Explanipedia

Precipitation nowcasting of satellite data using physically-aligned neural networks Open

Antônio Catão, Melvin Poveda, Leonardo Voltarelli, Paulo Orenstein · 2025

Accurate short-term precipitation forecasts predominantly rely on dense weather-radar networks, limiting operational value in places most exposed to climate extremes. We present TUPANN (Transferable and Universal Physics-Aligned Nowcasting…

Finite-sample properties of the trimmed mean Open

Roberto I. Oliveira, Paulo Orenstein, Zoraida F. Rico · 2025

The trimmed mean of $n$ scalar random variables from a distribution $P$ is the variant of the standard sample mean where the $k$ smallest and $k$ largest values in the sample are discarded for some parameter $k$. In this paper, we look at …

Deep Hashing via Householder Quantization Open

Lucas R. Schwengber, Lucas H. G. Resende, Paulo Orenstein, Roberto I. Oliveira · 2023

Hashing is at the heart of large-scale image similarity search, and recent methods have been substantially improved through deep learning techniques. Such algorithms typically learn continuous embeddings of the data. To avoid a subsequent …

OP06.03: Machine learning for amniotic fluid volume prediction Open

Heron Werner, Gerson Ribeiro, Paulo Orenstein, Roberto I. Oliveira, Raquel S. G. R. Seixas , et al. · 2023

Conclusions:The ECG-based DLM software (AiTiALVSD software version 1.0.0) is a non-invasive and effective way of identifying cardiomyopathies occurring during the peripartum period, and it could potentially be used as a highly sensitive sc…

AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification Open

Daniel Csillag, Lucas Monteiro Paes, Thiago Ramos, João Vitor Romano, R. B. Schüller , et al. · 2023

Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solutio…

Adaptive bias correction for improved subseasonal forecasting Open

Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu , et al. · 2023

Adaptive Bias Correction for Improved Subseasonal Forecasting Open

Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu , et al. · 2022

Subseasonal forecasting -- predicting temperature and precipitation 2 to 6 weeks ahead -- is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advance…

Robust importance sampling with adaptive winsorization Open

Paulo Orenstein · 2022

Importance sampling is a widely used technique to estimate properties of a distribution. This paper investigates trading-off some bias for variance by adaptively winsorizing the importance sampling estimator. The novel winsorizing procedur…

Split Conformal Prediction and Non-Exchangeable Data Open

Roberto I. Oliveira, Paulo Orenstein, Thiago Ramos, João Vitor Romano · 2022

Split conformal prediction (CP) is arguably the most popular CP method for uncertainty quantification, enjoying both academic interest and widespread deployment. However, the original theoretical analysis of split CP makes the crucial assu…

Adaptive Bias Correction for Improved Subseasonal Forecasting Open

Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen , et al. · 2022

<p>Improving our ability to forecast the weather and climate is of interest to all sectors of the economy and government agencies from the local to the national level. In fact, weather forecasts 0-10 days ahead and climate forecasts …

SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking Open

Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen , et al. · 2021

Subseasonal forecasting of the weather two to six weeks in advance is critical for resource allocation and advance disaster notice but poses many challenges for the forecasting community. At this forecast horizon, physics-based dynamical m…

Learned Benchmarks for Subseasonal Forecasting. Open

Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen , et al. · 2021

We develop a subseasonal forecasting toolkit of simple learned benchmark models that outperform both operational practice and state-of-the-art machine learning and deep learning methods. Our new models include (a) Climatology++, an adaptiv…

Online Learning with Optimism and Delay Open

Genevieve Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu , et al. · 2021

Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback. Our algorithms -- DORM, DORM…

Replication Data for: Online Learning with Optimism and Delay Open

Genevieve Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu , et al. · 2021

The model forecasts for the sub-seasonal forecasting application considered in the Online Learning under Optimism and Delay paper experiments. This dataset consists of a single ZIP archive (919MB) that contains 1) a "models" folder that co…

Improving Subseasonal Forecasting in the Western U.S. with Machine Learning Open