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We extend PAC-Bayesian theory to generative models and develop generalization bounds for models based on the Wasserstein distance and the total variation distance.
An iterative algorithm for computing the best estimate of an orthogonal matrix
Björck, Å. and Bowie, C · 1971
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On the method of bounded differences , pp. 148–188
McDiarmid, C · 1989
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Integral probability metrics and their generating classes of functions
Müller, A · 1997
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Some PAC-Bayesian theorems
McAllester, D. A · 1999
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(not) bounding the true error
Langford, J. and Caruana, R · 2001
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A survey of dimension reduction techniques
Fodor, I. K · 2002
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PAC-Bayesian stochastic model selection
McAllester, D. A · 2003
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McDiarmid’s inequalities of Bernstein and Bennett forms
Ying, Y · 2004
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PAC-Bayesian supervised classification: the thermodynamics of statistical learning , volume 56
Catoni, O · 2007
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PAC-Bayesian learning of linear classifiers
Germain, P., Lacasse, A., Laviolette, F., and Marchand, M · 2009
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Optimal transport: old and new , volume 338
Villani, C · 2009
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Sample complexity of testing the manifold hypothesis
Narayanan, H. and Mitter, S · 2010
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Wasserstein barycenter and its application to texture mixing
Rabin, J., Peyré, G., Delon, J., and Bernot, M · 2011
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The MNIST database of handwritten digit images for machine learning research
Deng, L · 2012
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PAC-Bayes bounds with data dependent priors
Parrado-Hernández, E., Ambroladze, A., Shawe-Taylor, J., and Sun, S · 2012
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-encoding Variational Bayes
Kingma, D. P. and Welling, M · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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PAC-Bayesian bounds based on the Rényi divergence
Bégin, L., Germain, P., Laviolette, F., and Roy, J.-F · 2016
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Testing the manifold hypothesis
Fefferman, C., Mitter, S., and Narayanan, H · 2016
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PAC-Bayesian theory meets Bayesian inference
Germain, P., Bach, F., Lacoste, A., and Lacoste-Julien, S · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X., and Chen, X · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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A note on the evaluation of generative models
Theis, L., van den Oord, A., and Bethge, M · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
Cited alongside, same era.
Generalization and equilibrium in generative adversarial nets (GANs)
Arora, S., Ge, R., Liang, Y., Ma, T., and Zhang, Y · 2017
Cited alongside, same era.
Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., and Courville, A · 2017
Cited alongside, same era.
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Data augmentation using generative adversarial networks (cyclegan) to improve generalizability in ct segmentation tasks
Sandfort, V., Yan, K., Pickhardt, P. J., and Summers, R. M · 2019
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Nonparametric density estimation & convergence rates for GANs under besov ipm losses
Uppal, A., Singh, S., and Póczos, B · 2019
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Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance
Weed, J. and Bach, F · 2019
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High-level library to help with training neural networks in pytorch
Fomin, V., Anmol, J., Desroziers, S., Kriss, J., and Tejani, A · 2020
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PAC-Bayes analysis beyond the usual bounds
Rivasplata, O., Kuzborskij, I., Szepesvári, C., and Shawe-Taylor, J · 2020
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Veegan: Reducing mode collapse in GANs using implicit variational learning
Srivastava, A., Valkov, L., Russell, C., Gutmann, M. U., and Sutton, C · 2017
Cited alongside, same era.
Energy-based generative adversarial networks
Zhao, J., Mathieu, M., and LeCun, Y · 2017
Cited alongside, same era.
Towards high resolution video generation with progressive growing of sliced Wasserstein GANs
Acharya, D., Huang, Z., Paudel, D. P., and Van Gool, L · 2018
Cited alongside, same era.
Meta-learning by adjusting priors based on extended PAC-Bayes theory
Amit, R. and Meir, R · 2018
Cited alongside, same era.
Consistency of variational Bayes inference for estimation and model selection in mixtures
Chérief-Abdellatif, B.-E. and Alquier, P · 2018
Cited alongside, same era.
Data-dependent PAC-Bayes priors via differential privacy
Dziugaite, G. K. and Roy, D. M · 2018
Cited alongside, same era.
Seddik, M. E. A., Louart, C., Tamaazousti, M., and Couillet, R · 2020
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Improved PAC-Bayesian bounds for linear regression
Shalaeva, V., Esfahani, A. F., Germain, P., and Petreczky, M · 2020
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User-friendly introduction to PAC-Bayes bounds
Alquier, P · 2021
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Some theoretical insights into Wasserstein GANs
Biau, G., Sangnier, M., and Tanielian, U · 2021
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A survey on text generation using generative adversarial networks
de Rosa, G. H. and Papa, J. P · 2021
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PAC-Bayes unleashed: Generalisation bounds with unbounded losses
Haddouche, M., Guedj, B., Rivasplata, O., and Shawe-Taylor, J · 2021
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How well generative adversarial networks learn distributions
Liang, T · 2021
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Distributional sliced-wasserstein and applications to generative modeling
Nguyen, K., Ho, N., Pham, T., and Bui, H · 2021
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Tighter risk certificates for neural networks
Pérez-Ortiz, M., Rivasplata, O., Shawe-Taylor, J., and Szepesvári, C · 2021
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The intrinsic dimension of images and its impact on learning
Pope, P., Zhu, C., Abdelkader, A., Goldblum, M., and Goldstein, T · 2021
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Expanding functional protein sequence spaces using generative adversarial networks
Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W., et al · 2021
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Statistical guarantees for generative models without domination
Schreuder, N., Brunel, V.-E., and Dalalyan, A · 2021
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A general framework for the disintegration of PAC-Bayesian bounds
Viallard, P., Germain, P., Habrard, A., and Morvant, E · 2021
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Integral probability metrics PAC-bayes bounds
Amit, R., Epstein, B., Moran, S., and Meir, R · 2022
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On margins and derandomisation in PAC-Bayes
Biggs, F. and Guedj, B · 2022
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On PAC-Bayesian reconstruction guarantees for VAEs
Chérief-Abdellatif, B.-E., Shi, Y., Doucet, A., and Guedj, B · 2022
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Online PAC-Bayes learning
Haddouche, M. and Guedj, B · 2022
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Shedding a PAC-Bayesian light on adaptive sliced-Wasserstein distances
Ohana, R., Nadjahi, K., Rakotomamonjy, A., and Ralaivola, L · 2022
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On predicting generalization using GANs
Zhang, Y., Gupta, A., Saunshi, N., and Arora, S · 2022
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