13 papers co-authored by members of the group have been accepted to the main program of NIPS 2018:

  • V. Perrone, R. Jenatton, M. Seeger, C. Archambeau. Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start.

  • J. Chan, V. Perrone, J. Spence, P. A. Jenkins, S. Mathieson and Yun S. Song. A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks.

  • S. Lyddon, S. Walker, C. Holmes. Nonparametric learning for Bayesian models via randomized objective functions.

  • A. L. Caterini, A. Doucet, and D. Sejdinovic. Hamiltonian Variational Auto-Encoder.

  • H. C. L. Law, D. Sejdinovic, E. Cameron, T. C. D. Lucas, S. Flaxman, K. Battle, and K. Fukumizu, Variational Learning on Aggregate Outputs with Gaussian Processes

  • H. Lee, J. Lee, S Kim, E Yang and S. J. Hwang. DropMax: Adaptive Variational Softmax.

  • J. Heo, H. Lee, S. Kim, J. Lee, K. Kim, E. Yang, and S. J. Hwang. Uncertainty-Aware Attention for Reliable Interpretation and Prediction.

  • Emilien Dupont. Learning Disentangled Joint Continuous and Discrete Representations.

  • X. Miscouridou, F. Caron, Y. W. Teh. Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data.

  • S. Webb, A. Golinski, R. Zinkov, N Siddharth, T. Rainforth, Y. W. Teh, F. Wood. Faithful Inversion of Generative Models for Effective Amortized Inference.

  • J. Chen, J. Zhu, Y. W. Teh, T. Zhang. Stochastic Expectation Maximization with Variance Reduction.

  • J. Mitrovic, D. Sejdinovic, Y. W. Teh. Causal Inference via Kernel Deviance Measures.

  • A. Kosiorek, H. Kim, Y. W. Teh, I. Posner. Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects.