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Information and Inference A Journal of the IMA is a peer-reviewed journal published by Oxford University Press, covering Sparse and Compressive Sensing Techniques, Statistical Methods and Inference, Blind Source Separation Techniques.
| Publisher | Oxford University Press |
|---|---|
| Reference style | Author–year (OUP) Author–year — (Smith, 2023) in the text Smith, A., Jones, B. and Lee, C. (2023) 'A representative article title', Information and Inference A Journal of the IMA, 12(3), pp. 45–58.
Formats any DOI in the closest standard style — Information and Inference A Journal of the IMA has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Sparse and Compressive Sensing Techniques Statistical Methods and Inference Blind Source Separation Techniques Stochastic Gradient Optimization Techniques Markov Chains and Monte Carlo Methods |
| ISSN | 2049-8764 |
| Citation impact (2-yr) | 1.19 |
| h-index | 33 |
| i10-index | 99 |
| Total citations | 4,447 |
| Article processing charge | $3,167 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | academic.oup.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Recent research indicates that many convex optimization problems with random constraints exhibit a phase transition as the number of constraints increases. For example, this phenomenon emerges in the ℓ1 minimization method for identifying a sparse vector from random linear measurements. Indeed, the ℓ1 approach succeeds with high probability when the number of measurements exceeds a…
The synchronization problem over the special orthogonal group SO(d) consists of estimating a set of unknown rotations from noisy measurements of a subset of their pairwise ratios . The problem has found applications in computer vision, computer graphics and sensor network localization, among others. Its least squares solution can be approximated by either spectral relaxation…
We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in compressed sensing of structured high-dimensional signals such as videos and hyperspectral images, as well as in the analysis of transformation invariant low-rank structure recovery. We analyse…
Abstract We develop procedures, based on minimization of the composition $f(x) = h(c(x))$ of a convex function $h$ and smooth function $c$, for solving random collections of quadratic equalities, applying our methodology to phase retrieval problems. We show that the prox-linear algorithm we develop can solve phase retrieval problems—even with adversarially faulty measurements—with high probability…
Abstract We study the sample complexity of learning neural networks by providing new bounds on their Rademacher complexity, assuming norm constraints on the parameter matrix of each layer. Compared to previous work, these complexity bounds have improved dependence on the network depth and, under some additional assumptions, are fully independent of the network size (both…
