A. McGovern

发表

Roles of Macro-Actions in Accelerating Reinforcement Learning

Richard S. Sutton, Andrew H. Fagg, Amy McGovern, 1998 .

Macro-Actions in Reinforcement Learning: An Empirical Analysis

R. Sutton, A. McGovern, 1998 .

Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density

Andrew G. Barto, Amy McGovern, A. Barto, 2001, ICML.

Exploiting relational structure to understand publication patterns in high-energy physics

David D. Jensen, Andrew S. Fast, A. McGovern, 2003, SKDD.

Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts

Andrew G. Barto, J. Eliot B. Moss, Amy McGovern, 2002, Machine Learning.

Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather

S. E. Haupt, Travis M. Smith, A. McGovern, 2017 .

Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles

S. E. Haupt, A. McGovern, M. Xue, 2017 .

Statistical Machine Learning for Large-Scale Optimization

Kee-Eung Kim, Thomas G. Dietterich, Russell Impagliazzo, 2000 .

Solar Energy Prediction: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems

A. McGovern, Gilles Louppe, P. Prettenhofer, 2015 .

Accelerating Reinforcement Learning through the Discovery of Useful Subgoals

Andrew G. Barto, Amy McGovern, A. Barto, 2001 .

Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts

J. Eliot B. Moss, Amy McGovern, A. McGovern, 1998, NIPS.

Identifying Predictive Structures in Relational Data Using Multiple Instance Learning

David D. Jensen, Amy McGovern, A. McGovern, 2003, ICML.

Chi-squared: A simpler evaluation function for multiple-instance learning

David D. Jensen, A. McGovern, 2003 .

The Thing We Tried That Worked : Utile Distinctions for Relational Reinforcement Learning

Will Dabney, A. McGovern, 2006 .

Evaluation of statistical learning configurations for gridded solar irradiance forecasting

Sue Ellen Haupt, S. E. Haupt, Amy McGovern, 2017 .

Expert Move Prediction for Computer Go using Spatial Probability Trees

A. McGovern, Zack Tidwell, Scott Hellman, 2011 .

Imitation Learning in The Game of Go with Joseki Options

A. Barto, Will Dabney, A. McGovern, 2010 .

Climatology and Variability of Warm and Cold Fronts over North America from 1979 to 2018

A. McGovern, Ryan Lagerquist, J. Allen, 2020 .

University of Oklahoma Graduate College Learning Grasp Affordances Learning Grasp Affordances a Thesis Approved for the School of Computer Science Brian Watson for All of Their Help and Support. in Addition, I Would Also like to Thank

A. McGovern, A. Fagg, M. Diftler, 2008 .

Machine Learning for Real-Time Prediction of Damaging Straight-Line Convective Wind

Travis M. Smith, A. McGovern, Ryan Lagerquist, 2017 .

Multi-Modal Utile Distinctions

Will Dabney, A. McGovern, 2010 .

Categorizing Unsupervised Relational Learning Algorithms ∗

A. McGovern, Hannah Blau, 2003 .

UNIVERSITY OF OKLAHOMA GRADUATE COLLEGE AUTOMATICALLY IDENTIFYING STIMULI FROM FIRING PATTERNS IN THE AUDITORY CORTEX OF RAT

A. McGovern, A. Fagg, Robort R. Rennaker, 2012 .

Do you know DCOP ? Intelligent Workflows for Visual Sty-lometry

Ricky J. Sethi, A. McGovern, Eric Eaton, 2022 .

J 7 . 2 Winter Hydrometeor Classification using Polarimetric Radar and Spatiotemporal Relational Probability Trees

A. McGovern, A. Spencer, 2009 .

Machine Learning Enhancement of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts

Amy McGovern, David John Gagne, Ming Xue, 2014 .

Statistical Machine Learning for Large-Scale Optimization Contributors

Thomas G. Dietterich, Wray L. Buntine, R. Impagliazzo, 2022 .

