66 citations · 188 across the 20 of their papers we have counts for
21 papers
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez +2
Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have un…
DF2: Distribution-Free Decision-Focused Learning
Lingkai Kong, Wenhao Mu, Jiaming Cui +4
Decision-focused learning (DFL), which differentiates through the KKT conditions, has recently emerged as a powerful approach for predict-then-optimize problems. However, under pro…
Autoregressive Diffusion Model for Graph Generation
Lingkai Kong, Jiaming Cui, Haotian Sun +3
Diffusion-based graph generative models have recently obtained promising results for graph generation. However, existing diffusion-based graph generative models are mostly one-shot…
PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
Zhiyuan Zhao, Xueying Ding, B. Aditya Prakash
Physics-Informed Neural Networks (PINNs) have emerged as a promising deep learning framework for approximating numerical solutions to partial differential equations (PDEs). However…
End-to-End Stochastic Optimization with Energy-Based Model
Lingkai Kong, Jiaming Cui, Yuchen Zhuang +3
Decision-focused learning (DFL) was recently proposed for stochastic optimization problems that involve unknown parameters. By integrating predictive modeling with an implicitly di…
Data-Centric Epidemic Forecasting: A Survey
Alexander Rodríguez, Harshavardhan Kamarthi, Pulak Agarwal +4
The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While…