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发表于 2020-10-14 14:45:11 |只看该作者 |倒序浏览
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题名The unit ball in conjugate L1 spaces.
作者lazar
杂志duke math j, 1971
链接https://projecteuclid.org/euclid.dmj/1077380094

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发表于 2021-5-11 09:29:26 |只看该作者
本帖最后由 colimae 于 2021-5-11 20:01 编辑

InfInf(2012-2020):
2012.
Dear Information and Inference Reader
        Calderbank, Donoho, Shawe-Taylor, Tanner
he masked sample covariance estimator: an analysis using matrix concentration inequalities
        Richard Y. Chen, Alex Gittens, Joel A. Tropp
Eigenvector synchronization, graph rigidity and the molecule problem
        Mihai Cucuringu, Amit Singer, David Cowburn
Semi-supervised single- and multi-domain regression with multi-domain training
        Michaeli, Eldar, Sapiro
2013.
Approximation of points on low-dimensional manifolds via random linear projections
        Mark A. Iwen, Mauro Maggioni
Compressive principal component pursuit
        John Wright, Arvind Ganesh, Kerui Min, Yi Ma
Tangent space estimation for smooth embeddings of Riemannian manifolds
        Hemant Tyagi, Elif Vural, Pascal Frossard
State evolution for general approximate message passing algorithms, with applications to spatial coupling
        Javanmard, Montanari
Exact and stable recovery of rotations for robust synchronization
        Lanhui Wang, Amit Singer
2014.
Cramér–Rao bounds for synchronization of rotations
        Nicolas Boumal, Amit Singer, P.-A. Absil, Vincent D. Blondel
Sigma–Delta quantization of sub-Gaussian frame expansions and its application to compressed sensing
        Felix Krahmer, Rayan Saab, Özgür Yilmaz
Higher order Sobol' indices........................Art B. Owen, Josef Dick, Su Chen
Phase retrieval from power spectra of masked signals
        Afonso S. Bandeira, Yutong Chen, Dustin G. Mixon
Finite sample posterior concentration in high-dimensional regression
        Nate Strawn, Artin Armagan, Rayan Saab, Lawrence Carin, David Dunson
Non-asymptotic analysis of tangent space perturbation
        Daniel N. Kaslovsky, François G. Meyer
1-Bit matrix completion
        Davenport, Plan, van den Berg, Wootters
Living on the edge: phase transitions in convex programs with random data
        Dennis Amelunxen, Martin Lotz, Michael B. McCoy, Joel A. Tropp
Scaling law for recovering the sparsest element in a subspace
        Laurent Demanet, Paul Hand
Persistent homology transform for modeling shapes and surfaces
        Katharine Turner, Sayan Mukherjee, Doug M. Boyer
Deterministic Bayesian information fusion and the analysis of its performance
        Gaurav Thakur
2015.
Graph connection Laplacian and random matrices with random blocks
        El Karoui, Hau-tieng Wu
Disparity and optical flow partitioning using extended Potts priors
        Xiaohao Cai, Jan Henrik Fitschen, Mila Nikolova, Gabriele Steidl, Martin Storath
On spectral properties for graph matching and graph isomorphism problems
        Fiori, Sapiro
Compressed subspace matching on the continuum
        Mantzel, Romberg
Riemannian metrics for neural networks I: feedforward networks
Riemannian metrics for neural networks II: recurrent networks and learning symbolic data sequences
        Yann Ollivier
Tensor sparsification via a bound on the spectral norm of random tensors
        Nam H. Nguyen, Petros Drineas, Trac D. Tran
Model selection with low complexity priors
        Samuel Vaiter, Mohammad Golbabaee, Jalal Fadili, Gabriel Peyré
CGIHT: conjugate gradient iterative hard thresholding for compressed sensing and matrix completion
        Jeffrey D. Blanchard, Jared Tanner, Ke Wei
Guarantees of total variation minimization for signal recovery
        Jian-Feng Cai, Weiyu Xu
Replication procedure for grouped Sobol' indices estimation in dependent uncertainty spaces
        Laurent Gilquin, Clémentine Prieur, Elise Arnaud
ERRATUM--------Finite sample posterior concentration in high-dimensional regression
N. Strawn, A. Armagan, R. Saab, L. Carin, D. Dunson
2016.