University of Oklahoma Graduate College Predicting Arm Motion from Cortical Activity Predicting Arm Motion from Cortical Activity a Thesis Approved for the School of Computer Science Dedication Acknowledgments

A. McGovern, A. Fagg, N. Hatsopoulos, 2007 .

Movie Movie RelatesTo Movie RelatesTo Movie Movie Movie RelatesTo RelatesTo Movie RelatesTo Movie Movie Best Picture Award RelatesTo NominatedAwarded Best Picture

A. McGovern, 2007 .

A simpler evaluation function for multiple-instance learning

A. McGovern, 2003 .

Using Machine Learning to Calibrate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System pdf

Corey K. Potvin, Montgomery L. Flora, Amy McGovern, 2020, Monthly Weather Review.

Evaluating Knowledge to Support Climate Action: A Framework for Sustained Assessment. Report of an Independent Advisory Committee on Applied Climate Assessment

Andrew D. Jones, R. Moss, A. McGovern, 2019, Weather, Climate, and Society.

Policy Gradient vs . Value Function Approximation : A Reinforcement Learning Shootout

A. McGovern, J. Fager, Josh Beitelspacher, 2006 .

USING SPATIOTEMPORAL RELATIONAL DATA MINING TO IDENTIFY THE KEY PARAMETERS FOR ANTICIPATING ROTATION INITIATION IN SIMULATED SUPERCELL THUNDERSTORMS

A. McGovern, K. Droegemeier, Rodger A. Brown, 2008 .

University of Oklahoma Graduate College Automatic Recognition of Supercells Thunderstorms in Numerical Weather Data Automatic Recognition of Supercells Thunderstorms in Numerical Weather Data a Thesis Approved for the School of Computer Science

A. McGovern, Dean Frederick Hougen, Rodger A. Brown, 2022 .

Toward Understanding Tornado Formation Through Spatiotemporal Data Mining

A. McGovern, Rodger A. Brown, D. Rosendahl, 2014 .

Emerging Data Science and Machine Learning Opportunities in the Weather and Climate Sciences

Karthik Kashinath, Anuj Karpatne, Kevin A. Reed, 2018 .

Genus-wide analysis of Fusarium polyketide synthases reveals broad chemical potential.

A. McGovern, Daren W Brown, R. Proctor, 2022, Fungal genetics and biology : FG & B.

Real-Time and Climatological Storm Classification Using Machine Learning

Amy McGovern, A. McGovern, 2018 .

Global Extreme Heat Forecasting Using Neural Weather Models

A. McGovern, Jason Hickey, Shreya Agrawal, 2022, ArXiv.

A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning

A. McGovern, G. Lackmann, R. Chase, 2022, ArXiv.

Tornadic Supercell Environments Analyzed Using Surface and Reanalysis Data: A Spatiotemporal Relational Data-Mining Approach

Amy McGovern, David John Gagne, Jeffrey B. Basara, 2012 .

Challenges and benchmark datasets for machine learning in the atmospheric sciences: Definition, status and outlook

A. McGovern, D. Gagne, M. Schultz, 2022, Artificial Intelligence for the Earth Systems.

Tutorial on Machine Learning and Deep Learning for the Environmental and Geosciences

Karthik Kashinath, Prabhat, Anuj Karpatne, 2019 .

A Framework for Sustained Climate Assessment in the United States

R. Moss, A. McGovern, A. Waple, 2019, Bulletin of the American Meteorological Society.

A Machine Learning Tutorial for Operational Meteorology, Part I: Traditional Machine Learning

A. McGovern, G. Lackmann, R. Chase, 2022, ArXiv.

A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning

A. McGovern, G. Lackmann, R. Chase, 2022, ArXiv.

Proceedings of the 2011 workshop on Knowledge discovery, modeling and simulation

William Rand, David Jensen, David D. Jensen, 2011, KDD 2011.

Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction

Montgomery L. Flora, Shawn L. Handler, A. McGovern, 2022, 2211.10378.

Enhanced spatiotemporal relational probability trees and forests

A. McGovern, John K. Williams, Rodger A. Brown, 2013, Data Mining and Knowledge Discovery.

Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning

A. McGovern, D. Gagne, John K. Williams, 2013, Machine Learning.

Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement

Montgomery L. Flora, Shawn L. Handler, A. McGovern, 2022, ArXiv.

