Yingzhen Li
Title
Cited by
Cited by
Year
Variational Continual Learning
CV Nguyen, Y Li, TD Bui, RE Turner
2952018
Variational Continual Learning
CV Nguyen, Y Li, TD Bui, RE Turner
arXiv preprint arXiv:1710.10628, 2017
2952017
Deep gaussian processes for regression using approximate expectation propagation
T Bui, D Hernández-Lobato, J Hernandez-Lobato, Y Li, R Turner
International Conference on Machine Learning, 1472-1481, 2016
1602016
Rényi divergence variational inference
Y Li, RE Turner
Advances in Neural Information Processing Systems, 1073-1081, 2016
1602016
Black-box α-divergence minimization
JM Hernández-Lobato, Y Li, M Rowland, D Hernández-Lobato, TD Bui, ...
International Machine Learning Society, 2016
1512016
Dropout inference in bayesian neural networks with alpha-divergences
Y Li, Y Gal
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
1342017
Disentangled Sequential Autoencoder
L Yingzhen, S Mandt
International Conference on Machine Learning, 5656-5665, 2018
101*2018
Stochastic expectation propagation
Y Li, JM Hernández-Lobato, RE Turner
Advances in Neural Information Processing Systems, 2323-2331, 2015
942015
Gradient Estimators for Implicit Models
Y Li, RE Turner
arXiv preprint arXiv:1705.07107, 2017
492017
Are generative classifiers more robust to adversarial attacks?
Y Li, J Bradshaw, Y Sharma
International Conference on Machine Learning, 3804-3814, 2019
382019
Approximate Inference with Amortised MCMC
Y Li, RE Turner, Q Liu
arXiv preprint arXiv:1702.08343, 2017
342017
Generalization in reinforcement learning with selective noise injection and information bottleneck
M Igl, K Ciosek, Y Li, S Tschiatschek, C Zhang, S Devlin, K Hofmann
Advances in Neural Information Processing Systems, 13978-13990, 2019
312019
Variational implicit processes
C Ma, Y Li, JM Hernández-Lobato
International Conference on Machine Learning, 4222-4233, 2019
262019
Meta-Learning for Stochastic Gradient MCMC
W Gong, Y Li, JM Hernández-Lobato
arXiv preprint arXiv:1806.04522, 2018
222018
Variational inference with Rényi divergence
Y Li, RE Turner
stat 1050, 6, 2016
162016
Training deep Gaussian processes using stochastic expectation propagation and probabilistic backpropagation
TD Bui, JM Hernández-Lobato, Y Li, D Hernández-Lobato, RE Turner
arXiv preprint arXiv:1511.03405, 2015
142015
A Deep Generative Model for Disentangled Representations of Sequential Data
Y Li, S Mandt
arXiv preprint arXiv:1803.02991, 2018
122018
On the expressiveness of approximate inference in bayesian neural networks
A Foong, D Burt, Y Li, R Turner
Advances in Neural Information Processing Systems 33, 2020
112020
Wild Variational Approximations
Y Li, Q Liu
NIPS workshop on advances in approximate Bayesian inference, 2016
102016
On the importance of the Kullback-Leibler divergence term in variational autoencoders for text generation
V Prokhorov, E Shareghi, Y Li, MT Pilehvar, N Collier
arXiv preprint arXiv:1909.13668, 2019
82019
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Articles 1–20