Robust subspace recovery by Tyler's M-estimator
        Teng Zhang
Super-resolution radar.......................Heckel, Morgenshtern, Soltanolkotabi
A null-space-based weighted l1 minimization approach to compressed sensing
        Shenglong Zhou, Naihua Xiu, Yingnan Wang, Lingchen Kong, Hou-Duo Qi
Super-resolution of point sources via convex programming
Carlos Fernandez-Granda
Detecting the large entries of a sparse covariance matrix in sub-quadratic time
        Ofer Shwartz, Boaz Nadler
Near-optimal estimation of simultaneously sparse and low-rank matrices from nested linear measurements
        Bahmani, Romberg
Total variation regularization on Riemannian manifolds by iteratively reweighted minimization
        Philipp Grohs, Markus Sprecher
On the optimality of averaging in distributed statistical learning
        Jonathan D. Rosenblatt, Boaz Nadler
Stable low-rank matrix recovery via null space properties
        Maryia Kabanava, Richard Kueng, Holger Rauhut, Ulrich Terstiege
2016s.
Special issue: Deep learning....................Francis Bach, Tomaso Poggio
Deep Haar scattering networks
        Xiuyuan Cheng, Xu Chen, Stéphane Mallat
On invariance and selectivity in representation learning
        Fabio Anselmi, Lorenzo Rosasco, Tomaso Poggio
A theoretical framework for deep transfer learning
        Tomer Galanti, Lior Wolf, Tamir Hazan
GSNs: generative stochastic networks
        Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski,
        Éric Thibodeau-Laufer, Saizheng Zhang,  Pascal Vincent
2017.

2018.
Superresolution without separation
        Geoffrey Schiebinger, Elina Robeva, Benjamin Recht
Quantized minimax estimation over Sobolev ellipsoids
        Yuancheng Zhu, John Lafferty
One-bit compressive sensing of dictionary-sparse signals
        Baraniuk, Foucart, Needell, Plan, Wootters
Demixing sines and spikes: Robust spectral super-resolution in the presence of outliers
        Carlos Fernandez-Granda, Gongguo Tang, Xiaodong Wang, Le Zheng
When is non-trivial estimation possible for graphons and stochastic block models?‡
        Audra McMillan, Adam Smith
Algorithms for learning sparse additive models with interactions in high dimensions*
        Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause
Weighted mining of massive collections of P-values by convex optimization
        Edgar Dobriban
Fast, robust and non-convex subspace recoverygraphic
        Gilad Lerman, Tyler Maunu
Universality laws for randomized dimension reduction, with applications
        Samet Oymak, Joel A Tropp
Sketching for large-scale learning of mixture models
        Nicolas Keriven, Anthony Bourrier, Rémi Gribonval, Patrick Pérez
Gaussian approximation of general non-parametric posterior distributions
        Zuofeng Shang, Guang Cheng
Iterative reconstruction of rank-one matrices in noise
        Alyson K Fletcher, Sundeep Rangan
A convex program for mixed linear regression with a recovery guarantee for well-separated datagraphic
        Paul Hand, Babhru Joshi
MC2: a two-phase algorithm for leveraged matrix completion
        Armin Eftekhari, Michael B Wakin, Rachel A Ward
New approach to Bayesian high-dimensional linear regression
        Shirin Jalali, Arian Maleki
Structured sampling and fast reconstruction of smooth graph signals
        Gilles Puy, Patrick Pérez
A spectral assignment approach for the graph isomorphism problemgraphic
        Stefan Klus, Tuhin Sahai
Isometric sketching of any set via the Restricted Isometry Property
        Oymak, Recht, Soltanolkotabi
Conditional expectation estimation through attributable components
        Esteban G Tabak, Giulio Trigila
Gradient descent with non-convex constraints: local concavity determines convergencegraphic
        Rina Foygel Barber, Wooseok Ha














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发表于 2021-5-11 20:00:42 |只看该作者
本帖最后由 colimae 于 2021-5-11 20:14 编辑

2019.
