* denotes equal contribution
Robust machine-learned algorithms for efficient grid operation
N. Christianson, C. Yeh, T. Li, M. Hosseini, M. Torabi Rad, A. Golmohammadi, A. Wierman
Environmental Data Science, April 2025
N. Christianson, C. Yeh, T. Li, M. Hosseini, M. Torabi Rad, A. Golmohammadi, and A. Wierman, “Robust machine-learned algorithms for efficient grid operation,” Environmental Data Science, vol. 4, p. e24, 2025. DOI: 10.1017/eds.2024.28. [Online]. Available: https://www.cambridge.org/core/journals/environmental-data-science/article/robust-machinelearned-algorithms-for-efficient-grid-operation/0E29B936A8FEE565B169F70372B7F9DE.
@article{christianson2025robust,
title = {Robust machine-learned algorithms for efficient grid operation},
author = {Christianson, Nicolas and Yeh, Christopher and Li, Tongxin and Hosseini, Mehdi and Torabi Rad, Mahdi and Golmohammadi, Azarang and Wierman, Adam},
year = 2025,
journal = {Environmental Data Science},
volume = 4,
pages = {e24},
doi = {10.1017/eds.2024.28},
url = {https://www.cambridge.org/core/journals/environmental-data-science/article/robust-machinelearned-algorithms-for-efficient-grid-operation/0E29B936A8FEE565B169F70372B7F9DE}
}
End-to-End Conformal Calibration for Optimization Under Uncertainty
C. Yeh*, N. Christianson*, A. Wu, A. Wierman, Y. Yue
Preprint
C. Yeh, N. Christianson, A. Wu, A. Wierman, and Y. Yue, End-to-end conformal calibration for optimization under uncertainty, 2024. DOI: 10.48550/arXiv.2409.20534. [Online]. Available: https://arxiv.org/abs/2409.20534.
@misc{yeh2024endtoendarxiv,
title = {End-to-End Conformal Calibration for Optimization Under Uncertainty},
author = {Yeh, Christopher and Christianson, Nicolas and Wu, Alan and Wierman, Adam and Yue, Yisong},
year = 2024,
doi = {10.48550/arXiv.2409.20534},
url = {https://arxiv.org/abs/2409.20534}
}
End-to-End Conformal Calibration for Robust Grid-Scale Battery Storage Optimization
C. Yeh*, N. Christianson*, A. Wierman, Y. Yue
NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning
C. Yeh, N. Christianson, A. Wierman, and Y. Yue, “End-to-end conformal calibration for robust grid-scale battery storage optimization,” in NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning, Vancouver, Canada, Dec. 2024. [Online]. Available: https://www.climatechange.ai/papers/neurips2024/18.
@inproceedings{yeh2024endtoend,
title = {End-to-End Conformal Calibration for Robust Grid-Scale Battery Storage Optimization},
author = {Christopher Yeh and Nicolas Christianson and Adam Wierman and Yisong Yue},
year = 2024,
month = 12,
booktitle = {NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning},
address = {Vancouver, Canada},
url = {https://www.climatechange.ai/papers/neurips2024/18}
}
Online learning for robust voltage control under uncertain grid topology
C. Yeh, J. Yu, Y. Shi, A. Wierman
IEEE Transactions on Smart Grid, September 2024
C. Yeh, J. Yu, Y. Shi, and A. Wierman, “Online learning for robust voltage control under uncertain grid topology,” IEEE Transactions on Smart Grid, vol. 15, no. 5, pp. 4754-4764, Sep. 2024, ISSN: 1949-3061. DOI: 10.1109/TSG.2024.3383804. [Online]. Available: https://ieeexplore.ieee.org/document/10486962.
@article{yeh2024online,
title = {Online Learning for Robust Voltage Control Under Uncertain Grid Topology},
author = {Yeh, Christopher and Yu, Jing and Shi, Yuanyuan and Wierman, Adam},
year = 2024,
month = 9,
journal = {IEEE Transactions on Smart Grid},
volume = 15,
number = 5,
pages = {4754--4764},
doi = {10.1109/TSG.2024.3383804},
issn = {1949-3061},
url = {https://ieeexplore.ieee.org/document/10486962}
}
SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems
C. Yeh, V. Li, R. Datta, J. Arroyo, N. Christianson, C. Zhang, Y. Chen, M. Hosseini, A. Golmohammadi, Y. Shi, Y. Yue, and A. Wierman
NeurIPS 2023 Datasets and Benchmarks Track
Project Page Code PDF Poster Cite
C. Yeh, V. Li, R. Datta, J. Arroyo, N. Christianson, C. Zhang, Y. Chen, M. Hosseini, A. Golmohammadi, Y. Shi, Y. Yue, and A. Wierman, “SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications,” in Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, New Orleans, LA, USA, Dec. 2023. [Online]. Available: https://openreview.net/forum?id=vZ9tA3o3hr.
