Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 journals)
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    - MATHEMATICS (714 journals)
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    - NUMERICAL ANALYSIS (26 journals)
    - PROBABILITIES AND MATH STATISTICS (113 journals)

MATHEMATICS (714 journals)            First | 1 2 3 4 | Last

Showing 201 - 400 of 538 Journals sorted alphabetically
Educação Matemática Debate     Open Access  
Edumatica : Jurnal Pendidikan Matematika     Open Access  
EduMatSains     Open Access  
Electronic Journal of Differential Equations     Open Access  
Electronic Journal of Graph Theory and Applications     Open Access   (Followers: 3)
Em Teia : Revista de Educação Matemática e Tecnológica Iberoamericana     Open Access  
Emergent Scientist     Open Access  
Energy for Sustainable Development     Hybrid Journal   (Followers: 13)
Enseñanza de las Ciencias : Revista de Investigación y Experiencias Didácticas     Open Access  
Entropy     Open Access   (Followers: 5)
ESAIM: Control Optimisation and Calculus of Variations     Open Access   (Followers: 2)
Euclid     Open Access  
European Journal of Applied Mathematics     Hybrid Journal  
European Journal of Combinatorics     Full-text available via subscription   (Followers: 3)
European Journal of Mathematics     Hybrid Journal   (Followers: 1)
European Scientific Journal     Open Access   (Followers: 1)
Examples and Counterexamples     Open Access  
Experimental Mathematics     Hybrid Journal   (Followers: 5)
Expositiones Mathematicae     Hybrid Journal   (Followers: 2)
Extracta Mathematicae     Open Access  
Facta Universitatis, Series : Mathematics and Informatics     Open Access  
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 5)
Fixed Point Theory and Applications     Open Access  
Formalized Mathematics     Open Access  
Forum of Mathematics, Pi     Open Access   (Followers: 1)
Forum of Mathematics, Sigma     Open Access   (Followers: 1)
Foundations and Trends® in Econometrics     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Networking     Full-text available via subscription   (Followers: 1)
Foundations and Trends® in Stochastic Systems     Full-text available via subscription   (Followers: 1)
Foundations and Trends® in Theoretical Computer Science     Full-text available via subscription   (Followers: 1)
Foundations of Computational Mathematics     Hybrid Journal  
Fractal and Fractional     Open Access  
Fractals     Hybrid Journal   (Followers: 1)
Frontiers of Mathematics in China     Hybrid Journal  
Fuel Cells Bulletin     Full-text available via subscription   (Followers: 9)
Functional Analysis and Other Mathematics     Hybrid Journal   (Followers: 4)
Fundamental Journal of Mathematics and Applications     Open Access  
Funktsional'nyi Analiz i ego Prilozheniya     Full-text available via subscription  
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Game Theory     Open Access   (Followers: 2)
Games     Open Access   (Followers: 4)
Games and Economic Behavior     Hybrid Journal   (Followers: 25)
Gamm - Mitteilungen     Hybrid Journal  
GANIT : Journal of Bangladesh Mathematical Society     Open Access  
GEM - International Journal on Geomathematics     Hybrid Journal   (Followers: 1)
General Mathematics     Open Access  
Glasgow Mathematical Journal     Full-text available via subscription  
Global Journal of Mathematical Sciences     Full-text available via subscription  
Graphs and Combinatorics     Hybrid Journal   (Followers: 4)
Grey Systems : Theory and Application     Hybrid Journal  
Groups, Complexity, Cryptology     Open Access   (Followers: 2)
GSTF Journal of Mathematics, Statistics and Operations Research     Open Access   (Followers: 1)
Historia Mathematica     Full-text available via subscription  
Historical Methods: A Journal of Quantitative and Interdisciplinary History     Hybrid Journal   (Followers: 28)
IMA Journal of Applied Mathematics     Hybrid Journal  
IMA Journal of Numerical Analysis - advance access     Hybrid Journal  
ImmunoInformatics     Open Access   (Followers: 1)
Indagationes Mathematicae     Open Access  
Indian Journal of Pure and Applied Mathematics     Hybrid Journal   (Followers: 4)
Indonesian Journal of Combinatorics     Open Access  
Indonesian Journal of Science and Mathematics Education     Open Access   (Followers: 1)
Infinite Dimensional Analysis, Quantum Probability and Related Topics     Hybrid Journal   (Followers: 1)
Infinity Jurnal Matematika dan Aplikasinya     Open Access   (Followers: 3)
Information and Inference     Free  
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan     Open Access  
InfraMatics     Open Access  
Insight - Non-Destructive Testing and Condition Monitoring     Full-text available via subscription   (Followers: 110)
International Electronic Journal of Algebra     Open Access  
International Journal for Numerical Methods in Engineering     Hybrid Journal   (Followers: 35)
International Journal for Numerical Methods in Fluids     Hybrid Journal   (Followers: 19)
