Danielle C. Maddix

According to our database, Danielle C. Maddix authored at least 22 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs.

S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Yuyang Wang

CoRR, 2024

Chronos: Learning the Language of Time Series.

Abdul Fatir Ansari, Lorenzo Stella, Ali Caner Türkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda-Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang

CoRR, 2024

2023

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey.

Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, François-Xavier Aubet, Laurent Callot, Tim Januschowski

ACM Comput. Surv., 2023

Cross-Frequency Time Series Meta-Forecasting.

Mike Van Ness, Huibin Shen, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Karthick Gopalswamy

CoRR, 2023

PreDiff: Precipitation Nowcasting with Latent Diffusion Models.

Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Yi Zhu, Mu Li, Yuyang Wang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting.

Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park

Proceedings of the International Conference on Machine Learning, 2023

Learning Physical Models that Can Respect Conservation Laws.

Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney

Proceedings of the International Conference on Machine Learning, 2023

Guiding continuous operator learning through Physics-based boundary constraints.

Nadim Saad, Gaurav Gupta, Shima Alizadeh, Danielle C. Maddix

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting.

Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang

CoRR, 2022

Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow.

Danielle C. Maddix, Nadim Saad, Yuyang Wang

CoRR, 2022

Domain Adaptation for Time Series Forecasting via Attention Sharing.

Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang

Proceedings of the International Conference on Machine Learning, 2022

Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting.

Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Attention-based Domain Adaptation for Time Series Forecasting.

Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Yuyang Wang, Xifeng Yan

CoRR, 2021

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems.

Rui Wang, Danielle C. Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu

Proceedings of the 3rd Annual Conference on Learning for Dynamics and Control, 2021

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics.

Ke Alexander Wang, Danielle C. Maddix, Yuyang Wang

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

2020

GluonTS: Probabilistic and Neural Time Series Modeling in Python.

Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang

J. Mach. Learn. Res., 2020

Neural forecasting: Introduction and literature overview.

Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle C. Maddix, Ali Caner Türkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Laurent Callot, Tim Januschowski

CoRR, 2020

2019

GluonTS: Probabilistic Time Series Models in Python.

Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang

CoRR, 2019

Deep Factors for Forecasting.

Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Numerical artifacts in the discontinuous Generalized Porous Medium Equation: How to avoid spurious temporal oscillations.

Danielle C. Maddix, Luiz Sampaio, Margot Gerritsen

J. Comput. Phys., 2018

Numerical artifacts in the Generalized Porous Medium Equation: Why harmonic averaging itself is not to blame.

Danielle C. Maddix, Luiz Sampaio, Margot Gerritsen

J. Comput. Phys., 2018

Deep Factors with Gaussian Processes for Forecasting.

Danielle C. Maddix, Yuyang Wang, Alex Smola

CoRR, 2018