@inproceedings{yeh2023sustaingym,
title = {{SustainGym}: {Reinforcement} Learning Environments for Sustainable Energy Systems},
author = {Yeh, Christopher and Li, Victor and Datta, Rajeev and Arroyo, Julio and Christianson, Nicolas and Zhang, Chi and Chen, Yize and Hosseini, Mehdi and Golmohammadi, Azarang and Shi, Yuanyuan and Yue, Yisong and Wierman, Adam},
year = 2023,
month = 12,
booktitle = {Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
address = {New Orleans, LA, USA},
url = {https://openreview.net/forum?id=vZ9tA3o3hr}
}
Decision-aware uncertainty-calibrated deep learning for robust energy system operation
C. Yeh, N. Christianson, S. Low, A. Wierman, Y. Yue
ICLR 2023 Workshop on Tackling Climate Change with Machine Learning
C. Yeh, N. Christianson, S. Low, A. Wierman, and Y. Yue, “Decision-aware uncertainty-calibrated deep learning for robust energy system operation,” in ICLR 2023 Workshop on Tackling Climate Change with Machine Learning, May 2023. [Online]. Available: https://www.climatechange.ai/papers/iclr2023/61.
@inproceedings{yeh2023decisionaware,
title = {Decision-aware uncertainty-calibrated deep learning for robust energy system operation},
author = {Yeh, Christopher and Christianson, Nicolas and Low, Steven and Wierman, Adam and Yue, Yisong},
year = 2023,
month = 5,
booktitle = {ICLR 2023 Workshop on Tackling Climate Change with Machine Learning},
address = {Kigali, Rwanda},
url = {https://www.climatechange.ai/papers/iclr2023/61}
}
SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications
C. Yeh, V. Li, R. Datta, Y. Yue, and A. Wierman
NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning
PDF Slides Presentation Project Page Code Cite
C. Yeh, V. Li, R. Datta, Y. Yue, and A. Wierman, “SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications,” in NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, Dec. 2022. [Online]. Available: https://www.climatechange.ai/papers/neurips2022/38.
@inproceedings{yeh2022sustaingym,
title = {{SustainGym}: A Benchmark Suite of Reinforcement Learning for Sustainability Applications},
author = {Yeh, Christopher and Li, Victor and Datta, Rajeev and Yue, Yisong and Wierman, Adam},
year = 2022,
month = 12,
booktitle = {NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning},
address = {New Orleans, LA, USA},
url = {https://www.climatechange.ai/papers/neurips2022/38}
}
Robustifying machine-learned algorithms for efficient grid operation
N. Christianson, C. Yeh, T. Li, M. Torabi Rad, A. Golmohammadi, and A. Wierman
NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning
N. Christianson, C. Yeh, T. Li, M. Torabi Rad, A. Golmohammadi, and A. Wierman, “Robustifying machine-learned algorithms for efficient grid operation,” in NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning, Dec. 2022. [Online]. Available: https://www.climatechange.ai/papers/neurips2022/19.
@inproceedings{christianson2022robustifying,
title = {Robustifying machine-learned algorithms for efficient grid operation},
author = {Christianson, Nicolas and Yeh, Christopher and Li, Tongxin and Torabi Rad, Mahdi and Golmohammadi, Azarang and Wierman, Adam},
year = 2022,
month = 12,
booktitle = {NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning},
address = {New Orleans, LA, USA},
url = {https://www.climatechange.ai/papers/neurips2022/19}
}
Robust online voltage control with an unknown grid topology
C. Yeh, J. Yu, Y. Shi, and A. Wierman
ACM e-Energy 2022, Best paper award finalist
PDF Slides arXiv Presentation Code Cite
C. Yeh, J. Yu, Y. Shi, and A. Wierman, “Robust online voltage control with an unknown grid topology,” in Proceedings of the Thirteenth ACM International Conference on Future Energy Systems (e-Energy ‘22), Association for Computing Machinery, Jun. 2022, pp. 240–250, ISBN: 9781450393973. DOI: 10.1145/3538637.3538853. [Online]. Available: https://dl.acm.org/doi/10.1145/3538637.3538853.