International Journal of Advanced Mathematical Sciences     Open Access  
International Journal of Advanced Mechatronic Systems     Hybrid Journal   (Followers: 2)
International Journal of Advanced Research in Mathematics     Open Access  
International Journal of Advances in Engineering Sciences and Applied Mathematics     Hybrid Journal   (Followers: 10)
International Journal of Algebra and Computation     Hybrid Journal   (Followers: 1)
International Journal of Algebra and Statistics     Open Access   (Followers: 3)
International Journal of Applied and Computational Mathematics     Hybrid Journal  
International Journal of Applied Mathematical Research     Open Access   (Followers: 1)
International Journal of Applied Mathematics and Computer Science     Open Access   (Followers: 7)
International Journal of Applied Mechanics     Hybrid Journal   (Followers: 8)
International Journal of Applied Nonlinear Science     Hybrid Journal  
International Journal of Autonomic Computing     Hybrid Journal   (Followers: 1)
International Journal of Bifurcation and Chaos     Hybrid Journal   (Followers: 4)
International Journal of Biomathematics     Hybrid Journal   (Followers: 2)
International Journal of Computational Complexity and Intelligent Algorithms     Hybrid Journal  
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
International Journal of Computational Geometry and Applications     Hybrid Journal   (Followers: 2)
International Journal of Computational Intelligence and Applications     Hybrid Journal   (Followers: 2)
International Journal of Computational Methods     Hybrid Journal   (Followers: 4)
International Journal of Computer Processing Of Languages     Hybrid Journal   (Followers: 1)
International Journal of Control, Automation and Systems     Hybrid Journal   (Followers: 15)
International Journal of Dynamical Systems and Differential Equations     Hybrid Journal   (Followers: 1)
International Journal of Economics and Accounting     Hybrid Journal   (Followers: 1)
International Journal of Foundations of Computer Science     Hybrid Journal   (Followers: 3)
International Journal of Fuzzy Computation and Modelling     Hybrid Journal   (Followers: 2)
International Journal of Image and Graphics     Hybrid Journal   (Followers: 5)
International Journal of Industrial Electronics and Drives     Hybrid Journal   (Followers: 3)
International Journal of Low-Carbon Technologies     Open Access   (Followers: 1)
International Journal of Mathematical Education in Science and Technology     Hybrid Journal   (Followers: 9)
International Journal of Mathematical Modelling & Computations     Open Access   (Followers: 3)
International Journal of Mathematical Modelling and Numerical Optimisation     Hybrid Journal   (Followers: 5)
International Journal of Mathematical Sciences and Computing     Open Access  
International Journal of Mathematics     Hybrid Journal   (Followers: 4)
International Journal of Mathematics & Computation     Full-text available via subscription  
International Journal of Mathematics and Mathematical Sciences     Open Access   (Followers: 4)
International Journal of Mathematics in Operational Research     Hybrid Journal   (Followers: 2)
International Journal of Metaheuristics     Hybrid Journal   (Followers: 1)
International Journal of Modelling in Operations Management     Hybrid Journal   (Followers: 2)
International Journal of Modern Nonlinear Theory and Application     Open Access   (Followers: 1)
International Journal of Number Theory     Hybrid Journal   (Followers: 1)
International Journal of Partial Differential Equations     Open Access   (Followers: 2)
International Journal of Polymer Science     Open Access   (Followers: 25)
International Journal of Pure Mathematical Sciences     Open Access  
International Journal of Reliability, Quality and Safety Engineering     Hybrid Journal   (Followers: 14)
International Journal of Research in Undergraduate Mathematics Education     Hybrid Journal   (Followers: 4)
International Journal of Sediment Research     Full-text available via subscription   (Followers: 2)
International Journal of Shape Modeling     Hybrid Journal   (Followers: 1)
International Journal of Theoretical and Mathematical Physics     Open Access   (Followers: 13)
International Journal of Trends in Mathematics Education Research     Open Access   (Followers: 4)
International Journal of Ultra Wideband Communications and Systems     Hybrid Journal  
International Journal of Wavelets, Multiresolution and Information Processing     Hybrid Journal  
International Journal on Artificial Intelligence Tools     Hybrid Journal   (Followers: 9)
International Mathematics Research Notices     Hybrid Journal   (Followers: 1)
Internet Mathematics     Hybrid Journal   (Followers: 1)
Inventiones mathematicae     Hybrid Journal   (Followers: 2)
Inverse Problems in Science and Engineering     Hybrid Journal   (Followers: 3)
Investigations in Mathematics Learning     Hybrid Journal  
Iranian Journal of Optimization     Open Access   (Followers: 2)
Israel Journal of Mathematics     Hybrid Journal  
