Paper:
Decomposable Submodular Function Minimization Discrete and Continuous
This paper investigates connections between discrete and continuous approaches for decomposable submodular function minimization. We provide improved running time estimates for the state-of-the-art continuous algorithms for the problem using combinatorial arguments. We also provide a systematic experimental comparison of the two types of methods, based on a clear distinction between level-0 and level-1 algorithms.

Review:
This paper studies the problem of minimizing a decomposable submodular function. Submodular minimization is a well studied and important problem in machine learning for which there exist algorithms to solve the problem exactly. However, the running time of these algorithms is a high polynomial and they are thus oftentimes not practical. To get around this issue, submodular functions that can be decomposed and written as a sum of submodular functions over a much smaller support (DSFM) are often considered as they often appear in practice. 
This paper improves the analysis of the fastest algorithms for DSFM by a factor equal to the number of functions in the decomposition. It also provides an experimental framework based on a distinction between “level 0” algorithms, which are subroutines for quadratic minimization, and “level 1” algorithms which minimize the function using level 0 as a black box. These allow a more meaningful comparison where the same level 0 algorithms are used to compare different algorithms. These experiments show a tradeoff between the discrete algorithms that require more calls to the level 0 subroutines and gradient methods with weaker requirements for level 0 but more computation for level 1.
The analysis is complex and relies on both discrete and continuous optimization techniques to meaningfully improve the running time of an important problem where the computational complexity is expensive. The experiments also highlight an interesting tradeoff which suggests that different algorithms should be used in different contexts for the running time of DSFM.
A weakness of the paper is that the writing is very dense and sometimes hard to follow. It would have been nice to have more discussion on the parameters kappa* and l* and the precise bounds in terms of these parameters. It would have also been nice to have some comparison on the theoretical bounds between RCDM, ACDM, and IBFS.