Deep Factors with Gaussian Processes for Forecasting. - Danielle C. Maddix; Yuyang Wang 001; Alex Smola (CoRR, 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)

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)

Deep Factors for Forecasting. - Yuyang Wang 001; Alex Smola; Danielle C. Maddix; Jan Gasthaus; Dean P. Foster; Tim Januschowski (ICML, 2019)

GluonTS: Probabilistic Time Series Models in Python. - Alexander Alexandrov 001; 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; et al. (CoRR, 2019)

Neural forecasting: Introduction and literature overview. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Bernie Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; Laurent Callot; Tim Januschowski (CoRR, 2020)

GluonTS: Probabilistic and Neural Time Series Modeling in Python. - Alexander Alexandrov 001; 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; et al. (J. Mach. Learn. Res., 2020)

GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics. - Ke Alexander Wang; Danielle C. Maddix; Yuyang Wang 001 (ICBINB@NeurIPS, 2021)

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems. - Rui Wang 086; Danielle C. Maddix; Christos Faloutsos; Yuyang Wang 001; Rose Yu (L4DC, 2021)

Attention-based Domain Adaptation for Time Series Forecasting. - Xiaoyong Jin; Youngsuk Park; Danielle C. Maddix; Yuyang Wang 001; Xifeng Yan (CoRR, 2021)

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 001 (AISTATS, 2022)

Domain Adaptation for Time Series Forecasting via Attention Sharing. - Xiaoyong Jin; Youngsuk Park; Danielle C. Maddix; Hao Wang 014; Yuyang Wang 001 (ICML, 2022)

Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow. - Danielle C. Maddix; Nadim Saad; Yuyang Wang 001 (CoRR, 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 014; Danielle C. Maddix; Yuyang Wang 001 (CoRR, 2022)

Guiding continuous operator learning through Physics-based boundary constraints. - Nadim Saad; Gaurav Gupta; Shima Alizadeh; Danielle C. Maddix (ICLR, 2023)

Learning Physical Models that Can Respect Conservation Laws. - Derek Hansen; Danielle C. Maddix; Shima Alizadeh; Gaurav Gupta; Michael W. Mahoney (ICML, 2023)

Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting. - Hilaf Hasson; Danielle C. Maddix; Bernie Wang 001; Gaurav Gupta; Youngsuk Park (ICML, 2023)

PreDiff: Precipitation Nowcasting with Latent Diffusion Models. - Zhihan Gao 001; Xingjian Shi; Boran Han; Hao Wang 014; Xiaoyong Jin; Danielle C. Maddix; Yi Zhu 001; Mu Li 003; Yuyang Wang 001 (NeurIPS, 2023)

Cross-Frequency Time Series Meta-Forecasting. - Mike Van Ness; Huibin Shen; Hao Wang 014; Xiaoyong Jin; Danielle C. Maddix; Karthick Gopalswamy (CoRR, 2023)

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey. - Konstantinos Benidis; Syama Sundar Rangapuram; Valentin Flunkert; Yuyang Wang 001; Danielle C. Maddix; Ali Caner Türkmen; Jan Gasthaus; Michael Bohlke-Schneider; David Salinas; Lorenzo Stella; François-Xavier Aubet; Laurent Callot; et al. (ACM Comput. Surv., 2023)

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; Bernie Wang 001 (ICML, 2024)

Transferring Knowledge From Large Foundation Models to Small Downstream Models. - Shikai Qiu; Boran Han; Danielle C. Maddix; Shuai Zhang 007; Bernie Wang 001; Andrew Gordon Wilson (ICML, 2024)

WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics. - 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 (NeurIPS, 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; et al. (CoRR, 2024)

Comparing and Contrasting Deep Learning Weather Prediction Backbones on Navier-Stokes and Atmospheric Dynamics. - Matthias Karlbauer; Danielle C. Maddix; Abdul Fatir Ansari; Boran Han; Gaurav Gupta; Yuyang Wang 001; Andrew Stuart; Michael W. Mahoney (CoRR, 2024)

AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics. - Neil Ashton; Danielle C. Maddix; Samuel Gundry; Parisa M. Shabestari (CoRR, 2024)

DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics. - Neil Ashton; Charles Mockett; Marian Fuchs; Louis Fliessbach; Hendrik Hetmann; Thilo Knacke; Norbert Schonwald; Vangelis Skaperdas; Grigoris Fotiadis; Astrid Walle; Burkhard Hupertz; Danielle C. Maddix (CoRR, 2024)

Hard Constraint Guided Flow Matching for Gradient-Free Generation of PDE Solutions. - Chaoran Cheng; Boran Han; Danielle C. Maddix; Abdul Fatir Ansari; Andrew Stuart; Michael W. Mahoney; Yuyang Wang 001 (CoRR, 2024)

Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization. - 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 001 (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; et al. (Trans. Mach. Learn. Res., 2024)

A Flexible Forecasting Stack. - Tim Januschowski; Yuyang Wang 001; Jan Gasthaus; Syama Sundar Rangapuram; Caner Türkmen; Jasper Zschiegner; Lorenzo Stella; Michael Bohlke-Schneider; Danielle C. Maddix; Konstantinos Benidis; Alexander Alexandrov 001; Christos Faloutsos; et al. (Proc. VLDB Endow., 2024)

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. - Sebastian Pineda Arango; Pedro Mercado; Shubham Kapoor; Abdul Fatir Ansari; Lorenzo Stella; Huibin Shen; Hugo Senetaire; Ali Caner Türkmen; Oleksandr Shchur; Danielle C. Maddix; Michael Bohlke-Schneider; Bernie Wang 001; et al. (AISTATS, 2025)

Gradient-Free Generation for Hard-Constrained Systems. - Chaoran Cheng; Boran Han; Danielle C. Maddix; Abdul Fatir Ansari; Andrew Stuart; Michael W. Mahoney; Bernie Wang 001 (ICLR, 2025)

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables. - Sebastian Pineda-Arango; Pedro Mercado; Shubham Kapoor; Abdul Fatir Ansari; Lorenzo Stella; Huibin Shen; Hugo Senetaire; Caner Turkmen; Oleksandr Shchur; Danielle C. Maddix; Michael Bohlke-Schneider; Yuyang Wang 001; et al. (CoRR, 2025)

End-to-End Probabilistic Framework for Learning with Hard Constraints. - Utkarsh Utkarsh; Danielle C. Maddix; Ruijun Ma; Michael W. Mahoney; Yuyang Wang 001 (CoRR, 2025)

Does Multimodality Lead to Better Time Series Forecasting? - Xiyuan Zhang; Boran Han; Haoyang Fang; Abdul Fatir Ansari; Shuai Zhang 007; Danielle C. Maddix; Cuixiong Hu; Andrew Gordon Wilson; Michael W. Mahoney; Hao Wang 014; Yan Liu; Huzefa Rangwala; et al. (CoRR, 2025)