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Franklyn Wang

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2

NeurIPS Conference 2023 Conference Paper

SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking

  • Soukayna Mouatadid
  • Paulo Orenstein
  • Genevieve Flaspohler
  • Miruna Oprescu
  • Judah Cohen
  • Franklyn Wang
  • Sean Knight
  • Maria Geogdzhayeva

Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and climate adaptation but poses many challenges for the forecasting community. At this forecast horizon, physics-based dynamical models have limited skill, and the targets for prediction depend in a complex manner on both local weather variables and global climate variables. Recently, machine learning methods have shown promise in advancing the state of the art but only at the cost of complex data curation, integrating expert knowledge with aggregation across multiple relevant data sources, file formats, and temporal and spatial resolutions. To streamline this process and accelerate future development, we introduce SubseasonalClimateUSA, a curated dataset for training and benchmarking subseasonal forecasting models in the United States. We use this dataset to benchmark a diverse suite of models, including operational dynamical models, classical meteorological baselines, and ten state-of-the-art machine learning and deep learning-based methods from the literature. Overall, our benchmarks suggest simple and effective ways to extend the accuracy of current operational models. SubseasonalClimateUSA is regularly updated and accessible via the https: //github. com/microsoft/subseasonal_data/ Python package.

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ICML Conference 2021 Conference Paper

Putting the "Learning" into Learning-Augmented Algorithms for Frequency Estimation

  • Elbert Du
  • Franklyn Wang
  • Michael Mitzenmacher

In learning-augmented algorithms, algorithms are enhanced using information from a machine learning algorithm. In turn, this suggests that we should tailor our machine-learning approach for the target algorithm. We here consider this synergy in the context of the learned count-min sketch from (Hsu et al. , 2019). Learning here is used to predict heavy hitters from a data stream, which are counted explicitly outside the sketch. We show that an approximately sufficient statistic for the performance of the underlying count-min sketch is given by the coverage of the predictor, or the normalized $L^1$ norm of keys that are filtered by the predictor to be explicitly counted. We show that machine learning models which are trained to optimize for coverage lead to large improvements in performance over prior approaches according to the average absolute frequency error. Our source code can be found at https: //github. com/franklynwang/putting-the-learning-in-LAA.

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v2026.09.13