27 citations · 113 across the 27 of their papers we have counts for
9 papers · 1 filter
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
Zichong Li, Yanbo Xu, Simiao Zuo +4
Transformer Hawkes process models have shown to be successful in modeling event sequence data. However, most of the existing training methods rely on maximizing the likelihood of e…
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Alexander Bukharin, Yan Li, Yue Yu +6
Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive…
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks
Simiao Zuo, Haoming Jiang, Qingyu Yin +3
Graph neural network (GNN) pre-training methods have been proposed to enhance the power of GNNs. Specifically, a GNN is first pre-trained on a large-scale unlabeled graph and then…
Differentially Private Estimation of Hawkes Process
Simiao Zuo, Tianyi Liu, Tuo Zhao +1
Point process models are of great importance in real world applications. In certain critical applications, estimation of point process models involves large amounts of sensitive pe…
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
Qingru Zhang, Simiao Zuo, Chen Liang +4
Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks. However, these models contain enormous amounts…
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization
Chen Liang, Simiao Zuo, Minshuo Chen +5
The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ``lottery tickets'', and training a certain collection of them (i.e., a subnetwork) can match t…
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach
Simiao Zuo, Chen Liang, Haoming Jiang +5
Adversarial regularization has been shown to improve the generalization performance of deep learning models in various natural language processing tasks. Existing works usually for…
A Hypergradient Approach to Robust Regression without Correspondence
Yujia Xie, Yixiu Mao, Simiao Zuo +4
We consider a variant of regression problem, where the correspondence between input and output data is not available. Such shuffled data is commonly observed in many real world pro…
Transformer Hawkes Process
Simiao Zuo, Haoming Jiang, Zichong Li +2
Modern data acquisition routinely produce massive amounts of event sequence data in various domains, such as social media, healthcare, and financial markets. These data often exhib…