Global Extreme Heat Forecasting Using Neural Weather Models

A. McGovern, Jason Hickey, Shreya Agrawal, 2022, Artificial Intelligence for the Earth Systems.

Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning

Amy McGovern, Travis M. Smith, Ryan Lagerquist, 2019, Bulletin of the American Meteorological Society.

Utile Distinctions for Relational Reinforcement Learning

Amy McGovern, William Dabney, Will Dabney, 2007, IJCAI.

Deep Learning for Spatially Explicit Prediction of Synoptic-Scale Fronts

Amy McGovern, Ryan Lagerquist, David John Gagne, 2019, Weather and Forecasting.

Using Machine Learning to Calibrate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System

Montgomery L. Flora, Shawn L. Handler, A. McGovern, 2020, Monthly Weather Review.

Quasi-Operational Testing of Real-Time Storm-Longevity Prediction via Machine Learning

Amy McGovern, Christopher D. Karstens, Travis M. Smith, 2019, Weather and Forecasting.

Classifying Convective Storms Using Machine Learning

Amy McGovern, Travis M. Smith, Ryan Lagerquist, 2019, Weather and Forecasting.

Using spatiotemporal relational random forests to improve our understanding of severe weather processes

Nathaniel Troutman, John K. Williams, Amy McGovern, 2011, Stat. Anal. Data Min..

Machine learning enhancement of Storm Scale Ensemble precipitation forecasts

Amy McGovern, David John Gagne, Ming Xue, 2011, 2012 Conference on Intelligent Data Understanding.

Valliappa Lakshmanan · Eric Gilleland Amy McGovern · Martin Tingley Editors

A. McGovern, J. Heitzig, N. Marwan, 2015 .

CREST-iMAP v1.0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction

Yang Hong, Amy McGovern, Jonathan J. Gourley, 2021, Environ. Model. Softw..

Machine learning in space: extending our reach

Kiri Wagstaff, Amy McGovern, A. McGovern, 2011, Machine Learning.

Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences

S. E. Haupt, K. Casey, A. McGovern, 2020, Bulletin of the American Meteorological Society.

Deep Learning on Three-Dimensional Multiscale Data for Next-Hour Tornado Prediction

Amy McGovern, Travis M. Smith, Ryan Lagerquist, 2020, Monthly Weather Review.

AI education matters: a first introduction to modeling and learning using the data science workflow

Katherine Guo, Georgy Noarov, Michael, 2019, SIGAI.

Automated detection of bird roosts using NEXRAD radar data and Convolutional Neural Networks

Amy McGovern, A. McGovern, D. Sheldon, 2018, Remote Sensing in Ecology and Conservation.

Creating significant learning experiences in introductory artificial intelligence

Amy McGovern, Jason Fager, A. McGovern, 2007, SIGCSE.

Development of a Probabilistic Subfreezing Road Temperature Nowcast and Forecast Using Machine Learning

Amy McGovern, Heather D. Reeves, Shawn L. Handler, 2020, Weather and Forecasting.

Learning ensembles of Continuous Bayesian Networks: An application to rainfall prediction

Ming Xue, Scott Hellman, Amy McGovern, 2012, 2012 Conference on Intelligent Data Understanding.

Storm Evader: Using an iPad to Teach Kids about Meteorology and Technology

A. McGovern, David R. Harrison, A. Balfour, 2015 .

Postprocessing Next-Day Ensemble Probabilistic Precipitation Forecasts Using Random Forests

Montgomery L. Flora, Adam J. Clark, Amy McGovern, 2019, Weather and Forecasting.

AcQuire-macros: An Algorithm for Automatically Learning Macro-actions

Amy McGovern, A. McGovern, 1998 .

A Review of Machine Learning for Convective Weather

A. McGovern, D. Gagne, Ryan Lagerquist, 2023, Artificial Intelligence for the Earth Systems.

Open problem: Dynamic Relational Models for Improved Hazardous Weather Prediction

Amy McGovern, Kelvin Droegemeier, A. McGovern, 2006 .

Spatiotemporal Relational Random Forests

John K. Williams, Amy McGovern, Timothy A. Supinie, 2009, 2009 IEEE International Conference on Data Mining Workshops.

