* denotes equal contribution
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,
address={New Orleans, LA, USA},
author={Yeh, Christopher and Christianson, Nicolas and Low, Steven and Wierman, Adam and Yue, Yisong},
booktitle={ICLR 2023 Workshop on Tackling Climate Change with Machine Learning},
month={5},
title={{Decision-aware uncertainty-calibrated deep learning for robust energy system operation}},
url={https://www.climatechange.ai/papers/iclr2023/61},
year={2023}
}
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,
address={New Orleans, LA, USA},
author={Yeh, Christopher and Li, Victor and Datta, Rajeev and Yue, Yisong and Wierman, Adam},
booktitle={NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning},
month={12},
title={{SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications}},
url={https://www.climatechange.ai/papers/neurips2022/38},
year={2022}
}
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,
address={New Orleans, LA, USA},
author={Christianson, Nicolas and Yeh, Christopher and Li, Tongxin and Torabi Rad, Mahdi and Golmohammadi, Azarang and Wierman, Adam},
booktitle={NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning},
month={12},
title={Robustifying machine-learned algorithms for efficient grid operation},
url={https://www.climatechange.ai/papers/neurips2022/19},
year={2022}
}
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,
author = {Christopher Yeh and Jing Yu and Yuanyuan Shi and Adam Wierman},
booktitle = {{Proceedings of the Thirteenth ACM International Conference on Future Energy Systems (e-Energy '22)}},
doi = {10.1145/3538637.3538853},
isbn = {9781450393973},
month = {6},
pages = {240-250},
publisher = {Association for Computing Machinery},
title = {Robust online voltage control with an unknown grid topology},
url = {https://dl.acm.org/doi/10.1145/3538637.3538853},
year = {2022}
}
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 Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), Dec. 2021. [Online]. Available: https://openreview.net/forum?id=5HR3vCylqD.
@inproceedings{yeh2021sustainbench,
author = {Christopher Yeh and Chenlin Meng and Sherrie Wang and Anne Driscoll and Erik Rozi and Patrick Liu and Jihyeon Lee and Marshall Burke and David B. Lobell and Stefano Ermon},
booktitle = {Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
doi = {10.48550/arXiv.2111.04724},
month = {12},
title = {{SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning}},
url = {https://openreview.net/forum?id=5HR3vCylqD},
year = {2021}
}
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,
author = {Yeh, Christopher and Perez, Anthony and Driscoll, Anne and Azzari, George and Tang, Zhongyi and Lobell, David and Ermon, Stefano and Burke, Marshall},
booktitle = {{Workshop on Machine Learning for Economic Policy at NeurIPS 2020}},
month = {12},
title = {{Deep learning for understanding economic well-being in Africa from publicly available satellite imagery}},
url = {http://www.mlforeconomicpolicy.com/papers/MLEconPolicy20_paper_30.pdf},
year = {2020}
}
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,
author = {Zhao, Shengjia and Yeh, Christopher, and Ermon, Stefano},
booktitle = {{The 23rd International Conference on Artificial Intelligence and Statistics}},
editor = {Chiappa, Silvia and Calandra, Roberto},
month = {8},
pages = {4583--4592},
pdf = {https://proceedings.mlr.press/v108/zhao20e/zhao20e.pdf},
publisher = {PMLR},
series = {Proceedings of Machine Learning Research},
title = {{A Framework for Sample Efficient Interval Estimation with Control Variates}},
url = {https://proceedings.mlr.press/v108/zhao20e.html},
volume = {108},
year = {2020}
}
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,
address = {Beijing, China},
author = {Yeh, Christopher},
month = {6},
school = {Schwarzman College, Tsinghua University},
title = {Quantifying the Value of Using Machine Learning to Improve Solar Irradiance Forecasting},
year = {2020}
}
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,
author = {Yeh, Christopher and Perez, Anthony and Driscoll, Anne and Azzari, George and Tang, Zhongyi and Lobell, David and Ermon, Stefano and Burke, Marshall},
day = {22},
doi = {10.1038/s41467-020-16185-w},
issn = {2041-1723},
journal = {{Nature Communications}},
month = {5},
number = {1},
title = {{Using publicly available satellite imagery and deep learning to understand economic well-being in Africa}},
url = {https://www.nature.com/articles/s41467-020-16185-w},
volume = {11},
year = {2020}
}
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,
author = {Coleman, Cody and Yeh, Christopher and Mussmann, Stephen and Mirzasoleiman, Baharan and Bailis, Peter and Liang, Percy and Leskovec, Jure and Zaharia, Matei},
booktitle = {{Practical Machine Learning for Developing Countries Workshop at ICLR 2020}},
month = {4},
title = {{Selection via Proxy: Increasing the Computational Efficiency of Deep Active Learning}},
url = {https://pml4dc.github.io/iclr2020/program/pml4dc_25.html},
year = {2020}
}
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,
author = {Coleman, Cody and Yeh, Christopher and Mussmann, Stephen and Mirzasoleiman, Baharan and Bailis, Peter and Liang, Percy and Leskovec, Jure and Zaharia, Matei},
booktitle = {{International Conference on Learning Representations}},
month = {4},
title = {{Selection via Proxy: Efficient Data Selection for Deep Learning}},
url = {https://openreview.net/forum?id=HJg2b0VYDr},
year = {2020}
}
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,
address = {Snowmass Village, CO, USA},
author = {Uzkent, Burak and Yeh, Christopher and Ermon, Stefano},
booktitle = {{2020 IEEE Winter Conference on Applications of Computer Vision (WACV)}},
doi = {10.1109/WACV45572.2020.9093447},
month = {3},
pages = {1813-1822},
title = {{Efficient Object Detection in Large Images using Deep Reinforcement Learning}},
url = {https://ieeexplore.ieee.org/document/9093447},
year = {2020}
}
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,
address = {Long Beach, CA, USA},
author = {Perez, Anthony and Yeh, Christopher and Azzari, George and Burke, Marshall and Lobell, David and Ermon, Stefano},
booktitle = {{NIPS 2017 Workshop on Machine Learning for the Developing World}},
day = {8},
doi = {10.48550/arXiv.1711.03654},
month = {12},
title = {{Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning}},
url = {https://arxiv.org/abs/1711.03654},
year = {2017}
}
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,
address = {Stanford, CA, USA},
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},
day = {20},
institution = {{Stanford Law School Law and Policy Lab}},
month = {6},
publisher = {{Stanford Law School}},
title = {{A Low-Cost Digital Licensing Platform for Photographs: Documentation for a Prototype}},
url = {https://law.stanford.edu/publications/a-low-cost-digital-licensing-platform-for-photographs-documentation-for-a-prototype/},
year = {2017}
}
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,
author = {Stevens, Matt and Yeh, Christopher},
institution = {{Stanford University}},
month = {6},
title = {{Reinforcement Learning for Traffic Optimization}},
url = {http://cs229.stanford.edu/proj2016spr/report/047.pdf},
year = {2016}
}