Paper:
Acceleration and Averaging In Stochastic Descent Dynamics
We formulate and study a general family of (continuous-time) stochastic dynamics for accelerated first-order minimization of smooth convex functions. Building on an averaging formulation of accelerated mirror descent, we propose a stochastic variant in which the gradient is contaminated by noise, and study the resulting stochastic differential equation. We prove a bound on the rate of change of an energy function associated with the problem, then use it to derive estimates of convergence rates of the function values (almost surely and in expectation), both for persistent and asymptotically vanishing noise. We discuss the interaction between the parameters of the dynamics (learning rate and averaging rates) and the covariation of the noise process. In particular, we show how the asymptotic rate of covariation affects the choice of parameters and, ultimately, the convergence rate.

Review:
Summary:
The authors consider mirror descent dynamics in continuous time for constrained optimization problems. They work in the context of stochastic differential equations and consider parametrized dynamics which include many of the recently considered parameters for such dynamics. This includes time dependant learning rate, sensitivity and averaging parameters which provide a great flexibility in the analysis. The dynamics also feature acceleration terms which were recently proposed in a deterministic setting. 
In a first step, the authors carry out an asymptotic analysis in the deterministic setting. They then introduce a stochastic variant of the dynamics and describe uniqueness properties of its solutions. Combining properties of the deterministic dynamics and stochastic calculus the authors propose three results: Almost sure convergence of the sample trajectories, convergence rates, both in expectation and for sample trajectories.
Main comments:
First of all, I have a limited knowledge of stochastic differential calculus and do not feel competent to validate all the technical details in the manuscript, although none of them felt particularly awkward.
I believe that this work is of very good quality. Technical elements are clearly presented and the authors do not neglect fundamental aspects of the problem such as existence and uniqueness. Furthermore, the overall exposition is clear and of good quality and the text is well balanced between the main proof arguments and further details given in the appendix. 
To my understanding, the convergence analysis carried out by the authors extends significantly existing results from the litterature. The model is flexible enough to allow many possible choice of the dynamics. For example the authors are able to show that unit learning rate may robustly maintain good properties of accelerated dynamics in the deterministic case provided that suitable averaging is performed. 
Overall, I think that this constitutes valuble work and should be considered for publication.
Additional comments and questions:
1- The authors motivate the introduction of stochastic dynamics by emphasizing on stochastic methods for finite sums. However the additive noise structure is not particularly well suited to model such cases. Indeed, in these settings the variance of the noise usually features a multiplicative term.
2- All the results are presented in abstract form with the addition of an other parameter "r" and assumptions on it. It is not really clear how the assumptions on r restrict the choice of the other parameters. As a result, the convergence rates are not easily read or interpreted. Beyon the proposed corollaries, it is not really clear how the given result provide interesting guaranties in terms of function values. For example would the work of the authors allow to get better rates in the small noise regime?
3- Do the authors have any comment on how the analysis which is carried out in a stochastic, continuous time setting could impact the discrete time setting. Is there a clear path to the conception of efficient algorithms and in particular is it possible to provide a discrete time equivalent for the different time dependent parameters in the ODE?