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ORCIDDanielle C. Maddix
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2020 – today
- 2024
[j5]Tim Januschowski, Yuyang Wang, Jan Gasthaus, Syama Sundar Rangapuram, Caner Turkmen, Jasper Zschiegner, Lorenzo Stella, Michael Bohlke-Schneider, Danielle C. Maddix, Konstantinos Benidis, Alexander Alexandrov, Christos Faloutsos, Sebastian Schelter:
A Flexible Forecasting Stack. Proc. VLDB Endow. 17(12): 3883-3892 (2024)- [c11]S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Bernie Wang:
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. ICML 2024 - [c10]Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Bernie Wang, Andrew Gordon Wilson:
Transferring Knowledge From Large Foundation Models to Small Downstream Models. ICML 2024 - [i23]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:
Chronos: Learning the Language of Time Series. CoRR abs/2403.07815 (2024) - [i22]S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W. Mahoney, Yuyang Wang:
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs. CoRR abs/2403.10642 (2024) - [i21]Shikai Qiu, Boran Han, Danielle C. Maddix, Shuai Zhang, Yuyang Wang, Andrew Gordon Wilson:
Transferring Knowledge from Large Foundation Models to Small Downstream Models. CoRR abs/2406.07337 (2024) - [i20]Matthias Karlbauer, Danielle C. Maddix, Abdul Fatir Ansari, Boran Han, Gaurav Gupta, Yuyang Wang, Andrew Stuart, Michael W. Mahoney:
Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics. CoRR abs/2407.14129 (2024) - [i19]Neil Ashton
, Jordan B. Angel, Aditya S. Ghate, Gaetan K. W. Kenway, Man Long Wong, Cetin C. Kiris, Astrid Walle, Danielle C. Maddix, Gary Page:
WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics. CoRR abs/2407.19320 (2024) - [i18]Neil Ashton, Danielle C. Maddix, Samuel Gundry, Parisa M. Shabestari:
AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics. CoRR abs/2407.20801 (2024) - [i17]Chaoran Cheng, Boran Han, Danielle C. Maddix, Abdul Fatir Ansari, Andrew Stuart, Michael W. Mahoney, Yuyang Wang:
Hard Constraint Guided Flow Matching for Gradient-Free Generation of PDE Solutions. CoRR abs/2412.01786 (2024) - [i16]Luca Masserano, Abdul Fatir Ansari, Boran Han, Xiyuan Zhang, Christos Faloutsos, Michael W. Mahoney, Andrew Gordon Wilson, Youngsuk Park, Syama Sundar Rangapuram, Danielle C. Maddix, Yuyang Wang:
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. CoRR abs/2412.05244 (2024) - 2023
- [j4]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:
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. ACM Comput. Surv. 55(6): 121:1-121:36 (2023) - [c9]Nadim Saad, Gaurav Gupta, Shima Alizadeh, Danielle C. Maddix:
Guiding continuous operator learning through Physics-based boundary constraints. ICLR 2023 - [c8]Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney:
Learning Physical Models that Can Respect Conservation Laws. ICML 2023: 12469-12510 - [c7]Hilaf Hasson, Danielle C. Maddix, Bernie Wang, Gaurav Gupta, Youngsuk Park:
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. ICML 2023: 12616-12632 - [c6]Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Yi Zhu, Mu Li, Yuyang Wang:
PreDiff: Precipitation Nowcasting with Latent Diffusion Models. NeurIPS 2023 - [i15]Mike Van Ness, Huibin Shen, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Karthick Gopalswamy:
Cross-Frequency Time Series Meta-Forecasting. CoRR abs/2302.02077 (2023) - [i14]Derek Hansen, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Michael W. Mahoney:
Learning Physical Models that Can Respect Conservation Laws. CoRR abs/2302.11002 (2023) - [i13]Hilaf Hasson, Danielle C. Maddix, Yuyang Wang, Gaurav Gupta, Youngsuk Park:
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. CoRR abs/2305.15786 (2023) - [i12]Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle C. Maddix, Yi Zhu, Mu Li, Yuyang Wang:
PreDiff: Precipitation Nowcasting with Latent Diffusion Models. CoRR abs/2307.10422 (2023) - 2022
- [c5]Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang:
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. AISTATS 2022: 8127-8150 - [c4]Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Hao Wang, Yuyang Wang:
Domain Adaptation for Time Series Forecasting via Attention Sharing. ICML 2022: 10280-10297 - [i11]Danielle C. Maddix, Nadim Saad, Yuyang Wang:
Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow. CoRR abs/2201.00001 (2022) - [i10]Nadim Saad, Gaurav Gupta, Shima Alizadeh, Danielle C. Maddix:
Guiding continuous operator learning through Physics-based boundary constraints. CoRR abs/2212.07477 (2022) - [i9]Xiyuan Zhang, Xiaoyong Jin, Karthick Gopalswamy, Gaurav Gupta, Youngsuk Park, Xingjian Shi, Hao Wang, Danielle C. Maddix, Yuyang Wang:
First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting. CoRR abs/2212.08151 (2022) - 2021
- [c3]Ke Alexander Wang, Danielle C. Maddix, Yuyang Wang:
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics. ICBINB@NeurIPS 2021: 80-85 - [c2]Rui Wang, Danielle C. Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu:
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems. L4DC 2021: 385-398 - [i8]Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Yuyang Wang, Xifeng Yan:
Attention-based Domain Adaptation for Time Series Forecasting. CoRR abs/2102.06828 (2021) - [i7]Youngsuk Park, Danielle C. Maddix, François-Xavier Aubet, Kelvin Kan, Jan Gasthaus, Yuyang Wang:
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting. CoRR abs/2111.06581 (2021) - [i6]Ke Alexander Wang, Danielle C. Maddix, Yuyang Wang:
GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics. CoRR abs/2112.09964 (2021) - 2020
- [j3]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:
GluonTS: Probabilistic and Neural Time Series Modeling in Python. J. Mach. Learn. Res. 21: 116:1-116:6 (2020) - [i5]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:
Neural forecasting: Introduction and literature overview. CoRR abs/2004.10240 (2020) - [i4]Rui Wang, Danielle C. Maddix, Christos Faloutsos, Yuyang Wang, Rose Yu:
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems. CoRR abs/2011.10616 (2020)
2010 – 2019
- 2019
- [c1]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. ICML 2019: 6607-6617 - [i3]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. CoRR abs/1905.12417 (2019) - [i2]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:
GluonTS: Probabilistic Time Series Models in Python. CoRR abs/1906.05264 (2019) - 2018
- [j2]Danielle C. Maddix, Luiz Sampaio, Margot Gerritsen:
Numerical artifacts in the Generalized Porous Medium Equation: Why harmonic averaging itself is not to blame. J. Comput. Phys. 361: 280-298 (2018) - [j1]Danielle C. Maddix, Luiz Sampaio, Margot Gerritsen:
Numerical artifacts in the discontinuous Generalized Porous Medium Equation: How to avoid spurious temporal oscillations. J. Comput. Phys. 368: 277-298 (2018) - [i1]Danielle C. Maddix, Yuyang Wang, Alex Smola:
Deep Factors with Gaussian Processes for Forecasting. CoRR abs/1812.00098 (2018)
Coauthor Index
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