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@inproceedings{10.5555/3157096.3157340,
author = {Chen, Xi and Duan, Yan and Houthooft, Rein and Schulman, John and Sutskever, Ilya and Abbeel, Pieter},
title = {InfoGAN: interpretable representation learning by information maximizing generative adversarial nets},
year = {2016},
isbn = {9781510838819},
publisher = {Curran Associates Inc.},
address = {Red Hook, NY, USA},
abstract = {This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner. InfoGAN is a generative adversarial network that also maximizes the mutual information between a small subset of the latent variables and the observation. We derive a lower bound of the mutual information objective that can be optimized efficiently. Specifically, InfoGAN successfully disentangles writing styles from digit shapes on the MNIST dataset, pose from lighting of 3D rendered images, and background digits from the central digit on the SVHN dataset. It also discovers visual concepts that include hair styles, presence/absence of eyeglasses, and emotions on the CelebA face dataset. Experiments show that InfoGAN learns interpretable representations that are competitive with representations learned by existing supervised methods. For an up-to-date version of this paper, please see https://arxiv.org/abs/1606.03657.},
booktitle = {Proceedings of the 30th International Conference on Neural Information Processing Systems},
pages = {2180–2188},
numpages = {9},
location = {Barcelona, Spain},
series = {NIPS'16}
}
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  year={2021},
  organization={PMLR}
}
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@article{nguyen2023linear,
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  pages={109351},
  year={2023},
  publisher={Elsevier}
}
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  title={Optimal transport for applied mathematicians},
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  pages={94},
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  publisher={Springer}
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@article{chen2020just,
  title={Just pick a sign: Optimizing deep multitask models with gradient sign dropout},
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  journal={Advances in Neural Information Processing Systems},
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  pages={2039--2050},
  year={2020}
}
@article{yu2020gradient,
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  author={Yu, Tianhe and Kumar, Saurabh and Gupta, Abhishek and Levine, Sergey and Hausman, Karol and Finn, Chelsea},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={5824--5836},
  year={2020}
}

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  volume={34},
  pages={18878--18890},
  year={2021}
}

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@incollection{deb2016multi,
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}
@inproceedings{ma2018modeling,
  title={Modeling task relationships in multi-task learning with multi-gate mixture-of-experts},
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@article{thomas2021multi,
  title={Multi-objective spibb: Seldonian offline policy improvement with safety constraints in finite mdps},
  author={Thomas, Philip S and Pineau, Joelle and Laroche, Romain and others},
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  year={2021}
}

@article{nguyen2023mirror,
  title={Mirror variational transport: a particle-based algorithm for distributional optimization on constrained domains},
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  pages={1--25},
  year={2023},
  publisher={Springer}
}

@article{nguyen2024moreau,
  title={Moreau-Yoshida variational transport: a general framework for solving regularized distributional optimization problems},
  author={Nguyen, Dai Hai and Sakurai, Tetsuya},
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  number={9},
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@article{chen2018unified,
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  year={2018}
}

@article{liu2016stein,
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@inproceedings{welling2011bayesian,
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@article{liu2021profiling,
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@article{nguyen2023wasserstein,
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  year={2023}
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@inproceedings{nguyenwasserstein,
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@inproceedings{zhang2016first,
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  volume={32},
  year={2019}
}

@article{petric2019got,
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}

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