Yingzhen Li

Proceedings of the 38th International Conference on Machine Learning, 2021

Sliced Kernelized Stein Discrepancy.

[BibT_eX]

[DOI]

Wenbo Gong

Proceedings of the 9th International Conference on Learning Representations, 2021

Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification.

[BibT_eX]

[DOI]

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

Meta-Learning Divergences for Variational Inference.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Reinforcement Learning with Efficient Active Feature Acquisition.

[BibT_eX]

[DOI]

Sebastian Tschiatschek

CoRR, 2020

A Study on Efficiency in Continual Learning Inspired by Human Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Hierarchical Sparse Variational Autoencoder for Text Encoding.

[BibT_eX]

[DOI]

CoRR, 2020

Interpreting Spatially Infinite Generative Models.

[BibT_eX]

[DOI]

CoRR, 2020

Meta-Learning for Variational Inference.

[BibT_eX]

[DOI]

CoRR, 2020

Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data.

[BibT_eX]

[DOI]

CoRR, 2020

On the Expressiveness of Approximate Inference in Bayesian Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Causal View on Robustness of Neural Networks.

[BibT_eX]

[DOI]

Kun Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2019

'In-Between' Uncertainty in Bayesian Neural Networks.

[BibT_eX]

[DOI]

Andrew Y. K. Foong

CoRR, 2019

Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care.

[BibT_eX]

[DOI]

Yordan Zaykov

CoRR, 2019

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck.

[BibT_eX]

[DOI]

Maximilian Igl

Kamil Ciosek

Sebastian Tschiatschek

Sam Devlin

Katja Hofmann

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bayesian Learning for Neural Dependency Parsing.

[BibT_eX]

[DOI]

Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019

Variational Implicit Processes.

[BibT_eX]

[DOI]

Chao Ma

Proceedings of the 36th International Conference on Machine Learning, 2019

Are Generative Classifiers More Robust to Adversarial Attacks?

[BibT_eX]

[DOI]

John Bradshaw

Yash Sharma

Proceedings of the 36th International Conference on Machine Learning, 2019

Meta-Learning For Stochastic Gradient MCMC.

[BibT_eX]

[DOI]

Wenbo Gong

Proceedings of the 7th International Conference on Learning Representations, 2019

On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation.

[BibT_eX]

[DOI]

Victor Prokhorov

Ehsan Shareghi

Mohammad Taher Pilehvar

Nigel Collier

Proceedings of the 3rd Workshop on Neural Generation and Translation@EMNLP-IJCNLP 2019, 2019

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals.

[BibT_eX]

[DOI]

Chao Ma

Sebastian Tschiatschek

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018

Approximate inference: new visions.

[BibT_eX]

[DOI]

PhD thesis, 2018

A Deep Generative Model for Disentangled Representations of Sequential Data.

[BibT_eX]

[DOI]

Stephan Mandt

CoRR, 2018

Are Generative Classifiers More Robust to Adversarial Attacks?

[BibT_eX]

[DOI]

CoRR, 2018

Disentangled Sequential Autoencoder.

[BibT_eX]

[DOI]

Stephan Mandt

Proceedings of the 35th International Conference on Machine Learning, 2018

Variational Continual Learning.

[BibT_eX]

[DOI]

Proceedings of the 6th International Conference on Learning Representations, 2018

Gradient Estimators for Implicit Models.

[BibT_eX]

[DOI]

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Approximate Inference with Amortised MCMC.

[BibT_eX]

[DOI]

Qiang Liu

CoRR, 2017

Dropout Inference in Bayesian Neural Networks with Alpha-divergences.

[BibT_eX]

[DOI]

Yarin Gal

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Variational Inference with Rényi Divergence.

[BibT_eX]

[DOI]

CoRR, 2016

Rényi Divergence Variational Inference.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Black-Box Alpha Divergence Minimization.

[BibT_eX]

[DOI]

Mark Rowland

Thang D. Bui

Daniel Hernández-Lobato

Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Gaussian Processes for Regression using Approximate Expectation Propagation.

[BibT_eX]

[DOI]

Thang D. Bui

Daniel Hernández-Lobato

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Stochastic Expectation Propagation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2012

Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog

[BibT_eX]

[DOI]