Supporting Transparent Thread Assignment in Heterogeneous Multicore Processors Using Reinforcement Learning

R. D. Barnes, A. McGovern, L. Sawalha, 2012 .

Machine Learning and Data Mining Approaches to Climate Science: Proceedings of the 4th International Workshop on Climate Informatics

Eric Gilleland, Amy McGovern, Valliappa Lakshmanan, 2015 .

Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction

Amy McGovern, Rodger A. Brown, Kelvin Droegemeier, 2010, Data Mining and Knowledge Discovery.

An Automated, Multiparameter Dryline Identification Algorithm

Adam J. Clark, Amy McGovern, Valliappa Lakshmanan, 2015 .

Welcome to AI matters 4(3)

Amy McGovern, A. McGovern, 2018, SIGAI.

Welcome to AI matters 4(4)

Iolanda Leite, Amy McGovern, A. McGovern, 2019, AI Matters.

Mobile agents on the digital battlefield

Amy McGovern, Martin O. Hofmann, Kenneth R. Whitebread, 1998, AGENTS '98.

A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning

A. McGovern, G. Lackmann, R. Chase, 2022, Weather and forecasting.

Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction

A. McGovern, K. Droegemeier, Rodger A. Brown, 2010, Data Mining and Knowledge Discovery.

Toward Operational Real-time Identification of Frontal Boundaries Using Machine Learning

A. McGovern, J. Allen, Andrew D. Justin, 2023, Artificial Intelligence for the Earth Systems.

Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science pdf

Imme Ebert-Uphoff, Amy McGovern, Ann Bostrom, 2021, Environmental Data Science.

Trustworthy Artificial Intelligence for Environmental Sciences: An Innovative Approach for Summer School

A. McGovern, I. Ebert‐Uphoff, D. Gagne, 2023, Bulletin of The American Meteorological Society - (BAMS).

Comparing and Interpreting Differently-Designed Random Forests for Next-Day Severe Weather Hazard Prediction

A. McGovern, A. Clark, Eric D. Loken, 2022, Weather and Forecasting.

Deep learning and a changing economy in weather and climate prediction

A. McGovern, B. Stevens, F. Doblas-Reyes, 2023, Nature Reviews Earth & Environment.

Weathering Environmental Change Through Advances in AI

Julie L. Demuth, A. McGovern, Jason Hickey, 2020 .

Using Large-Scale Machine Learning to Improve Our Understanding of the Formation of Tornadoes

Corey K. Potvin, Amy McGovern, Rodger A. Brown, 2017 .

Severe Weather Processes through Spatiotemporal Relational Random Forests

Nathaniel Troutman, John K. Williams, Amy McGovern, 2010, CIDU.

NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES)

A. McGovern, J. Demuth, David John Gagne II, 2022, Bulletin of the American Meteorological Society.

Genus-wide analysis of Fusarium polyketide synthases reveals broad chemical potential.

A. McGovern, R. Proctor, Hye-Seon Kim, 2022, Fungal genetics and biology : FG & B.

Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather

S. E. Haupt, Travis M. Smith, A. McGovern, 2017 .

Machine Learning Enhancement of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts

A. McGovern, M. Xue, D. Gagne, 2014 .

Calibration of Machine Learning–Based Probabilistic Hail Predictions for Operational Forecasting

Amy McGovern, David John Gagne, Amanda Burke, 2020, Weather and Forecasting.

Verification of Hail Forecasts Produced by Machine Learning Algorithms

Amy McGovern, Nathan Snook, Sarah McCorkle, 2019 .

Classification of Convective Areas Using Decision Trees

Amy McGovern, David John Gagne, Jerry Brotzge, 2009 .

SOLAR ENERGY PREDICTION An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems

A. McGovern, Gilles Louppe, P. Prettenhofer, 2015 .

Severe Hail Prediction within a Spatiotemporal Relational Data Mining Framework

Ming Xue, Amy McGovern, David John Gagne, 2013, 2013 IEEE 13th International Conference on Data Mining Workshops.

Day-Ahead Hail Prediction Integrating Machine Learning with Storm-Scale Numerical Weather Models

James Correia, Ming Xue, Amy McGovern, 2015, AAAI.

Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning

John K. Williams, Amy McGovern, David John Gagne, 2013, Machine Learning.