We also provide a detailed analysis quantifying its complexity.

Neural Information Processing Systems http://nips.cc/

The analysis for Hogwild algorithm seems to be new as well, but I didn't go through the proof of that part. Significance: The new analysis supporting asynchronous update when features are sparse, will be an interesting message to the community. However as the assumption in Eq.(2) excludes many interesting problems, I don't think it will have a large impact. Q2: Please summarize your review in 1-2 sentences The paper suggests an analysis showing that the optimal bounds can be achieved by their suggested algorithm up to some factors, where the algorithm is claimed to have linear speedup with the number of cores and to benefit from the sparsity of input features. However, its applicability seems to be limited due to a rather restrictive assumption it is based upon, which makes the suggested method sensitive to the choices of stepsizes, and the other assumption on asynchronous update delays.

For this problem, with either a known or unknown system, we give an efficient sublinear regret algorithm.

The field of machine programming (MP), the automation of the development of software, is making notable research advances.

In Appendix A, we review some statistical results for sparse linear regression. In Appendix B, we provide the proof of main theorems as well as main claims. We review some classical results in sparse linear regression. B.1 Proof of Claim 3.5 We first prove the first part. Combining with Eq. (B.6), we have under event D B.2 Proof of Claim 3.6 From the divergence decomposition lemma (Lemma C.2 in the appendix), we have KLnull P To prove the claim, we use a simple argument "minimum is always smaller than the average".

We propose new continuous-time formulations for first-order stochastic optimization algorithms such as mini-batch gradient descent and variance-reduced methods.

Neural Information Processing Systems http://nips.cc/