48 citations · 68 across the 8 of their papers we have counts for
8 papers
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…
Continuous Invariance Learning
Yong Lin, Fan Zhou, Lu Tan +8
Invariance learning methods aim to learn invariant features in the hope that they generalize under distributional shifts. Although many tasks are naturally characterized by continu…
DiffusionTrack: Diffusion Model For Multi-Object Tracking
Run Luo, Zikai Song, Lintao Ma +3
Multi-object tracking (MOT) is a challenging vision task that aims to detect individual objects within a single frame and associate them across multiple frames. Recent MOT approach…
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies
Fan Zhou, Chen Pan, Lintao Ma +9
Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities,…
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation
Shiyu Wang, Fan Zhou, Yinbo Sun +3
Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconc…
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…
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…
A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning
Shijun Wang, Baocheng Zhu, Lintao Ma +1
In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints. To solve the problem, we first convert it to a dual prob…