Venue: NeurIPS 2020 Workshop DL-IG

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: This paper explores annealed importance sampling (AIS) using a power-mean of the initial and target distributions. Geometric mean of the initial and target is typically used to estimate the log-partition function via AIS, using a power-mean has the benefit that the interpolated distribution can belong to a different family, as shown by the authors in Fig. 1. The paper further discusses connections with log-mixtures, alpha-divergences and q-exponential families. Experimental results are provided that compute the ratio of the partition functions between two Gaussians with Hamiltonian Monte Carlo to obtain samples from the interpolants. The Appendix develops q-paths further.
review_title: Review

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: The submission revisits the path used for Annealed Importance Sampling and considers paths that interpolate between the two distributions using a generalized mean.  This gives them a family of q-paths, with properties analogous to the standard geometric mixture paths but with generalized notions.  E.g. generalized exponential families over ordinary ones, or having the intermediate states be the solution to a weighted alpha-divergence minimization rather than a weighted KL divergence.

I think the paper is nice, it's a neat result and its is presented well.  It's shown that on a simple problem these broader notions of paths can show improved convergence and more accurate measurements than the typically chosen paths.

I think it's a great workshop submission and a neat result, don't think I have any complaints.
review_title: Nice result

Decision: Accept (Oral)