Danielle C. Maddix

发表

GluonTS: Probabilistic and Neural Time Series Modeling in Python

Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, 2020, J. Mach. Learn. Res..

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

Margot Gerritsen, Danielle C. Maddix, Luiz Sampaio, 2017, J. Comput. Phys..

GluonTS: Probabilistic Time Series Models in Python pdf

Syama Sundar Rangapuram, Yuyang Wang, Konstantinos Benidis, 2019, ArXiv.

Neural forecasting: Introduction and literature overview pdf

Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, 2020, ArXiv.

Deep Factors with Gaussian Processes for Forecasting pdf

Alex Smola, Yuyang Wang, Danielle C. Maddix, 2018, ArXiv.

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

Margot Gerritsen, Danielle C. Maddix, Luiz Sampaio, 2017, J. Comput. Phys..

Deep Factors for Forecasting pdf

Tim Januschowski, Alex Smola, Jan Gasthaus, 2019, ICML.

Domain Adaptation for Time Series Forecasting via Attention Sharing

Xifeng Yan, Yuyang Wang, Danielle C. Maddix, 2021 .

Attention-based Domain Adaptation for Time Series Forecasting pdf

Xifeng Yan, Yuyang Wang, Danielle C. Maddix, 2021, ArXiv.

Neural time series models with GluonTS Time Series Workshop ICML 2019

Syama Sundar Rangapuram, Danielle C. Maddix, Ali Caner Türkmen, 2019 .

Diagnosing Malignant versus Benign Breast Tumors via Machine Learning Techniques in High Dimensions

Danielle C. Maddix, 2014 .

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Syama Sundar Rangapuram, Danielle C. Maddix, Bernie Wang, 2022, ACM Computing Surveys.

Domain Adaptation for Time Series Forecasting via Attention Sharing

Danielle C. Maddix, Bernie Wang, Xifeng Yan, 2021, ICML.

Deep Learning for Time Series Forecasting: Tutorial and Literature Survey

Syama Sundar Rangapuram, Danielle C. Maddix, Bernie Wang, 2020, ACM Comput. Surv..

Towards Reverse Causal Inference on Panel Data: Precise Formulation and Challenges

Danielle C. Maddix, Bernie Wang, Jiayao Zhang, 2022 .

Guiding continuous operator learning through Physics-based boundary constraints

Danielle C. Maddix, S. Alizadeh, Gaurav Gupta, 2022, ArXiv.

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

Danielle C. Maddix, Xingjian Shi, Hongya Wang, 2022, ArXiv.

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

Danielle C. Maddix, C. Faloutsos, Rui Wang, 2020, L4DC.

Learning Physical Models that Can Respect Conservation Laws

Michael W. Mahoney, Danielle C. Maddix, S. Alizadeh, 2023, ArXiv.

Guiding continuous operator learning through Physics-based boundary constraints

Danielle C. Maddix, S. Alizadeh, Gaurav Gupta, 2022, ICLR.

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

Danielle C. Maddix, Jan Gasthaus, Yuyang Wang, 2021, AISTATS.

Learning Physical Models that Can Respect Conservation Laws

Michael W. Mahoney, Danielle C. Maddix, S. Alizadeh, 2023, ArXiv.

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

Yuyang Wang, Danielle C. Maddix, Danielle C Maddix, 2021, ArXiv.

PreDiff: Precipitation Nowcasting with Latent Diffusion Models

Danielle C. Maddix, Bernie Wang, Xingjian Shi, 2023, NeurIPS.

Cross-Frequency Time Series Meta-Forecasting

Danielle C. Maddix, Huibin Shen, Hongya Wang, 2023, ArXiv.

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

Danielle C. Maddix, Hilaf Hasson, Yuyang Wang, 2023, International Conference on Machine Learning.

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

Rose Yu, Christos Faloutsos, Rui Wang, 2020, L4DC.

Learning Physical Models that Can Respect Conservation Laws

Michael W. Mahoney, Danielle C. Maddix, S. Alizadeh, 2023, ICML.

Chronos: Learning the Language of Time Series

Syama Sundar Rangapuram, Danielle C. Maddix, Sebastian Pineda Arango, 2024, 2403.07815.

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

Danielle C. Maddix, S. Alizadeh, Gaurav Gupta, 2024, ArXiv.