Paper:
A-NICE-MC: Adversarial Training for MCMC
Existing Markov Chain Monte Carlo (MCMC) methods are either based on generalpurpose and domain-agnostic schemes, which can lead to slow convergence, or problem-specific proposals hand-crafted by an expert. In this paper, we propose A-NICE-MC, a novel method to automatically design efficient Markov chain kernels tailored for a specific domain. First, we propose an efficient likelihood-free adversarial training method to train a Markov chain and mimic a given data distribution. Then, we leverage flexible volume preserving flows to obtain parametric kernels for MCMC. Using a bootstrap approach, we show how to train efficient Markov chains to sample from a prescribed posterior distribution by iteratively improving the quality of both the model and the samples. Empirical results demonstrate that A-NICE-MC combines the strong guarantees of MCMC with the expressiveness of deep neural networks, and is able to significantly outperform competing methods such as Hamiltonian Monte Carlo.

Review:
This paper describes a novel adversarial training procedure to fit a generative model described by a Markov chain to sampled data. The Markov chain transitions are based on NICE, and so are reversible and volume preserving. It is therefore straightforward to use these as proposals in a Metropolis MCMC method to sample from arbitrary distributions. By repeatedly fitting the Markov chain model to samples from preliminary runs, we can hope that we'll end up with an MCMC method that mixes well on an arbitrary target distribution. Like HMC, the Markov chain is actually on a joint distribution of the parameters of interest, and some auxiliary random draws used to make a deterministic proposal.
It's a promising idea. The MCMC examples in the paper aren't particularly compelling. They're ok, but limited. However, this is the sort of algorithm that could work and be useful in some realistic situations.
Quality
The paper appears to be technically sound. It describes a valid MCMC method. It's unclear when a bootstrapping procedure (a precise procedure isn't given in the paper) will converge to a working proposal mechanism. The paper ends alluding to this issue. Variational inference would find a feasible region of parameters, but (like all MCMC methods), the method could be trapped there.
There is a danger that parts of the distribution are ignored. The cost function doesn't ensure that the proposal mechanism will get everywhere. Future work could investigate that further. Mixing updates with a more standard MCMC mechanism may reveal blind spots.
The toy experiments indicate that the method can learn to hop modes with similar densities in 2D. But don't reflect statistical models where MCMC inference is used. Of course section 3 shows some interesting transitions can be learned in high dimensions. However, the interest is in application to MCMC, as there is plenty of work on generating synthetic images using a variety of procedures.
The experiments on Bayesian logistic regression are a starting point for looking at real statistical analyses. Personally, if forced to use HMC on logistic regression, I would estimate and factor the covariance of the posterior S = LL', using a preliminary run, or other approximate inference. Then reparameterize the space z = L^{-1}x, and run HMC on z. Equivalently set the mass matrix to be S^{-1}. Learning a linear transformation is simpler and more standard than fitting neural nets -- would the added complication still be worth it?
Projects like Stan come with demonstrations. Are there more complicated distributions from real problems where A-NICE-MC would readily out-perform current practice?
Clarity
The paper is fairly clearly written. The MCMC algorithm isn't fully spelled out, or made concrete (what's p(v)?). It would only be reproducible (to some extent) for those familiar with both HMC and NICE.
Originality
While the general idea of fitting models and adapting proposals has appeared periodically in the MCMC literature, this work is novel. The adversarial method proposed seems a good fit for learning proposals, rather than boldly trying to learn an explicit representation of the global distribution.
Significance
Statisticians are unlikely to adopt this work in its current form. It's a rather heavier dependency than "black-box variational inference", and I would argue that fitting neural nets is not generically black-box yet. The work is promising, but more compelling statistical examples would help.
Volume preserving proposals won't solve everything. One might naively think that the only challenge for MCMC is to come up with proposal operators that can make proposals to all the modes. However, typical samples from the posterior of a rich model can potentially vary in density a lot. There could be narrow tall modes, or short large basins of density, potentially with comparable posterior mass. Hierarchical models in particular can have explanations with quite different densities. A volume-preserving proposal can usually only change the log-density by ~1, or the Metropolis rule will reject too often.
Minor
The discussion of HMC and rules that return to (x,v) after two updates are missing the idea of negating the velocity. In HMC, the dynamics only reverse if we deterministically set
    v' <- -v'
after the first update.
Please don't use the term "Exact Sampling" in the header to section 4.1. In the MCMC literature that term usually means generating perfect independent samples from a target distribution, a much stronger requirement than creating updates that leave the target distribution invariant: http://dbwilson.com/exact/
The filesize of this paper is larger than it should be, making the paper slow to download and display. Figure 2 seems to be stored as a .jpeg, but should be a vector graphic -- or if you can't manage that, a .png that hasn't passed through being a .jpeg. Figure 3 doesn't seem to be stored as a .jpeg, and I think would be ~10x smaller if it were(?).
Several bibtex entries aren't capitalized properly. Protect capital letters with {.}.