Ithaca : Viaggio nella Scienza     Open Access  
ITM Web of Conferences     Open Access  
Izvestiya Rossiiskoi Akademii Nauk. Seriya Matematicheskaya     Full-text available via subscription  
Jahresbericht der Deutschen Mathematiker-Vereinigung     Hybrid Journal  
Japan Journal of Industrial and Applied Mathematics     Hybrid Journal  
Japanese Journal of Mathematics     Hybrid Journal  
JIPM (Jurnal Ilmiah Pendidikan Matematika)     Open Access  
JMPM : Jurnal Matematika dan Pendidikan Matematika     Open Access  
JOHME : Journal of Holistic Mathematics Education     Open Access   (Followers: 2)
Johnson Matthey Technology Review     Open Access  
Jornal Internacional de Estudos em Educação Matemática     Open Access  
Journal d'Analyse Mathématique     Hybrid Journal   (Followers: 2)
Journal de Mathématiques Pures et Appliquées     Full-text available via subscription   (Followers: 3)
Journal for Research in Mathematics Education     Full-text available via subscription   (Followers: 28)
Journal für Mathematik-Didaktik     Hybrid Journal  
Journal of Advanced Mathematics and Applications     Full-text available via subscription   (Followers: 1)
Journal of Algebra     Full-text available via subscription   (Followers: 3)
Journal of Algebra and Its Applications     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Journal of Algorithms & Computational Technology     Open Access  
Journal of Applied Mathematics     Open Access   (Followers: 3)
Journal of Applied Mathematics and Computing     Hybrid Journal  
Journal of Applied Mathematics, Statistics and Informatics     Open Access   (Followers: 1)
Journal of Artificial Intelligence and Data Mining     Open Access   (Followers: 10)
Journal of Classification     Hybrid Journal   (Followers: 5)
Journal of Combinatorial Designs     Hybrid Journal   (Followers: 4)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Combinatorial Theory, Series A     Full-text available via subscription   (Followers: 5)
Journal of Combinatorial Theory, Series B     Full-text available via subscription   (Followers: 3)
Journal of Complex Analysis     Open Access   (Followers: 2)
Journal of Complex Networks     Hybrid Journal   (Followers: 1)
Journal of Complexity     Hybrid Journal   (Followers: 6)
Journal of Computational and Applied Mathematics     Hybrid Journal   (Followers: 6)
Journal of Computational Biology     Hybrid Journal   (Followers: 9)
Journal of Computational Mathematics and Data Science     Open Access  
Journal of Computational Multiphase Flows     Open Access   (Followers: 1)
Journal of Computational Physics     Hybrid Journal   (Followers: 59)
Journal of Computational Physics : X     Open Access   (Followers: 1)
Journal of Computer Engineering, System and Science (CESS)     Open Access  
Journal of Contemporary Mathematical Analysis     Hybrid Journal  
Journal of Cryptology     Hybrid Journal   (Followers: 5)
Journal of Difference Equations and Applications     Hybrid Journal  
Journal of Differential Equations     Full-text available via subscription   (Followers: 1)
Journal of Discrete Mathematics     Open Access   (Followers: 1)
Journal of Dynamics and Differential Equations     Hybrid Journal  
Journal of Engineering Mathematics     Hybrid Journal   (Followers: 2)
Journal of Evolution Equations     Hybrid Journal  
Journal of Experimental Algorithmics     Full-text available via subscription  
Journal of Flood Risk Management     Hybrid Journal   (Followers: 14)
Journal of Function Spaces     Open Access  
Journal of Functional Analysis     Full-text available via subscription   (Followers: 3)
Journal of Geochemical Exploration     Hybrid Journal   (Followers: 4)
Journal of Geological Research     Open Access   (Followers: 1)
Journal of Geovisualization and Spatial Analysis     Hybrid Journal  
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Global Research in Mathematical Archives     Open Access  
Journal of Homotopy and Related Structures     Hybrid Journal  
Journal of Honai Math     Open Access  
Journal of Humanistic Mathematics     Open Access   (Followers: 1)
Journal of Hyperbolic Differential Equations     Hybrid Journal  
Journal of Indian Council of Philosophical Research     Hybrid Journal  
Journal of Industrial Mathematics     Open Access   (Followers: 2)
Journal of Inequalities and Applications     Open Access  
Journal of Infrared, Millimeter and Terahertz Waves     Hybrid Journal   (Followers: 3)
Journal of Integrable Systems     Open Access  
Journal of Knot Theory and Its Ramifications     Hybrid Journal   (Followers: 2)
Journal of Liquid Chromatography & Related Technologies     Hybrid Journal   (Followers: 7)
Journal of Logical and Algebraic Methods in Programming     Hybrid Journal   (Followers: 1)
Journal of Manufacturing Systems     Full-text available via subscription   (Followers: 3)
Journal of Mathematical Analysis and Applications     Full-text available via subscription   (Followers: 3)
Journal of mathematical and computational science     Open Access   (Followers: 2)

