206 lines
8.1 KiB
BibTeX
206 lines
8.1 KiB
BibTeX
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@article{scikit-learn,
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title={Scikit-learn: Machine Learning in {P}ython},
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author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
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and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
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and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
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Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
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journal={Journal of Machine Learning Research},
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volume={12},
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pages={2825--2830},
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year={2011}
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}
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@inproceedings{catboost,
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author = {Prokhorenkova, Liudmila and Gusev, Gleb and Vorobev, Aleksandr and Dorogush, Anna Veronika and Gulin, Andrey},
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title = {CatBoost: Unbiased Boosting with Categorical Features},
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year = {2018},
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publisher = {Curran Associates Inc.},
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address = {Red Hook, NY, USA},
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abstract = {This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.},
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booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems},
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pages = {6639–6649},
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numpages = {11},
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location = {Montr\'{e}al, Canada},
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series = {NIPS'18}
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}
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@article{lightgbm,
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title={Lightgbm: A highly efficient gradient boosting decision tree},
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author={Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
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journal={Advances in neural information processing systems},
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volume={30},
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pages={3146--3154},
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year={2017}
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}
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@inproceedings{xgboost,
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author = {Chen, Tianqi and Guestrin, Carlos},
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title = {{XGBoost}: A Scalable Tree Boosting System},
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booktitle = {Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
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series = {KDD '16},
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year = {2016},
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isbn = {978-1-4503-4232-2},
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location = {San Francisco, California, USA},
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pages = {785--794},
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numpages = {10},
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url = {http://doi.acm.org/10.1145/2939672.2939785},
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doi = {10.1145/2939672.2939785},
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acmid = {2939785},
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publisher = {ACM},
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address = {New York, NY, USA},
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keywords = {large-scale machine learning},
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}
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@article{stable-baselines3,
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author = {Antonin Raffin and Ashley Hill and Adam Gleave and Anssi Kanervisto and Maximilian Ernestus and Noah Dormann},
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title = {Stable-Baselines3: Reliable Reinforcement Learning Implementations},
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journal = {Journal of Machine Learning Research},
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year = {2021},
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volume = {22},
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number = {268},
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pages = {1-8},
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url = {http://jmlr.org/papers/v22/20-1364.html}
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}
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@misc{openai,
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title={OpenAI Gym},
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author={Greg Brockman and Vicki Cheung and Ludwig Pettersson and Jonas Schneider and John Schulman and Jie Tang and Wojciech Zaremba},
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year={2016},
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eprint={1606.01540},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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@misc{tensorflow,
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title={ {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
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url={https://www.tensorflow.org/},
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note={Software available from tensorflow.org},
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author={
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Mart\'{i}n~Abadi and
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Ashish~Agarwal and
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Paul~Barham and
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Eugene~Brevdo and
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Zhifeng~Chen and
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Craig~Citro and
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Greg~S.~Corrado and
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Andy~Davis and
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Jeffrey~Dean and
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Matthieu~Devin and
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Sanjay~Ghemawat and
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Ian~Goodfellow and
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Andrew~Harp and
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Geoffrey~Irving and
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Michael~Isard and
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Yangqing Jia and
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Rafal~Jozefowicz and
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Lukasz~Kaiser and
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Manjunath~Kudlur and
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Josh~Levenberg and
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Dandelion~Man\'{e} and
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Rajat~Monga and
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Sherry~Moore and
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Derek~Murray and
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Chris~Olah and
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Mike~Schuster and
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Jonathon~Shlens and
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Benoit~Steiner and
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Ilya~Sutskever and
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Kunal~Talwar and
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Paul~Tucker and
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Vincent~Vanhoucke and
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Vijay~Vasudevan and
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Fernanda~Vi\'{e}gas and
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Oriol~Vinyals and
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Pete~Warden and
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Martin~Wattenberg and
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Martin~Wicke and
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Yuan~Yu and
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Xiaoqiang~Zheng},
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year={2015},
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}
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@incollection{pytorch,
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title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
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author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
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booktitle = {Advances in Neural Information Processing Systems 32},
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editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
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pages = {8024--8035},
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year = {2019},
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publisher = {Curran Associates, Inc.},
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url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
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}
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@ARTICLE{scipy,
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author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
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Haberland, Matt and Reddy, Tyler and Cournapeau, David and
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Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
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Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
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Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
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Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
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Kern, Robert and Larson, Eric and Carey, C J and
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Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
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{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
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Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
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Harris, Charles R. and Archibald, Anne M. and
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Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
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{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
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title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
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Computing in Python}},
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journal = {Nature Methods},
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year = {2020},
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volume = {17},
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pages = {261--272},
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adsurl = {https://rdcu.be/b08Wh},
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doi = {10.1038/s41592-019-0686-2},
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}
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@Article{numpy,
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title = {Array programming with {NumPy}},
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author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
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van der Walt and Ralf Gommers and Pauli Virtanen and David
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Cournapeau and Eric Wieser and Julian Taylor and Sebastian
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Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
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and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
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Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
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R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
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G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
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Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
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Travis E. Oliphant},
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year = {2020},
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month = sep,
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journal = {Nature},
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volume = {585},
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number = {7825},
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pages = {357--362},
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doi = {10.1038/s41586-020-2649-2},
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publisher = {Springer Science and Business Media {LLC}},
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url = {https://doi.org/10.1038/s41586-020-2649-2}
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}
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@inproceedings{pandas,
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title={Data structures for statistical computing in python},
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author={McKinney, Wes and others},
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booktitle={Proceedings of the 9th Python in Science Conference},
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volume={445},
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pages={51--56},
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year={2010},
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organization={Austin, TX}
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}
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@online{finrl,
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title = {AI4Finance-Foundation},
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year = 2022,
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url = {https://github.com/AI4Finance-Foundation/FinRL},
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urldate = {2022-09-30}
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}
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@online{tensortrade,
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title = {tensortrade},
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year = 2022,
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url = {https://tensortradex.readthedocs.io/en/latest/L},
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urldate = {2022-09-30}
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}
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