Deep Gaussian Processes with Importance-Weighted Variational Inference
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James Hensman | Marc Peter Deisenroth | Hugh Salimbeni | Vincent Dutordoir | M. Deisenroth | J. Hensman | Hugh Salimbeni | Vincent Dutordoir
[1] Juan José Murillo-Fuentes,et al. Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo , 2018, NeurIPS.
[2] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[3] Joshua B. Tenenbaum,et al. Automatic Construction and Natural-Language Description of Nonparametric Regression Models , 2014, AAAI.
[4] Carl Henrik Ek,et al. Latent Gaussian Process Regression , 2017, ArXiv.
[5] Yee Whye Teh,et al. Tighter Variational Bounds are Not Necessarily Better , 2018, ICML.
[6] James Hensman,et al. On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes , 2015, AISTATS.
[7] Neil D. Lawrence,et al. Bayesian Gaussian Process Latent Variable Model , 2010, AISTATS.
[8] George Tucker,et al. Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives , 2019, ICLR.
[9] Honglak Lee,et al. Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.
[10] Andrew M. Stuart,et al. How Deep Are Deep Gaussian Processes? , 2017, J. Mach. Learn. Res..
[11] Ruslan Salakhutdinov,et al. Importance Weighted Autoencoders , 2015, ICLR.
[12] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Michalis K. Titsias,et al. Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.
[14] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[15] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[16] Marc Peter Deisenroth,et al. Doubly Stochastic Variational Inference for Deep Gaussian Processes , 2017, NIPS.
[17] Radford M. Neal. Regression and Classification Using Gaussian Process Priors , 2009 .
[18] Katja Hofmann,et al. Meta Reinforcement Learning with Latent Variable Gaussian Processes , 2018, UAI.
[19] Radford M. Neal,et al. Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals , 2012, ArXiv.
[20] Neil D. Lawrence,et al. Variational Auto-encoded Deep Gaussian Processes , 2015, ICLR.
[21] Guodong Zhang,et al. Differentiable Compositional Kernel Learning for Gaussian Processes , 2018, ICML.
[22] James Hensman,et al. Gaussian Process Conditional Density Estimation , 2018, NeurIPS.
[23] Ruslan Salakhutdinov,et al. On the Quantitative Analysis of Decoder-Based Generative Models , 2016, ICLR.
[24] Neil D. Lawrence,et al. Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.
[25] Neil D. Lawrence,et al. Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes , 2017, NIPS.
[26] Daniel Hernández-Lobato,et al. Deep Gaussian Processes for Regression using Approximate Expectation Propagation , 2016, ICML.
[27] Neil D. Lawrence,et al. Kernels for Vector-Valued Functions: a Review , 2011, Found. Trends Mach. Learn..
[28] Neil D. Lawrence,et al. Gaussian Processes for Big Data , 2013, UAI.
[29] Neil D. Lawrence,et al. Deep Gaussian Processes , 2012, AISTATS.
[30] Neil D. Lawrence,et al. Semi-described and semi-supervised learning with Gaussian processes , 2015, UAI.
[31] Alexis Boukouvalas,et al. GPflow: A Gaussian Process Library using TensorFlow , 2016, J. Mach. Learn. Res..
[32] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[33] Ryan P. Adams,et al. Avoiding pathologies in very deep networks , 2014, AISTATS.
[34] Byron Boots,et al. Incremental Variational Sparse Gaussian Process Regression , 2016, NIPS.
[35] El-Ghazali Talbi,et al. Bayesian optimization using deep Gaussian processes with applications to aerospace system design , 2019, Optimization and Engineering.
[36] Neil D. Lawrence,et al. Fast Variational Inference in the Conjugate Exponential Family , 2012, NIPS.
[37] A. P. Dawid,et al. Regression and Classification Using Gaussian Process Priors , 2009 .
[38] James Hensman,et al. Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models , 2018, AISTATS.
[39] Justin Domke,et al. Importance Weighting and Variational Inference , 2018, NeurIPS.