  First | 1 2 3 4 | Last

Similar Journals
Journal Cover
Information and Inference
Number of Followers: 0  

  Free journal Free journal
ISSN (Print) 2049-8764 - ISSN (Online) 2049-8772
Published by Oxford University Press Homepage  [419 journals]
  • Does SLOPE outperform bridge regression'
    • Authors: Wang S; Weng H, Maleki A.
      Pages: 1 - 54
      Abstract: AbstractA recently proposed SLOPE estimator [6] has been shown to adaptively achieve the minimax $\ell _2$ estimation rate under high-dimensional sparse linear regression models [25]. Such minimax optimality holds in the regime where the sparsity level $k$, sample size $n$ and dimension $p$ satisfy $k/p\rightarrow 0, k\log p/n\rightarrow 0$. In this paper, we characterize the estimation error of SLOPE under the complementary regime where both $k$ and $n$ scale linearly with $p$, and provide new insights into the performance of SLOPE estimators. We first derive a concentration inequality for the finite sample mean square error (MSE) of SLOPE. The quantity that MSE concentrates around takes a complicated and implicit form. With delicate analysis of the quantity, we prove that among all SLOPE estimators, LASSO is optimal for estimating $k$-sparse parameter vectors that do not have tied nonzero components in the low noise scenario. On the other hand, in the large noise scenario, the family of SLOPE estimators are sub-optimal compared with bridge regression such as the Ridge estimator.
      PubDate: Mon, 15 Nov 2021 00:00:00 GMT
      DOI: 10.1093/imaiai/iaab025
      Issue No: Vol. 11, No. 1 (2021)
       
