add references.bib

paper/references.bib

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+@article{Pearson:2017,
+	url = {http://adsabs.harvard.edu/abs/2017arXiv170304627P},
+	Archiveprefix = {arXiv},
+	Author = {{Pearson}, S. and {Price-Whelan}, A.~M. and {Johnston}, K.~V.},
+	Eprint = {1703.04627},
+	Journal = {ArXiv e-prints},
+	Keywords = {Astrophysics - Astrophysics of Galaxies},
+	Month = mar,
+	Title = {{Gaps in Globular Cluster Streams: Pal 5 and the Galactic Bar}},
+	Year = 2017
+}
+
+@inproceedings{watson2023,
+	title={Adversarial random forests for density estimation and generative modeling},
+	author={Watson, David S and Blesch, Kristin and Kapar, Jan and Wright, Marvin N},
+	booktitle={International Conference on Artificial Intelligence and Statistics},
+	pages={5357--5375},
+	year={2023},
+	organization={PMLR}
+}
+
+@article{goodfellow2014,
+	title={Generative adversarial nets},
+	author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
+	journal={Advances in neural information processing systems},
+	volume={27},
+	year={2014}
+}
+
+@article{xu2019,
+	title={Modeling tabular data using conditional gan},
+	author={Xu, Lei and Skoularidou, Maria and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan},
+	journal={Advances in neural information processing systems},
+	volume={32},
+	year={2019}
+}
+
+@misc{nock2023,
+	title={Generative Forests}, 
+	author={Richard Nock and Mathieu Guillame-Bert},
+	year={2023},
+	eprint={2308.03648},
+	archivePrefix={arXiv},
+	primaryClass={cs.LG}
+}
+
+@misc{synthcity,
+	author = {van der Schaar Lab},
+	title = {synthcity: A library for generating and evaluating synthetic tabular data},
+	year = {2023},
+	publisher = {GitHub},
+	journal = {GitHub repository},
+	url = {https://github.com/vanderschaarlab/synthcity}
+}
+
+@misc{rpy2,
+	author = {{Laurent Gautier et al.}},
+	title = {rpy2: Python-R Bridge},
+	year = {2023},
+	publisher = {GitHub},
+	journal = {GitHub repository},
+	url = {https://github.com/rpy2/rpy2}
+}
+
+@article{grinsztajn2022,
+	title={Why do tree-based models still outperform deep learning on typical tabular data?},
+	author={Grinsztajn, L{\'e}o and Oyallon, Edouard and Varoquaux, Ga{\"e}l},
+	journal={Advances in Neural Information Processing Systems},
+	volume={35},
+	pages={507--520},
+	year={2022}
+}
+
+
+@article{breiman2001,
+  title={Random forests},
+  author={Breiman, Leo},
+  journal={Machine learning},
+  volume={45},
+  pages={5--32},
+  year={2001},
+  publisher={Springer}
+}
+
+@inproceedings{kingma2014,
+  title={Auto-encoding variational Bayes international},
+  author={Kingma, DP and Welling, M},
+  booktitle={Proc. Int. Conf. Learn.(ICLR)},
+  year={2014}
+}
+
+
+@InProceedings{rezende2015,
+  title = 	 {Variational Inference with Normalizing Flows},
+  author = 	 {Rezende, Danilo and Mohamed, Shakir},
+  booktitle = 	 {Proceedings of the 32nd International Conference on Machine Learning},
+  pages = 	 {1530--1538},
+  year = 	 {2015},
+  editor = 	 {Bach, Francis and Blei, David},
+  volume = 	 {37},
+  series = 	 {Proceedings of Machine Learning Research},
+  address = 	 {Lille, France},
+  month = 	 {07--09 Jul},
+  publisher =    {PMLR},
+  pdf = 	 {http://proceedings.mlr.press/v37/rezende15.pdf},
+  url = 	 {https://proceedings.mlr.press/v37/rezende15.html}
+}
+
+@article{ho2020,
+  title={Denoising diffusion probabilistic models},
+  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+  journal={Advances in neural information processing systems},
+  volume={33},
+  pages={6840--6851},
+  year={2020}
+}
+
+@article{vaswani2017,
+  title={Attention is all you need},
+  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
+  journal={Advances in neural information processing systems},
+  volume={30},
+  year={2017}
+}
+
+@article{shi2006,
+  title={Unsupervised learning with random forest predictors},
+  author={Shi, Tao and Horvath, Steve},
+  journal={Journal of Computational and Graphical Statistics},
+  volume={15},
+  number={1},
+  pages={118--138},
+  year={2006},
+  publisher={Taylor \& Francis}
+}
+
+  @Manual{wright2023,
+    title = {arf: Adversarial Random Forests},
+    author = {Marvin N. Wright and David S. Watson},
+    year = {2023},
+    note = {R package version 0.1.3},
+    url = {https://CRAN.R-project.org/package=arf},
+  }