@inproceedings{yeh2022robust,
title = {Robust online voltage control with an unknown grid topology},
author = {Christopher Yeh and Jing Yu and Yuanyuan Shi and Adam Wierman},
year = 2022,
month = 6,
booktitle = {Proceedings of the Thirteenth ACM International Conference on Future Energy Systems},
publisher = {Association for Computing Machinery},
pages = {240--250},
doi = {10.1145/3538637.3538853},
isbn = 9781450393973,
url = {https://dl.acm.org/doi/10.1145/3538637.3538853}
}
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
C. Yeh*, C. Meng*, S. Wang*, A. Driscoll, E. Rozi, P. Liu, J. Lee, M. Burke, D. B. Lobell, and S. Ermon
NeurIPS 2021, Datasets and Benchmarks Track (Round 2)
PDF Poster arXiv Presentation Project Page Code Cite
C. Yeh, C. Meng, S. Wang, A. Driscoll, E. Rozi, P. Liu, J. Lee, M. Burke, D. B. Lobell, and S. Ermon, “SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, Dec. 2021. [Online]. Available: https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/hash/950a4152c2b4aa3ad78bdd6b366cc179-Abstract-round2.html.
@inproceedings{yeh2021sustainbench,
title = {SustainBench: Benchmarks for Monitoring the {Sustainable Development Goals} with Machine Learning},
author = {Yeh, Christopher and Meng, Chenlin and Wang, Sherrie and Driscoll, Anne and Rozi, Erik and Liu, Patrick and Lee, Jihyeon and Burke, Marshall and Lobell, David and Ermon, Stefano},
year = 2021,
month = 12,
booktitle = {Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks},
publisher = {Curran},
volume = 1,
doi = {10.48550/arXiv.2111.04724},
url = {https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/hash/950a4152c2b4aa3ad78bdd6b366cc179-Abstract-round2.html},
editor = {J. Vanschoren and S. Yeung}
}
Deep learning for understanding economic well-being in Africa from publicly available satellite imagery
C. Yeh, A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. Burke
NeurIPS 2021, Workshop on Machine Learning for Economic Policy
C. Yeh, A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. Burke, “Deep learning for understanding economic well-being in Africa from publicly available satellite imagery,” in Workshop on Machine Learning for Economic Policy at NeurIPS 2020, Dec. 2020. [Online]. Available: http://www.mlforeconomicpolicy.com/papers/MLEconPolicy20_paper_30.pdf.
@inproceedings{yeh2020deep,
title = {Deep learning for understanding economic well-being in {Africa} from publicly available satellite imagery},
author = {Yeh, Christopher and Perez, Anthony and Driscoll, Anne and Azzari, George and Tang, Zhongyi and Lobell, David and Ermon, Stefano and Burke, Marshall},
year = 2020,
month = 12,
booktitle = {Workshop on Machine Learning for Economic Policy at NeurIPS 2020},
url = {http://www.mlforeconomicpolicy.com/papers/MLEconPolicy20_paper_30.pdf}
}
A Framework for Sample Efficient Interval Estimation with Control Variates
S. Zhao, C. Yeh, and S. Ermon
AISTATS 2020
PDF Appendix PDF arXiv Presentation Cite
S. Zhao, C. Yeh, and S. Ermon, “A Framework for Sample Efficient Interval Estimation with Control Variates,” in The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), S. Chiappa and R. Calandra, Eds., ser. Proceedings of Machine Learning Research, vol. 108, PMLR, Aug. 2020, pp. 4583-4592. [Online]. Available: https://proceedings.mlr.press/v108/zhao20e.html.
@inproceedings{zhao2020interval,
title = {A Framework for Sample Efficient Interval Estimation with Control Variates},
author = {Zhao, Shengjia and Yeh, Christopher and Ermon, Stefano},
year = 2020,
month = 8,
booktitle = {The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
volume = 108,
pages = {4583--4592},
url = {https://proceedings.mlr.press/v108/zhao20e.html},
editor = {Silvia Chiappa and Roberto Calandra},
pdf = {http://proceedings.mlr.press/v108/zhao20e/zhao20e.pdf}
}
Quantifying the value of using machine learning to improve solar irradiance forecasting
C. Yeh
Master’s thesis, Schwarzman College, Tsinghua University, 2020
C. Yeh, “Quantifying the value of using machine learning to improve solar irradiance forecasting,” Master’s thesis, Schwarzman College, Tsinghua University, Beijing, China, Jun. 2020.