  • Secure multiparty computations in floating-point arithmetic
    • Authors: Guo C; Hannun A, Knott B, et al.
      Pages: 103 - 135
      Abstract: AbstractSecure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shares). Thus, the parties may conspire to send all their processed results to a trusted third party (perhaps the data providers) at the conclusion of the computations, with only the trusted third party being able to view the final results. Secure multiparty computations for privacy-preserving machine-learning turn out to be possible using solely standard floating-point arithmetic, at least with a carefully controlled leakage of information less than the loss of accuracy due to roundoff, all backed by rigorous mathematical proofs of worst-case bounds on information loss and numerical stability in finite-precision arithmetic. Numerical examples illustrate the high performance attained on commodity off-the-shelf hardware for generalized linear models, including ordinary linear least-squares regression, binary and multinomial logistic regression, probit regression and Poisson regression.
      PubDate: Mon, 18 Jan 2021 00:00:00 GMT
      DOI: 10.1093/imaiai/iaaa038
      Issue No: Vol. 11, No. 1 (2021)
       
  • Distributed information-theoretic clustering
    • Authors: Pichler G; Piantanida P, Matz G.
      Pages: 137 - 166
      Abstract: AbstractWe study a novel multi-terminal source coding setup motivated by the biclustering problem. Two separate encoders observe two i.i.d. sequences $X^n$ and $Y^n$, respectively. The goal is to find rate-limited encodings $f(x^n)$ and $g(z^n)$ that maximize the mutual information $\textrm{I}(\,{f(X^n)};{g(Y^n)})/n$. We discuss connections of this problem with hypothesis testing against independence, pattern recognition and the information bottleneck method. Improving previous cardinality bounds for the inner and outer bounds allows us to thoroughly study the special case of a binary symmetric source and to quantify the gap between the inner and the outer bound in this special case. Furthermore, we investigate a multiple description (MD) extension of the CEO problem with mutual information constraint. Surprisingly, this MD-CEO problem permits a tight single-letter characterization of the achievable region.
      PubDate: Fri, 14 May 2021 00:00:00 GMT
      DOI: 10.1093/imaiai/iaab007
      Issue No: Vol. 11, No. 1 (2021)
       
  • A dimensionality reduction technique for unconstrained global optimization
           of functions with low effective dimensionality
    • Authors: Cartis C; Otemissov A.
      Pages: 167 - 201
      Abstract: AbstractWe investigate the unconstrained global optimization of functions with low effective dimensionality, which are constant along certain (unknown) linear subspaces. Extending the technique of random subspace embeddings in Wang et al. (2016, J. Artificial Intelligence Res., 55, 361–387), we study a generic Random Embeddings for Global Optimization (REGO) framework that is compatible with any global minimization algorithm. Instead of the original, potentially large-scale optimization problem, within REGO, a Gaussian random, low-dimensional problem with bound constraints is formulated and solved in a reduced space. We provide novel probabilistic bounds for the success of REGO in solving the original, low effective-dimensionality problem, which show its independence of the (potentially large) ambient dimension and its precise dependence on the dimensions of the effective and embedding subspaces. These results significantly improve existing theoretical analyses by providing the exact distribution of a reduced minimizer and its Euclidean norm and by the general assumptions required on the problem. We validate our theoretical findings by extensive numerical testing of REGO with three types of global optimization solvers, illustrating the improved scalability of REGO compared with the full-dimensional application of the respective solvers.
      PubDate: Wed, 19 May 2021 00:00:00 GMT
      DOI: 10.1093/imaiai/iaab011
      Issue No: Vol. 11, No. 1 (2021)
       
