5 citations · 15 across the 4 of their papers we have counts for
4 papers
A Gaussian Sliding Windows Regression Model for Hydrological Inference
Stefan Schrunner, Parham Pishrobat, Joseph Janssen +4
Statistical models are an essential tool to model, forecast and understand the hydrological processes in watersheds. In particular, the understanding of time lags associated with t…
How to out-perform default random forest regression: choosing hyperparameters for applications in large-sample hydrology
Divya K. Bilolikar, Aishwarya More, Aella Gong +1
Predictions are a central part of water resources research. Historically, physically-based models have been preferred; however, they have largely failed at modeling hydrological pr…
Learning from limited temporal data: Dynamically sparse historical functional linear models with applications to Earth science
Joseph Janssen, Shizhe Meng, Asad Haris +5
Scientists and statisticians often want to learn about the complex relationships that connect two time-varying variables. Recent work on sparse functional historical linear models…
Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees
Joseph Janssen, Vincent Guan, Elina Robeva
Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter. Marginal contr…