@mastersthesis{yeh2020quantifying,
title = {Quantifying the Value of Using Machine Learning to Improve Solar Irradiance Forecasting},
author = {Yeh, Christopher},
year = 2020,
month = 6,
address = {Beijing, China},
school = {Schwarzman College, Tsinghua University}
}
Using publicly available satellite imagery and deep learning to understand economic well-being in Africa
C. Yeh*, A. Perez*, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. Burke
Nature Communications, May 2020
PDF Supplementary PDF Nature Communications Stanford News Code Cite
C. Yeh, A. Perez, A. Driscoll, G. Azzari, Z. Tang, D. Lobell, S. Ermon, and M. Burke, “Using publicly available satellite imagery and deep learning to understand economic well-being in Africa,” Nature Communications, vol. 11, no. 1, May 2020, ISSN: 2041-1723. DOI: 10.1038/s41467-020-16185-w. [Online]. Available: https://www.nature.com/articles/s41467-020-16185-w.
@article{yeh2020using,
title = {Using publicly available satellite imagery and deep learning to understand economic well-being in {Africa}},
author = {Yeh, Christopher and Perez, Anthony and Driscoll, Anne and Azzari, George and Tang, Zhongyi and Lobell, David and Ermon, Stefano and Burke, Marshall},
year = 2020,
month = 5,
day = 22,
journal = {{Nature Communications}},
volume = 11,
number = 1,
doi = {10.1038/s41467-020-16185-w},
issn = {2041-1723},
url = {https://www.nature.com/articles/s41467-020-16185-w}
}
Selection via Proxy: Increasing the Computational Efficiency of Deep Active Learning
C. Coleman, C. Yeh, S. Mussmann, B. Mirzasoleiman, P. Bailis, P. Liang, J. Leskovec, and M. Zaharia
ICLR 2020, Practical Machine Learning for Developing Countries Workshop
C. Coleman, C. Yeh, S. Mussmann, B. Mirzasoleiman, P. Bailis, P. Liang, J. Leskovec, and M. Zaharia, “Selection via Proxy: Increasing the Computational Efficiency of Deep Active Learning,” in Practical Machine Learning for Developing Countries Workshop at ICLR 2020, Apr. 2020. [Online]. Available: https://pml4dc.github.io/iclr2020/program/pml4dc_25.html.
@inproceedings{coleman2020active,
title = {Selection via Proxy: Increasing the Computational Efficiency of Deep Active Learning},
author = {Coleman, Cody and Yeh, Christopher and Mussmann, Stephen and Mirzasoleiman, Baharan and Bailis, Peter and Liang, Percy and Leskovec, Jure and Zaharia, Matei},
year = 2020,
month = 4,
booktitle = {{Practical Machine Learning for Developing Countries Workshop at ICLR 2020}},
url = {https://pml4dc.github.io/iclr2020/program/pml4dc_25.html}
}
Selection via Proxy: Efficient Data Selection for Deep Learning
C. Coleman, C. Yeh, S. Mussmann, B. Mirzasoleiman, P. Bailis, P. Liang, J. Leskovec, and M. Zaharia
ICLR 2020
PDF Slides arXiv Presentation Blog Post Code Cite
C. Coleman, C. Yeh, S. Mussmann, B. Mirzasoleiman, P. Bailis, P. Liang, J. Leskovec, and M. Zaharia, “Selection via Proxy: Efficient Data Selection for Deep Learning,” in International Conference on Learning Representations, Apr. 2020. [Online]. Available: https://openreview.net/forum?id=HJg2b0VYDr.
@inproceedings{coleman2020selection,
title = {Selection via Proxy: Efficient Data Selection for Deep Learning},
author = {Coleman, Cody and Yeh, Christopher and Mussmann, Stephen and Mirzasoleiman, Baharan and Bailis, Peter and Liang, Percy and Leskovec, Jure and Zaharia, Matei},
year = 2020,
month = 4,
booktitle = {{International Conference on Learning Representations}},
url = {https://openreview.net/forum?id=HJg2b0VYDr}
}
Efficient Object Detection in Large Images using Deep Reinforcement Learning
B. Uzkent, C. Yeh, and S. Ermon
IEEE WACV 2020
PDF Poster Presentation Code Cite
B. Uzkent, C. Yeh, and S. Ermon, “Efficient Object Detection in Large Images using Deep Reinforcement Learning,” in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), Snowmass Village, CO, USA, Mar. 2020, pp. 1813–1822. DOI: 10.1109/WACV45572.2020.9093447. [Online]. Available: https://ieeexplore.ieee.org/document/9093447.