Regularized gradient descent: a non-convex recipe for fast joint blind deconvolution and demixing
        Shuyang Ling, Strohmer
The non-convex geometry of low-rank matrix optimizationgraphic
        Qiuwei Li, Zhihui Zhu, Gongguo Tang
Phase retrieval via randomized Kaczmarz: theoretical guarantees
        Yan Shuo Tan, Vershynin
Non-Gaussian observations in nonlinear compressed sensing via Stein discrepancies
        Larry Goldstein, Xiaohan Wei
Quantization for low-rank matrix recoverygraphic
        Eric Lybrand, Rayan Saab
Simple, direct and efficient multi-way spectral clusteringgraphic
        Anil Damle, Victor Minden, Lexing Ying
Through the haze: a non-convex approach to blind gain calibration for linear random sensing models
        Valerio Cambareri, Laurent Jacques
Duality of graphical models and tensor networks
        Elina Robeva, Anna Seigal
Ensemble-based estimates of eigenvector error for empirical covariance matrices
        Dane Taylor, Juan G Restrepo, François G Meyer
A pseudo knockoff filter for correlated features
        Jiajie Chen, Anthony Hou, Thomas Y Hou
An efficient algorithm for compression-based compressed sensing
        Sajjad Beygi, Shirin Jalali, Arian Maleki, Urbashi Mitra
Mahalanobis distance informed by clusteringgraphic
        Almog Lahav, Ronen Talmon, Yuval Kluger
Exact solutions of infinite dimensional total-variation regularized problems
        Axel Flinth, Pierre Weiss
Matrix decompositions using sub-Gaussian random matrices
        Yariv Aizenbud, Amir Averbuch
Solving (most) of a set of quadratic equalities: composite optimization for robust phase retrieval
        John C Duchi, Feng Ruan
Sparsity/undersampling tradeoffs in anisotropic undersampling, with applications in MR imaging/spectroscopy
        Monajemi, Donoho
Near-optimal sample complexity for convex tensor completion
        Ghadermarzy, Plan, Yilmaz
Bayesian sparse linear regression with unknown symmetric error
        Minwoo Chae, Lizhen Lin, David B Dunson
2019s.
Editorial IMA IAI - Information and Inference special issue on optimal transport in data sciences
        Gabriel Peyré, Marco Cuturi
On parameter estimation with the Wasserstein distancegraphic
        Espen Bernton, Pierre E Jacob, Mathieu Gerber, Christian P Robert
Estimating matching affinity matrices under low-rank constraints
        Arnaud Dupuy, Alfred Galichon, Yifei Sun
Uncoupled isotonic regression via minimum Wasserstein deconvolution
        Philippe Rigollet, Jonathan Weed
Data-driven regularization of Wasserstein barycenters with an application to multivariate density registration
        Jérémie Bigot, Elsa Cazelles, Nicolas Papadakis
The Gromov–Wasserstein distance between networks and stable network invariants
        Samir Chowdhury, Facundo Mémoli
Adaptive optimal transport
        Montacer Essid, Debra F Laefer, Esteban G Tabak
A central limit theorem for Lp transportation cost on the real line with application to fairness assessment in machine learning
        Eustasio del Barrio, Paula Gordaliza, Jean-Michel Loubes
2020.
