, Yixiang Fang, Yan Song +2
Knowledge bases are prevalent in various domains and have been widely used in a large number of real applications such as applications in online encyclopedia, social media, biomedi…
Understanding the Spread of COVID-19 Epidemic: A Spatio-Temporal Point Process View
Shuang Li, Lu Wang, Xinyun Chen +2
Since the first coronavirus case was identified in the U.S. on Jan. 21, more than 1 million people in the U.S. have confirmed cases of COVID-19. This infectious respiratory disease…
Recovery Guarantees for Time-varying Pairwise Comparison Matrices with Non-transitivity
Shuang Li, Michael B. Wakin
Pairwise comparison matrices have received substantial attention in a variety of applications, especially in rank aggregation, the task of flattening items into a one-dimensional (…
Landscape Correspondence of Empirical and Population Risks in the Eigendecomposition Problem
Shuang Li, Gongguo Tang, Michael B. Wakin
Spectral methods include a family of algorithms related to the eigenvectors of certain data-generated matrices. In this work, we are interested in studying the geometric landscape…
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
Lu Wang, Xiaofu Chang, Shuang Li +7
Dynamic graph modeling has recently attracted much attention due to its extensive applications in many real-world scenarios, such as recommendation systems, financial transactions,…
Digital Beamforming Robust to Time-Varying Carrier Frequency Offset
Shuang Li, Payam Nayeri, Michael B. Wakin
Adaptive interference cancellation is rapidly becoming a necessity for our modern wireless communication systems, due to the proliferation of wireless devices that interfere with e…
Energy-Based Models for Continual Learning
Shuang Li, Yilun Du, Gido M. van de Ven +1
We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing mo…
Imitation Learning of Neural Spatio-Temporal Point Processes
Shixiang Zhu, Shuang Li, Zhigang Peng +1
We present a novel Neural Embedding Spatio-Temporal (NEST) point process model for spatio-temporal discrete event data and develop an efficient imitation learning (a type of reinfo…
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Xinshi Chen, Shuang Li, Hui Li +3
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither…
Learning Temporal Point Processes via Reinforcement Learning
Shuang Li, Shuai Xiao, Shixiang Zhu +3
Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time. The generative processes of these event data can be ver…
Learning More Robust Features with Adversarial Training
Shuangtao Li, Yuanke Chen, Yanlin Peng +1
In recent years, it has been found that neural networks can be easily fooled by adversarial examples, which is a potential safety hazard in some safety-critical applications. Many…
Stochastic Greedy Algorithms For Multiple Measurement Vectors
Jing Qin, Shuang Li, Deanna Needell +4
Sparse representation of a single measurement vector (SMV) has been explored in a variety of compressive sensing applications. Recently, SMV models have been extended to solve mult…
Fake News Mitigation via Point Process Based Intervention
Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye +6
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. Th…
Data-Driven Threshold Machine: Scan Statistics, Change-Point Detection, and Extreme Bandits
Shuang Li, Yao Xie, Le Song
We present a novel distribution-free approach, the data-driven threshold machine (DTM), for a fundamental problem at the core of many learning tasks: choose a threshold for a given…
Detecting weak changes in dynamic events over networks
Shuang Li, Yao Xie, Mehrdad Farajtabar +2
Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring a…
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez +3
Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new lin…
Scan -Statistic for Kernel Change-Point Detection
Shuang Li, Yao Xie, Hanjun Dai +1
Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning. Kernel-based nonparametric statistics have been used for this task which…