Search Results for author: Anand Avati

Found 8 papers, 4 papers with code

BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark

1 code implementation17 Jul 2021 Anand Avati, Martin Seneviratne, Emily Xue, Zhen Xu, Balaji Lakshminarayanan, Andrew M. Dai

Most ML approaches focus on generalization performance on unseen data that are similar to the training data (In-Distribution, or IND).

Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models

no code implementations9 Oct 2020 Eric Zelikman, Sharon Zhou, Jeremy Irvin, Cooper Raterink, Hao Sheng, Anand Avati, Jack Kelly, Ram Rajagopal, Andrew Y. Ng, David Gagne

Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid.

CRUDE: Calibrating Regression Uncertainty Distributions Empirically

no code implementations26 May 2020 Eric Zelikman, Christopher Healy, Sharon Zhou, Anand Avati

Calibrated uncertainty estimates in machine learning are crucial to many fields such as autonomous vehicles, medicine, and weather and climate forecasting.

Autonomous Vehicles General Classification

NGBoost: Natural Gradient Boosting for Probabilistic Prediction

4 code implementations ICML 2020 Tony Duan, Anand Avati, Daisy Yi Ding, Khanh K. Thai, Sanjay Basu, Andrew Y. Ng, Alejandro Schuler

NGBoost generalizes gradient boosting to probabilistic regression by treating the parameters of the conditional distribution as targets for a multiparameter boosting algorithm.

Weather Forecasting

Predicting Inpatient Discharge Prioritization With Electronic Health Records

no code implementations2 Dec 2018 Anand Avati, Stephen Pfohl, Chris Lin, Thao Nguyen, Meng Zhang, Philip Hwang, Jessica Wetstone, Kenneth Jung, Andrew Ng, Nigam H. Shah

Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care.

Countdown Regression: Sharp and Calibrated Survival Predictions

1 code implementation21 Jun 2018 Anand Avati, Tony Duan, Sharon Zhou, Kenneth Jung, Nigam H. Shah, Andrew Ng

Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.

Decision Making Mortality Prediction +1

Improving Palliative Care with Deep Learning

no code implementations17 Nov 2017 Anand Avati, Kenneth Jung, Stephanie Harman, Lance Downing, Andrew Ng, Nigam H. Shah

The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care.

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