Computational complexity versus statistical performance on sparse recovery problems
        Vincent Roulet, Nicolas Boumal, Alexandre d’Aspremont
State evolution for approximate message passing with non-separable functions
        Raphaël Berthier, Andrea Montanari, Phan-Minh Nguyen
Second-order asymptotically optimal statistical classificationgraphic
        Lin Zhou, Vincent Y F Tan, Mehul Motani
Low noise sensitivity analysis of graphic-minimization in oversampled systems
        Haolei Weng, Arian Maleki
Generalized notions of sparsity and restricted isometry property. Part I: a unified framework
        Marius Junge, Kiryung Lee
Empirical Bayes estimators for high-dimensional sparse vectors
        K Pavan Srinath, Ramji Venkataramanan
A characterization of the Non-Degenerate Source Condition in super-resolutiongraphic
        Vincent Duval
A prototype knockoff filter for group selection with FDR control
        Jiajie Chen, Anthony Hou, Thomas Y Hou
Non-convex low-rank matrix recovery with arbitrary outliers via median-truncated gradient descent
        Yuanxin Li, Yuejie Chi, Huishuai Zhang, Yingbin Liang
Network topology inference using information cascades with limited statistical knowledge
        Feng Ji, Wenchang Tang, Wee Peng Tay, Edwin K P Chong
Robust 1-bit compressed sensing via hinge loss minimization
        Martin Genzel, Alexander Stollenwerk
Near-optimal recovery of linear and N-convex functions on unions of convex sets
        Anatoli Juditsky, Arkadi Nemirovski
Analysis of hard-thresholding for distributed compressed sensing with one-bit measurements
        Johannes Maly, Lars Palzer
Size-independent sample complexity of neural networks
        Noah Golowich, Alexander Rakhlin, Ohad Shamir
Phase transitions of spectral initialization for high-dimensional non-convex estimation
        Yue M Lu, Gen Li
Quantized compressive sensing with RIP matrices: the benefit of dithering
        Chunlei Xu, Laurent Jacques
Maximum number of modes of Gaussian mixtures
        Carlos Améndola, Alexander Engström, Christian Haase
One-bit compressed sensing with partial Gaussian circulant matrices
        Sjoerd Dirksen, Hans Christian Jung, Holger Rauhut
On the S-instability and degeneracy of discrete deep learning models
        Andee Kaplan, Daniel J Nordman, Stephen B Vardeman
Quantifying the estimation error of principal component vectors
        Raphael Hauser, Jüri Lember, Heinrich Matzinger, Raul Kangro
Two-sample statistics based on anisotropic kernelsgraphic
        Xiuyuan Cheng, Alexander Cloninger, Ronald R Coifman
Phase harmonic correlations and convolutional neural networks
        Stéphane Mallat, Sixin Zhang, Gaspar Rochette
Matchability of heterogeneous networks pairsgraphic
        Vince Lyzinski, Daniel L Sussman
Analysis of fast structured dictionary learninggraphic
        Saiprasad Ravishankar, Anna Ma, Deanna Needell
Concentration inequalities for the empirical distribution of discrete distributions: beyond the method of types
        Jay Mardia, Jiantao Jiao, Ervin Tánczos, Robert D Nowak, Tsachy Weissman
Stochastic modified equations for the asynchronous stochastic gradient descent
        Jing An, Jianfeng Lu, Lexing Ying
CHIRRUP: a practical algorithm for unsourced multiple access
        Robert Calderbank, Andrew Thompson
Between hard and soft thresholding: optimal iterative thresholding algorithms
        Haoyang Liu, Rina Foygel Barber
Minimal Lipschitz and ∞-harmonic extensions of vector-valued functions on finite graphsgraphic
        Miroslav Bačák, Johannes Hertrich, Sebastian Neumayer, Gabriele Steidl
Total variation multiscale estimators for linear inverse problems
        Miguel del Álamo, Axel Munk
Erratum to: Robust 1-bit compressed sensing via hinge loss minimization
Martin Genzel, Alexander Stollenwerk
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