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- George Papamakarios, Theo Pavlakou, Iain Murray
- NIPS
- 2017

Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers… (More)

- George Papamakarios, Iain Murray
- NIPS
- 2016

Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data.… (More)

- George Papamakarios, David C. Sterratt, Iain Murray
- AISTATS
- 2018

We present Sequential Neural Likelihood (SNL), a new method for Bayesian inference in simulator models, where the likelihood is intractable but simulating data from the model is possible. SNL trains… (More)

- George Papamakarios
- ArXiv
- 2015

Top-performing machine learning systems, such as deep neural networks, large ensembles and complex probabilistic graphical models, can be expensive to store, slow to evaluate and hard to integrate… (More)

Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data.… (More)

- George Papamakarios, Iain Murray
- NIPS
- 2016

An important challenge in reinforcement learning arises in domains where the agent’s observations are partial or noisy measurements of the state of the environment. In such domains, a policy that… (More)

A generative model’s partition function is typically expressed as an intractable multi-dimensional integral, whose approximation presents a challenge to numerical and Monte Carlo integration. In this… (More)

- Conor Durkan, George Papamakarios, Iain Murray
- ArXiv
- 2018

Likelihood-free inference refers to inference when a likelihood function cannot be explicitly evaluated, which is often the case for models based on simulators. Most of the literature is based on… (More)

Gradient-based optimization methods are popular in machine learning applications. In large-scale problems, stochastic methods are preferred due to their good scaling properties. In this project, we… (More)