3 citations · 3 across the 7 of their papers we have counts for
7 papers
Recurrent Interpolants for Probabilistic Time Series Prediction
Yu Chen, Marin Biloš, Sarthak Mittal +3
Sequential models like recurrent neural networks and transformers have become standard for probabilistic multivariate time series forecasting across various domains. Despite their…
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI
Sahil Garg, Anderson Schneider, Anant Raj +6
Building on the remarkable achievements in generative sampling of natural images, we propose an innovative challenge, potentially overly ambitious, which involves generating sample…
Structural Knowledge Informed Continual Multivariate Time Series Forecasting
Zijie Pan, Yushan Jiang, Dongjin Song +4
Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling the hidden dependencies among different time series can yield promising forecasting per…
Empowering Time Series Analysis with Large Language Models: A Survey
Yushan Jiang, Zijie Pan, Xikun Zhang +4
Recently, remarkable progress has been made over large language models (LLMs), demonstrating their unprecedented capability in varieties of natural language tasks. However, complet…
Learning to Abstain From Uninformative Data
Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard +5
Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high. In this pape…
Inference and Sampling of Point Processes from Diffusion Excursions
Ali Hasan, Yu Chen, Yuting Ng +3
Point processes often have a natural interpretation with respect to a continuous process. We propose a point process construction that describes arrival time observations in terms…