@inproceedings{uzkent2020efficient,
title = {Efficient Object Detection in Large Images using Deep Reinforcement Learning},
author = {Uzkent, Burak and Yeh, Christopher and Ermon, Stefano},
year = 2020,
month = 3,
booktitle = {{2020 IEEE Winter Conference on Applications of Computer Vision (WACV)}},
address = {Snowmass Village, CO, USA},
pages = {1813--1822},
doi = {10.1109/WACV45572.2020.9093447},
url = {https://ieeexplore.ieee.org/document/9093447}
}
Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
A. Perez, C. Yeh, G. Azzari, M. Burke, D. Lobell, and S. Ermon
NIPS 2017, Workshop on Machine Learning for the Developing World
A. Perez, C. Yeh, G. Azzari, M. Burke, D. Lobell, and S. Ermon, “Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning,” in NIPS 2017 Workshop on Machine Learning for the Developing World, Long Beach, CA, USA, Dec. 2017. arXiv:1711.03654. [Online]. Available: https://arxiv.org/abs/1711.03654.
@inproceedings{perez2017poverty,
title = {{Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning}},
author = {Perez, Anthony and Yeh, Christopher and Azzari, George and Burke, Marshall and Lobell, David and Ermon, Stefano},
year = 2017,
month = 12,
day = 8,
booktitle = {{NIPS 2017 Workshop on Machine Learning for the Developing World}},
address = {Long Beach, CA, USA},
doi = {10.48550/arXiv.1711.03654},
url = {https://arxiv.org/abs/1711.03654}
}
A Low-Cost Digital Licensing Platform for Photographs: Documentation for a Prototype
A. Itai, S. Yadav, W. Zhong, L. Zhu, C. Yeh, E. Shayer, R. Barcelo, T. Liu, H. Stoyanov, P. Goldstein, L. Herman, and A. Terra
Stanford Law School Law and Policy Lab, 2017
A. Itai, S. Yadav, W. Zhong, L. Zhu, C. Yeh, E. Shayer, R. Barcelo, T. Liu, H. Stoyanov, P. Goldstein, L. Herman, and A. Terra, “A Low-Cost Digital Licensing Platform for Photographs: Documentation for a Prototype,” Stanford Law School Law and Policy Lab, Stanford, CA, USA, Tech. Rep., Jun. 2017. [Online]. Available: https://law.stanford.edu/publications/a-low-cost-digital-licensing-platform-for-photographs-documentation-for-a-prototype/.
@techreport{itai2017copyright,
title = {A Low-Cost Digital Licensing Platform for Photographs: Documentation for a Prototype},
author = {Itai, Amit and Yadav, Sahil and Zhong, Weili and Zhu, Li and Yeh, Christopher and Shayer, Eli and Barcelo, Rey and Liu, Thomas and Stoyanov, Hristo and Goldstein, Paul and Herman, Luciana and Terra, Antoni},
year = 2017,
month = 6,
day = 20,
publisher = {{Stanford Law School}},
address = {Stanford, CA, USA},
url = {https://law.stanford.edu/publications/a-low-cost-digital-licensing-platform-for-photographs-documentation-for-a-prototype/},
institution = {{Stanford Law School Law and Policy Lab}}
}
Reinforcement Learning for Traffic Optimization
M. Stevens* and C. Yeh*
Stanford CS 229 Final Report, 2016
M. Stevens and C. Yeh, “Reinforcement Learning for Traffic Optimization,” Stanford University, Tech. Rep., Jun. 2016. [Online]. Available: http://cs229.stanford.edu/proj2016spr/report/047.pdf.
@techreport{stevens2016traffic,
title = {Reinforcement Learning for Traffic Optimization},
author = {Stevens, Matt and Yeh, Christopher},
year = 2016,
month = 6,
url = {http://cs229.stanford.edu/proj2016spr/report/047.pdf},
institution = {{Stanford University}}
}