  • Compressed Sensing with 1D Total Variation: Breaking Sample Complexity
           Barriers via Non-Uniform Recovery
    • Authors: Genzel M; März M, Seidel R.
      Pages: 203 - 250
      Abstract: AbstractThis paper investigates total variation minimization in one spatial dimension for the recovery of gradient-sparse signals from undersampled Gaussian measurements. Recently established bounds for the required sampling rate state that uniform recovery of all $s$-gradient-sparse signals in ${\mathbb{R}}^n$ is only possible with $m \gtrsim \sqrt{s n} \cdot{\operatorname{PolyLog}}(n)$ measurements. Such a condition is especially prohibitive for high-dimensional problems, where $s$ is much smaller than $n$. However, previous empirical findings seem to indicate that this sampling rate does not reflect the typical behavior of total variation minimization. The present work provides a rigorous analysis that breaks the $\sqrt{s n}$-bottleneck for a large class of “natural” signals. The main result shows that non-uniform recovery succeeds with high probability for $m \gtrsim s \cdot{\operatorname{PolyLog}}(n)$ measurements if the jump discontinuities of the signal vector are sufficiently well separated. In particular, this guarantee allows for signals arising from a discretization of piecewise constant functions defined on an interval. The key ingredient of the proof is a novel upper bound for the associated conic Gaussian mean width, which is based on a signal-dependent, non-dyadic Haar wavelet transform. Furthermore, a natural extension to stable and robust recovery is addressed.
      PubDate: Sat, 15 May 2021 00:00:00 GMT
      DOI: 10.1093/imaiai/iaab001
      Issue No: Vol. 11, No. 1 (2021)
       
  • Compressive learning with privacy guarantees
    • Authors: Chatalic A; Schellekens V, Houssiau F, et al.
      Pages: 251 - 305
      Abstract: AbstractThis work addresses the problem of learning from large collections of data with privacy guarantees. The compressive learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, called a sketch vector, from which the learning task is then performed. We provide sharp bounds on the so-called sensitivity of this sketching mechanism. This allows us to leverage standard techniques to ensure differential privacy—a well-established formalism for defining and quantifying the privacy of a random mechanism—by adding Laplace of Gaussian noise to the sketch. We combine these standard mechanisms with a new feature subsampling mechanism, which reduces the computational cost without damaging privacy. The overall framework is applied to the tasks of Gaussian modeling, k-means clustering and principal component analysis, for which sharp privacy bounds are derived. Empirically, the quality (for subsequent learning) of the compressed representation produced by our mechanism is strongly related with the induced noise level, for which we give analytical expressions.
      PubDate: Sat, 15 May 2021 00:00:00 GMT
      DOI: 10.1093/imaiai/iaab005
      Issue No: Vol. 11, No. 1 (2021)
       
  • Learning deep linear neural networks: Riemannian gradient flows and
           convergence to global minimizers
    • Authors: Bah B; Rauhut H, Terstiege U, et al.
      Pages: 307 - 353
      Abstract: AbstractWe study the convergence of gradient flows related to learning deep linear neural networks (where the activation function is the identity map) from data. In this case, the composition of the network layers amounts to simply multiplying the weight matrices of all layers together, resulting in an overparameterized problem. The gradient flow with respect to these factors can be re-interpreted as a Riemannian gradient flow on the manifold of rank-$r$ matrices endowed with a suitable Riemannian metric. We show that the flow always converges to a critical point of the underlying functional. Moreover, we establish that, for almost all initializations, the flow converges to a global minimum on the manifold of rank $k$ matrices for some $k\leq r$.
      PubDate: Mon, 08 Feb 2021 00:00:00 GMT
      DOI: 10.1093/imaiai/iaaa039
      Issue No: Vol. 11, No. 1 (2021)
       
