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Anastasios Kyrillidis | Oluwasanmi Koyejo | Rajiv Khanna | Jacky Y. Zhang | Anastasios Kyrillidis | O. Koyejo | Rekha Khanna | Oluwasanmi Koyejo
[1] Michael I. Jordan,et al. Sampling can be faster than optimization , 2018, Proceedings of the National Academy of Sciences.
[2] Trevor Campbell,et al. Sparse Variational Inference: Bayesian Coresets from Scratch , 2019, NeurIPS.
[3] Deanna Needell,et al. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.
[4] Mike E. Davies,et al. Normalized Iterative Hard Thresholding: Guaranteed Stability and Performance , 2010, IEEE Journal of Selected Topics in Signal Processing.
[5] Volkan Cevher,et al. Recipes on hard thresholding methods , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).
[6] Thomas Blumensath,et al. Accelerated iterative hard thresholding , 2012, Signal Process..
[7] Edward I. George,et al. Bayes and big data: the consensus Monte Carlo algorithm , 2016, Big Data and Information Theory.
[8] Mike E. Davies,et al. Iterative Hard Thresholding for Compressed Sensing , 2008, ArXiv.
[9] Euhanna Ghadimi,et al. Global convergence of the Heavy-ball method for convex optimization , 2014, 2015 European Control Conference (ECC).
[10] Olgica Milenkovic,et al. Subspace Pursuit for Compressive Sensing Signal Reconstruction , 2008, IEEE Transactions on Information Theory.
[11] Max Welling,et al. Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget , 2013, ICML 2014.
[12] Volkan Cevher,et al. Model-Based Compressive Sensing , 2008, IEEE Transactions on Information Theory.
[13] Philip Wolfe,et al. An algorithm for quadratic programming , 1956 .
[14] Volkan Cevher,et al. Matrix Recipes for Hard Thresholding Methods , 2012, Journal of Mathematical Imaging and Vision.
[15] Simon Foucart,et al. Hard Thresholding Pursuit: An Algorithm for Compressive Sensing , 2011, SIAM J. Numer. Anal..
[16] Trevor Campbell,et al. Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent , 2018, ICML.
[17] Tong Zhang,et al. Gradient Hard Thresholding Pursuit , 2018, J. Mach. Learn. Res..
[18] S. Frick,et al. Compressed Sensing , 2014, Computer Vision, A Reference Guide.
[19] Ahn,et al. Bayesian posterior sampling via stochastic gradient Fisher scoring Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring , 2012 .
[20] Bhiksha Raj,et al. Greedy sparsity-constrained optimization , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).
[21] S. Sanghavi,et al. A general framework for high-dimensional estimation in the presence of incoherence , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[22] Michael I. Jordan,et al. Variational Consensus Monte Carlo , 2015, NIPS.
[23] Anastasios Kyrillidis,et al. IHT dies hard: Provable accelerated Iterative Hard Thresholding , 2017, AISTATS.
[24] Ryan P. Adams,et al. Firefly Monte Carlo: Exact MCMC with Subsets of Data , 2014, UAI.
[25] Y. Nesterov. A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .
[26] Anastasios Kyrillidis,et al. Learning Sparse Distributions using Iterative Hard Thresholding , 2019, NeurIPS.
[27] Oluwasanmi Koyejo,et al. On Prior Distributions and Approximate Inference for Structured Variables , 2014, NIPS.
[28] Tong Zhang,et al. Trading Accuracy for Sparsity in Optimization Problems with Sparsity Constraints , 2010, SIAM J. Optim..
[29] Deanna Needell,et al. Linear Convergence of Stochastic Iterative Greedy Algorithms With Sparse Constraints , 2014, IEEE Transactions on Information Theory.
[30] Joel A. Tropp,et al. Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.
[31] Volkan Cevher,et al. WASP: Scalable Bayes via barycenters of subset posteriors , 2015, AISTATS.
[32] E. Candès. The restricted isometry property and its implications for compressed sensing , 2008 .
[33] Volkan Cevher,et al. Structured Sparsity: Discrete and Convex approaches , 2015, ArXiv.
[34] Gunnar Rätsch,et al. Boosting Black Box Variational Inference , 2018, NeurIPS.
[35] Oluwasanmi Koyejo,et al. Information Projection and Approximate Inference for Structured Sparse Variables , 2016, AISTATS.
[36] Andre Wibisono,et al. Streaming Variational Bayes , 2013, NIPS.
[37] Inderjit S. Dhillon,et al. Structured Sparse Regression via Greedy Hard Thresholding , 2016, NIPS.
[38] Martin Jaggi,et al. Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization , 2013, ICML.
[39] Tuo Zhao,et al. Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning , 2016, ICML.
[40] Gunnar Rätsch,et al. Boosting Variational Inference: an Optimization Perspective , 2017, AISTATS.
[41] Yee Whye Teh,et al. Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.
[42] Chong Wang,et al. Stochastic variational inference , 2012, J. Mach. Learn. Res..
[43] Volkan Cevher,et al. Group-Sparse Model Selection: Hardness and Relaxations , 2013, IEEE Transactions on Information Theory.
[44] Trevor Campbell,et al. Coresets for Scalable Bayesian Logistic Regression , 2016, NIPS.
[45] Prateek Jain,et al. On Iterative Hard Thresholding Methods for High-dimensional M-Estimation , 2014, NIPS.
[46] R. DeVore,et al. A Simple Proof of the Restricted Isometry Property for Random Matrices , 2008 .
[47] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[48] Trevor Campbell,et al. Automated Scalable Bayesian Inference via Hilbert Coresets , 2017, J. Mach. Learn. Res..