93 citations · 177 across the 10 of their papers we have counts for
11 papers
Adaptive Learning on User Segmentation: Universal to Specific Representation via Bipartite Neural Interaction
Xiaoyu Tan, Yongxin Deng, Chao Qu +4
Recently, models for user representation learning have been widely applied in click-through-rate (CTR) and conversion-rate (CVR) prediction. Usually, the model learns a universal u…
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
Xiaoming Shi, Shiyu Wang, Yuqi Nie +4
Deep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision d…
TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
Shiyu Wang, Haixu Wu, Xiaoming Shi +5
Time series forecasting is widely used in extensive applications, such as traffic planning and weather forecasting. However, real-world time series usually present intricate tempor…
Prompt-augmented Temporal Point Process for Streaming Event Sequence
Siqiao Xue, Yan Wang, Zhixuan Chu +7
Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling continuous-time event sequences, such as user activities on the web and financial transactions. In re…
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
Ming Jin, Shiyu Wang, Lintao Ma +8
Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision…
EasyTPP: Towards Open Benchmarking Temporal Point Processes
Siqiao Xue, Xiaoming Shi, Zhixuan Chu +9
Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point…
Full Scaling Automation for Sustainable Development of Green Data Centers
Shiyu Wang, Yinbo Sun, Xiaoming Shi +5
The rapid rise in cloud computing has resulted in an alarming increase in data centers' carbon emissions, which now accounts for >3% of global greenhouse gas emissions, necessitati…
A Graph Regularized Point Process Model For Event Propagation Sequence
Siqiao Xue, Xiaoming Shi, Hongyan Hao +4
Point process is the dominant paradigm for modeling event sequences occurring at irregular intervals. In this paper we aim at modeling latent dynamics of event propagation in graph…
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
Siqiao Xue, Xiaoming Shi, James Y Zhang +1
In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences. Existing state-of-the-art models do not perform well at…
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Siqiao Xue, Chao Qu, Xiaoming Shi +11
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating w…
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
Chao Qu, Xiaoyu Tan, Siqiao Xue +3
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such…