Fetching the paper…
Reading the bibliography…
The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching.
Nonparametric Density Estimation: The L 1 L_{1} View
Devroye, L. and Györfi, L · 1985
Earlier work this paper cites.
Radial basis functions, multi-variable functional interpolation and adaptive networks
Broomhead, D. S. and Lowe, D · 1988
Earlier work this paper cites.
Tutorial on large deviations for the binomial distribution
Arratia, R. and Gordon, L · 1989
Earlier work this paper cites.
Approximation of density functions by sequences of exponential families
Barron, A. and Sheu, C.-H · 1991
Earlier work this paper cites.
Normalized cuts and image segmentation
Shi, J. and Malik, J · 2000
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
Earlier work this paper cites.
Kernel methods and the exponential family
Canu, S. and Smola, A. J · 2006
Earlier work this paper cites.
A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., and Huang, F · 2006
Earlier work this paper cites.
All of Nonparametric Statistics
Wasserman, L · 2006
Earlier work this paper cites.
Exponential manifold by reproducing kernel Hilbert spaces
Fukumizu, K · 2009
Earlier work this paper cites.
Interpretation and generalization of score matching
Lyu, S · 2009
Earlier work this paper cites.
Regularized estimation of image statistics by score matching
Kingma, D. P. and LeCun, Y · 2010
Earlier work this paper cites.
Rnade: The real-valued neural autoregressive density-estimator
Uria, B., Murray, I., and Larochelle, H · 2013
Earlier work this paper cites.
Score function features for discriminative learning: Matrix and tensor framework, 2014
Janzamin, M., Sedghi, H., and Anandkumar, A · 2014
Cited alongside, same era.
Clustering via mode seeking by direct estimation of the gradient of a log-density
Sasaki, H., Hyvärinen, A., and Sugiyama, M · 2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
Cited alongside, same era.
Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H · 2015
Cited alongside, same era.
Variational inference with normalizing flows
Jimenez Rezende, D. and Mohamed, S · 2015
Cited alongside, same era.
Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
Later among the works it cites.
A linear-time kernel goodness-of-fit test
Jitkrittum, W., Xu, W., Szábo, Z., Fukumizu, K., and Gretton, A · 2017
Later among the works it cites.
MMD GAN: Towards deeper understanding of moment matching network
Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., and Póczos, B · 2017
Later among the works it cites.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
Later among the works it cites.
Density estimation in infinite dimensional exponential families
Sriperumbudur, B., Fukumizu, K., Gretton, A., Hyvärinen, A., and Kumar, R · 2017
Later among the works it cites.
Kernel conditional exponential family
Arbel, M. and Gretton, A · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. L · 2015
Cited alongside, same era.
Gradient-free Hamiltonian Monte Carlo with efficient kernel exponential families
Strathmann, H., Sejdinovic, D., Livingstone, S., Szabó, Z., and Gretton, A · 2015
Cited alongside, same era.
Learning structured densities via infinite dimensional exponential families
Sun, S., Kolar, M., and Xu, J · 2015
Cited alongside, same era.
A kernel test of goodness of fit
Chwialkowski, K., Strathmann, H., and Gretton, A · 2016
Cited alongside, same era.
Pixel recurrent neural networks
van den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Cited alongside, same era.
Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P · 2016
Cited alongside, same era.
The Cramer distance as a solution to biased Wasserstein gradients, 2017
Bellemare, M. G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., and Munos, R · 2017
Cited alongside, same era.
On gradient regularizers for MMD GANs
Arbel, M., Sutherland, D. J., Binkowski, M., and Gretton, A · 2018
Closest in time.
Demystifying MMD GANs
Bińkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A · 2018
Closest in time.
Semi-supervised deep kernel learning: Regression with unlabeled data by minimizing predictive variance
Jean, N., Xie, S., and Ermon, S · 2018
Closest in time.
Informative features for model comparison
Jitkrittum, W., Kanagawa, H., Szábo, Z., Sangkloy, P., Hays, J., Schölkopf, B., and Gretton, A · 2018
Closest in time.
Sharpening Jensen’s inequality
Liao, J. G. and Berg, A · 2018
Closest in time.
Deep energy estimator networks, 2018
Saremi, S., Mehrjou, A., Schölkopf, B., and Hyvärinen, A · 2018
Closest in time.
Efficient and principled score estimation with Nyström kernel exponential families
Sutherland, D. J., Strathmann, H., Arbel, M., and Gretton, A · 2018
Closest in time.