| GluonTS: Probabilistic and Neural Time Series Modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... Journal of Machine Learning Research 21 (116), 1-6, 2020 | 183* | 2020 |
| Deep factors for forecasting Y Wang, A Smola, D Maddix, J Gasthaus, D Foster, T Januschowski International conference on machine learning, 6607-6617, 2019 | 129 | 2019 |
| Deep Learning for Time Series Forecasting: Tutorial and Literature Survey K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ... ACM Computing Surveys (CSUR), 2018 | 109* | 2018 |
| Deep factors with gaussian processes for forecasting DC Maddix, Y Wang, A Smola arXiv preprint arXiv:1812.00098, 2018 | 30 | 2018 |
| Bridging physics-based and data-driven modeling for learning dynamical systems R Wang, D Maddix, C Faloutsos, Y Wang, R Yu Learning for Dynamics and Control, 385-398, 2021 | 27 | 2021 |
| Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting Y Park, D Maddix, FX Aubet, K Kan, J Gasthaus, Y Wang International Conference on Artificial Intelligence and Statistics 151, 8127 …, 2022 | 17 | 2022 |
| Domain adaptation for time series forecasting via attention sharing X Jin, Y Park, D Maddix, H Wang, Y Wang International Conference on Machine Learning, 10280-10297, 2022 | 8 | 2022 |
| Numerical Artifacts in the Generalized Porous Medium Equation: Why Harmonic Averaging Itself Is Not to Blame D Maddix, M Gerritsen, L Sampaio Journal of Computational Physics 361, 280-298, 2018 | 8 | 2018 |
| Numerical artifacts in the discontinuous Generalized Porous Medium Equation: How to avoid spurious temporal oscillations DC Maddix, L Sampaio, M Gerritsen Journal of Computational Physics 368, 277-298, 2018 | 5 | 2018 |
| Advanced Fluid Reduced Order Models for Compressible Flow. IK Tezaur, JA Fike, KT Carlberg, MF Barone, D Maddix, EE Mussoni, ... Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2017 | 5 | 2017 |
| Diagnosing malignant versus benign breast tumors via machine learning techniques in high dimensions DC Maddix Stanford Univ., Stanford, CA, USA, Tech. Rep, 2014 | 3 | 2014 |
| Minres: Sparse symmetric equations CC Paige, MA Saunders, SC Choi, D Orban, UE Villa, D Maddix, S Regev | 2 | 2004 |
| Learning Dynamical Systems Requires Rethinking Generalization R Wang, D Maddix, C Faloutsos, W Yuyang, R Yu Interpretable Inductive Bias and Physically Structured Learning NeurIPS Workshop, 2020 | 1 | 2020 |
| GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamics KA Wang, D Maddix, Y Wang I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 80-85, 2022 | | 2022 |
| Guiding continuous operator learning through Physics-based boundary constraints N Saad, G Gupta, S Alizadeh, DC Maddix arXiv preprint arXiv:2212.07477, 2022 | | 2022 |
| Towards Reverse Causal Inference on Panel Data: Precise Formulation and Challenges J Zhang, Y Park, DC Maddix, D Roth, B Wang A causal view on dynamical systems, NeurIPS 2022 workshop, 2022 | | 2022 |
| First De-Trend then Attend: Rethinking Attention for Time-Series Forecasting X Zhang, X Jin, K Gopalswamy, G Gupta, Y Park, X Shi, H Wang, ... NeurIPS'22 Workshop on All Things Attention: Bridging Different Perspectives …, 2022 | | 2022 |
| Modeling Advection on Directed Graphs using Matérn Gaussian Processes for Traffic Flow DC Maddix, N Saad, Y Wang Machine Learning and the Physical Sciences (MLPS) NeurIPS 2021 Workshop, 2021 | | 2021 |
| AutoODE: Bridging Physics-based and Data-driven modeling for COVID-19 Forecasting R Wang, D Maddix, C Faloutsos, Y Wang, R Yu | | 2020 |
| Numerical Artifacts in the Generalized Porous Medium Equation and Solutions DC Maddix Stanford University, 2018 | | 2018 |
