| Generative Models as Distributions of Functions | 0 | 0.34 | 2022 |
| Amortized Rejection Sampling in Universal Probabilistic Programming | 0 | 0.34 | 2022 |
| Multiplicative Interactions and Where to Find Them | 0 | 0.34 | 2020 |
| Bootstrapping Neural Processes | 0 | 0.34 | 2020 |
| Bayesian Deep Ensembles via the Neural Tangent Kernel | 0 | 0.34 | 2020 |
| How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? | 0 | 0.34 | 2020 |
| Functional Regularisation for Continual Learning with Gaussian Processes | 0 | 0.34 | 2020 |
| Continual Unsupervised Representation Learning. | 0 | 0.34 | 2019 |
| Noise Contrastive Meta-Learning For Conditional Density Estimation Using Kernel Mean Embeddings | 0 | 0.34 | 2019 |
| Exploiting Hierarchy for Learning and Transfer in KL-regularized RL. | 2 | 0.36 | 2019 |
| Probabilistic Symmetries and Invariant Neural Networks | 5 | 0.47 | 2019 |
| Hierarchical Representations with Poincaré Variational Auto-Encoders. | 0 | 0.34 | 2019 |
| Meta-Learning surrogate models for sequential decision making. | 0 | 0.34 | 2019 |
| Random Tessellation Forests. | 0 | 0.34 | 2019 |
| Information asymmetry in KL-regularized RL. | 1 | 0.35 | 2019 |
| Hybrid Models with Deep and Invertible Features. | 1 | 0.35 | 2019 |
| Hijacking Malaria Simulators with Probabilistic Programming. | 0 | 0.34 | 2019 |
| Meta-learning of Sequential Strategies. | 2 | 0.36 | 2019 |
| Augmented Neural ODEs. | 0 | 0.34 | 2019 |
| Task Agnostic Continual Learning via Meta Learning. | 0 | 0.34 | 2019 |
| Revisiting Reweighted Wake-Sleep. | 0 | 0.34 | 2018 |
| Faithful Inversion of Generative Models for Effective Amortized Inference | 0 | 0.34 | 2018 |
| Sampling And Inference For Beta Neutral-To-The-Left Models Of Sparse Networks | 0 | 0.34 | 2018 |
| Conditional Neural Processes. | 0 | 0.34 | 2018 |
| Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data | 0 | 0.34 | 2018 |
| On Exploration, Exploitation and Learning in Adaptive Importance Sampling. | 1 | 0.35 | 2018 |
| Neural Processes. | 0 | 0.34 | 2018 |
| Stochastic Expectation Maximization with Variance Reduction. | 2 | 0.37 | 2018 |
| A Statistical Approach to Assessing Neural Network Robustness. | 0 | 0.34 | 2018 |
| Mix & Match Agent Curricula for Reinforcement Learning. | 2 | 0.35 | 2018 |
| Hamiltonian Descent Methods. | 0 | 0.34 | 2018 |
| Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects. | 8 | 0.45 | 2018 |
| Tighter Variational Bounds are Not Necessarily Better. | 8 | 0.52 | 2018 |
| Neural probabilistic motor primitives for humanoid control. | 4 | 0.39 | 2018 |
| Disentangling Disentanglement. | 0 | 0.34 | 2018 |
| Poisson intensity estimation with reproducing kernels. | 4 | 0.50 | 2017 |
| Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. | 0 | 0.34 | 2017 |
| Particle Value Functions. | 0 | 0.34 | 2017 |
| Gaussian Processes for Survival Analysis. | 0 | 0.34 | 2016 |
| Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics. | 1 | 0.35 | 2016 |
| The Mondrian Kernel. | 1 | 0.38 | 2016 |
| DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. | 1 | 0.36 | 2016 |
| Consistency and Fluctuations For Stochastic Gradient Langevin Dynamics | 18 | 0.87 | 2016 |
| The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. | 24 | 0.59 | 2016 |
| Image Retrieval with a Bayesian Model of Relevance Feedback. | 1 | 0.35 | 2016 |
| The Mondrian Process for Machine Learning | 0 | 0.34 | 2015 |
| A hybrid sampler for Poisson-Kingman mixture models | 1 | 0.40 | 2015 |
| Mondrian Forests for Large-Scale Regression when Uncertainty Matters | 6 | 0.50 | 2015 |
| On a class of σ-stable Poisson---Kingman models and an effective marginalized sampler | 0 | 0.34 | 2015 |
| Asynchronous Anytime Sequential Monte Carlo. | 13 | 0.87 | 2014 |
