48 citations · 166 across the 31 of their papers we have counts for
17 papers · 1 filter
QuitoBench: A High-Quality Open Time Series Forecasting Benchmark
Siqiao Xue, Zhaoyang Zhu, Wei Zhang +7
Time series forecasting is critical across finance, healthcare, and cloud computing, yet progress is constrained by a fundamental bottleneck: the scarcity of large-scale, high-qual…
Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning
Gangwei Jiang, Caigao Jiang, Zhaoyi Li +5
Catastrophic forgetting (CF) poses a significant challenge in machine learning, where a model forgets previously learned information upon learning new tasks. Despite the advanced c…
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…
GMP-AR: Granularity Message Passing and Adaptive Reconciliation for Temporal Hierarchy Forecasting
Fan Zhou, Chen Pan, Lintao Ma +9
Time series forecasts of different temporal granularity are widely used in real-world applications, e.g., sales prediction in days and weeks for making different inventory plans. H…
Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models
Zhixuan Chu, Lei Zhang, Yichen Sun +4
The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis of high-fidelity video content guided by textual descriptions. Despite this significant pro…
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…
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
Yan Wang, Zhixuan Chu, Tao Zhou +9
Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries. Temporal point processes(TPPs) are the standar…
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
Ming Jin, Yaxuan Kong, Yuxuan Liang +13
Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record d…
CoCA: Fusing Position Embedding with Collinear Constrained Attention in Transformers for Long Context Window Extending
Shiyi Zhu, Jing Ye, Wei Jiang +4
Self-attention and position embedding are two key modules in transformer-based Large Language Models (LLMs). However, the potential relationship between them is far from well studi…
Continual Learning in Predictive Autoscaling
Hongyan Hao, Zhixuan Chu, Shiyi Zhu +7
Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. Ho…
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…
Automatic Deduction Path Learning via Reinforcement Learning with Environmental Correction
Shuai Xiao, Chen Pan, Min Wang +6
Automatic bill payment is an important part of business operations in fintech companies. The practice of deduction was mainly based on the total amount or heuristic search by divid…
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…
Learning Large-scale Universal User Representation with Sparse Mixture of Experts
Caigao Jiang, Siqiao Xue, James Zhang +3
Learning user sequence behaviour embedding is very sophisticated and challenging due to the complicated feature interactions over time and high dimensions of user features. Recent…
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…