Paper:
An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression
Multi-response linear models aggregate a set of vanilla linear models by assuming correlated noise across them, which has an unknown covariance structure. To find the coefficient vector, estimators with a joint approximation of the noise covariance are often preferred than the simple linear regression in view of their superior empirical performance, which can be generally solved by alternating-minimizationtype procedures. Due to the non-convex nature of such joint estimators, the theoretical justification of their efficiency is typically challenging. The existing analyses fail to fully explain the empirical observations due to the assumption of resampling on the alternating procedures, which requires access to fresh samples in each iteration. In this work, we present a resampling-free analysis for the alternating minimization algorithm applied to the multi-response regression. In particular, we focus on the high-dimensional setting of multi-response linear models with structured coefficient parameter, and the statistical error of the parameter can be expressed by the complexity measure, Gaussian width, which is related to the assumed structure. More importantly, to the best of our knowledge, our result reveals for the first time that the alternating minimization with random initialization can achieve the same performance as the well-initialized one when solving this multi-response regression problem. Experimental results support our theoretical developments.

Review:
This paper studies the multi-response regression model, and in particular, the alternating minimization algorithm for the problem . In contrast to prior work, this paper makes the following improvements: 1. It does not require the resampling assumptions that usually abound in showing results on alternating procedures. With good initialization, the procedure without resampling is able to achieve what is usually the minimax rate of such problems. 2. It shows that an arbitrarily initialized problem will still exhibit convergence, but with a worse rate. 3. It allows for the unknown parameter to lie in a possibly non-convex set, and for the noise to be sub-Gaussian.  The major technique is that of generic chaining, which allows the authors to prove bounds that hold uniformly over all iterates.  I liked the paper and the result overall seems quite interesting, in particular points 1 and 2 above. It seems as though the techniques could also translate to other problems. While I did not read the proofs very carefully, they seem quite reasonable overall given the deterministic conditions that the authors posit. On the negative side, the fact that proof for Alternating Minimization are known to exist without re-sampling means that the paper would benefit from a comparison of the current techniques to those used in the past; if the techniques themselves are not so different, then analyzing AltMin in this particular setting without resampling is not particularly impactful or insightful.  Concerns: 1. The authors could de-emphasize point 3 above. It does not particularly impress me, since the authors require an exact solution to each iteration of the parameter estimation problem anyway, irrespective of non-convexity, and this can only be provided in some settings. Similarly, most Gaussian upper bounds can be translated to sub-Gaussian upper bounds since the required behavior of the stochastic process is similar in the two cases. 2. It would be good to compare these results to other results on alternating minimization that do not require resampling (e.g., see the references below).   Minor: Line 131: The function could be misinterpreted. Consider stating as d_1 (respectively d_2) to signify two clauses.  [1] I. Waldspurger, “Phase retrieval with random Gaussian sensing vectors by alternating projections” IEEE Trans. on Inf Theory, 2018. [2] M. Soltanolkotabi. Algorithms and theory for clustering and nonconvex quadratic programming. PhD thesis, Stanford University, 2014.  Post-rebuttal: I have read the response, and maintain my score. This is a strong paper with interesting results.