Ergodic Inference: Accelerate Convergence by Optimisation
Statistical inference methods are fundamentally important in machine learning. Most state-of-the-art inference algorithms are variants of Markov chain Monte Carlo (MCMC) or variational inference (VI). However, both methods struggle with limitations in practice: MCMC methods can be computationally demanding; VI methods may have large bias. In this work, we aim to improve upon MCMC and VI by a novel hybrid method based on the idea of reducing simulation bias of finite-length MCMC chains using gradient-based optimisation. The proposed method can generate low-biased samples by increasing the length of MCMC simulation and optimising the MCMC hyper-parameters, which offers attractive balance between approximation bias and computational efficiency. We show that our method produces promising results on popular benchmarks when compared to recent hybrid methods of MCMC and VI.
The presented method is very useful to deep learning in the era of uncertainty modelling, which requires the use of Bayesian inference arguments. It*s a valuable improvement upon variational inference, it*s novel, and the derivations are correct. The presentation is elaborate and covers all expected aspects. The literature review is up to date. The experimental results are diverse enough and convincing. The authors have considered both proof of concept experiments and deep learning architectures. The comparisons are valid.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Ergodic Inference: Accelerate Convergence by Optimisation
Statistical inference methods are fundamentally important in machine learning. Most state-of-the-art inference algorithms are variants of Markov chain Monte Carlo (MCMC) or variational inference (VI). However, both methods struggle with limitations in practice: MCMC methods can be computationally demanding; VI methods may have large bias. In this work, we aim to improve upon MCMC and VI by a novel hybrid method based on the idea of reducing simulation bias of finite-length MCMC chains using gradient-based optimisation. The proposed method can generate low-biased samples by increasing the length of MCMC simulation and optimising the MCMC hyper-parameters, which offers attractive balance between approximation bias and computational efficiency. We show that our method produces promising results on popular benchmarks when compared to recent hybrid methods of MCMC and VI.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The presented method is very useful to deep learning in the era of uncertainty modelling, which requires the use of Bayesian inference arguments. It*s a valuable improvement upon variational inference, it*s novel, and the derivations are correct. The presentation is elaborate and covers all expected aspects. The literature review is up to date. The experimental results are diverse enough and convincing. The authors have considered both proof of concept experiments and deep learning architectures. The comparisons are valid.
assistant
The presented method is very useful to deep learning in the era of uncertainty modelling, which requires the use of Bayesian inference arguments. It*s a valuable improvement upon variational inference, it*s novel, and the derivations are correct. The presentation is elaborate and covers all expected aspects. The literature review is up to date. The experimental results are diverse enough and convincing. The authors have considered both proof of concept experiments and deep learning architectures. The comparisons are valid.