  • Breaking the waves: asymmetric random periodic features for low-bitrate
           kernel machines
    • Authors: Schellekens V; Jacques L.
      Pages: 385 - 421
      Abstract: AbstractMany signal processing and machine learning applications are built from evaluating a kernel on pairs of signals, e.g., to assess the similarity of an incoming query to a database of known signals. This nonlinear evaluation can be simplified to a linear inner product of the random Fourier features (RFFs) of those signals: random projections followed by a periodic map, the complex exponential. It is known that a simple quantization of those features (corresponding to replacing the complex exponential by a different periodic map that takes binary values, which is appealing for their transmission and storage) distorts the approximated kernel, which may be undesirable in practice. Our take-home message is that when the features of only one of the two signals are quantized, the original kernel is recovered without distortion; its practical interest appears in several cases where the kernel evaluations are asymmetric by nature, such as a client-server scheme. Concretely, we introduce the general framework of asymmetric random periodic features, where the two signals of interest are observed through random periodic features—random projections followed by a general periodic map, which is allowed to be different for both signals. We derive the influence of those periodic maps on the approximated kernel and prove uniform probabilistic error bounds holding for all pair of signals picked in an infinite low-complexity set. Interestingly, our results allow the periodic maps to be discontinuous, thanks to a new mathematical tool, i.e., the mean Lipschitz smoothness. We then apply this generic framework to semi-quantized kernel machines (where only one of the signals has quantized features and the other has classical RFFs), for which we show theoretically that the approximated kernel remains unchanged (with the associated error bound) and confirm the power of the approach with numerical simulations.
      PubDate: Tue, 11 May 2021 00:00:00 GMT
      DOI: 10.1093/imaiai/iaab008
      Issue No: Vol. 11, No. 1 (2021)
       
  • High-temperature structure detection in ferromagnets
    • Authors: Cao Y; Neykov M, Liu H.
      Pages: 55 - 102
      Abstract: AbstractThis paper studies structure detection problems in high-temperature ferromagnetic (positive interaction only) Ising models. The goal is to distinguish whether the underlying graph is empty, i.e., the model consists of independent Rademacher variables, vs. the alternative that the underlying graph contains a subgraph of a certain structure. We give matching upper and lower minimax bounds under which testing this problem is possible/impossible, respectively. Our results reveal that a key quantity called graph arboricity drives the testability of the problem. On the computational front, under a conjecture of the computational hardness of sparse principal component analysis, we prove that, unless the signal is strong enough, there are no polynomial time tests which are capable of testing this problem. In order to prove this result, we exhibit a way to give sharp inequalities for the even moments of sums of i.i.d. Rademacher random variables which may be of independent interest.
      PubDate: Sat, 26 Dec 2020 00:00:00 GMT
      DOI: 10.1093/imaiai/iaaa032
      Issue No: Vol. 11, No. 1 (2020)
       
  • Recovery analysis of damped spectrally sparse signals and its relation to
           MUSIC
    • Authors: Li S; Mansour H, Wakin M.
      Pages: 355 - 383
      Abstract: AbstractOne of the classical approaches for estimating the frequencies and damping factors in a spectrally sparse signal is the MUltiple SIgnal Classification (MUSIC) algorithm, which exploits the low-rank structure of an autocorrelation matrix. Low-rank matrices have also received considerable attention recently in the context of optimization algorithms with partial observations, and nuclear norm minimization (NNM) has been widely used as a popular heuristic of rank minimization for low-rank matrix recovery problems. On the other hand, it has been shown that NNM can be viewed as a special case of atomic norm minimization (ANM), which has achieved great success in solving line spectrum estimation problems. However, as far as we know, the general ANM (not NNM) considered in many existing works can only handle frequency estimation in undamped sinusoids. In this work, we aim to fill this gap and deal with damped spectrally sparse signal recovery problems. In particular, inspired by the dual analysis used in ANM, we offer a novel optimization-based perspective on the classical MUSIC algorithm and propose an algorithm for spectral estimation that involves searching for the peaks of the dual polynomial corresponding to a certain NNM problem, and we show that this algorithm is in fact equivalent to MUSIC itself. Building on this connection, we also extend the classical MUSIC algorithm to the missing data case. We provide exact recovery guarantees for our proposed algorithms and quantify how the sample complexity depends on the true spectral parameters. In particular, we provide a parameter-specific recovery bound for low-rank matrix recovery of jointly sparse signals rather than use certain incoherence properties as in existing literature. Simulation results also indicate that the proposed algorithms significantly outperform some relevant existing methods (e.g., ANM) in frequency estimation of damped exponentials.
      PubDate: Wed, 07 Oct 2020 00:00:00 GMT
      DOI: 10.1093/imaiai/iaaa024
      Issue No: Vol. 11, No. 1 (2020)
       
 
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