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Empirical Risk Minimization (ERM) is a standard technique in machine learning, where a model is selected by minimizing a loss function over constraint set.
An algorithm for quadratic programming
Marguerite Frank and Philip Wolfe · 1956
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Problem Complexity and Method Efficiency in Optimization
A. S. Nemirovski and D. B. Yudin · 1983
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Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
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An elementary introduction to modern convex geometry
Keith Ball · 1997
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The lasso method for variable selection in the cox model
Robert Tibshirani et al · 1997
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The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, and Jerome Friedman · 2001
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2003
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Amir Beck and Marc Teboulle · 2003
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Higher criticism for detecting sparse heterogeneous mixtures
David Donoho and Jiashun Jin · 2004
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Decoding by linear programming
Emmanuel Candes and Terrance Tao · 2005
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Compressed sensing
David L Donoho · 2006
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Differential privacy
Cynthia Dwork · 2006
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The dantzig selector: Statistical estimation when p is much larger than n
Emmanuel Candes and Terence Tao · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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Logarithmic regret algorithms for strongly convex repeated games
Shai Shalev-Shwartz and Yoram Singer · 2007
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2008
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Simultaneous analysis of lasso and dantzig selector
Peter J Bickel, Ya’acov Ritov, and Alexandre B Tsybakov · 2009
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The differential privacy frontier
Cynthia Dwork · 2009
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Lecture notes on online learning
Alexander Rakhlin · 2009
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Minimax rates of estimation for high-dimensional linear regression over $ \ \backslash ell_q$-balls
G. Raskutti, M. J. Wainwright, and B. Yu · 2009
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Stochastic Convex Optimization
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan · 2009
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Discovering frequent patterns in sensitive data
Raghav Bhaskar, Srivatsan Laxman, Adam Smith, and Abhradeep Thakurta · 2010
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Private convex empirical risk minimization and high-dimensional regression
Daniel Kifer, Adam Smith, and Abhradeep Thakurta · 2012
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Restricted isometry property, rest in peace
Larry Wasserman · 2012
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Concentration inequalities: A nonasymptotic theory of independence
Stéphane Boucheron, Gábor Lugosi, and Pascal Massart · 2013
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Lectures on modern convex optimization
Aharon Ben-Tal and Arkadi Nemirovski · 2013
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Estimation, optimization, and parallelism when data is sparse
John C. Duchi, Michael I. Jordan, and Brendan McMahan · 2013
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Local privacy and statistical minimax rates
John C. Duchi, Michael I. Jordan, and Martin J. Wainwright · 2013
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Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm
Kenneth L. Clarkson · 2010
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Differential privacy under continual observation
Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N. Rothblum · 2010
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Boosting and differential privacy
Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan · 2010
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Composite objective mirror descent
John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, and Ambuj Tewari · 2010
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Trading accuracy for sparsity in optimization problems with sparsity constraints
Shai Shalev-Shwartz, Nathan Srebro, and Tong Zhang · 2010
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Convex games in banach spaces
Karthik Sridharan and Ambuj Tewari · 2010
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Efficient algorithms for privately releasing marginals via convex relaxations
Cynthia Dwork, Aleksandar Nikolov, and Kunal Talwar · 2013
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Revisiting { \{ Frank-Wolfe } \} : Projection-free sparse convex optimization
Martin Jaggi · 2013
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The geometry of differential privacy: The sparse and approximate cases
Aleksandar Nikolov, Kunal Talwar, and Li Zhang · 2013
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Differentially private feature selection via stability arguments, and the robustness of the lasso
Adam Smith and Abhradeep Thakurta · 2013
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Follow the perturbed leader is differentially private with optimal regret guarantees, 2013
Adam Smith and Abhradeep Thakurta · 2013
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Nearly optimal algorithms for private online learning in full-information and bandit settings
Adam Smith and Abhradeep Thakurta · 2013
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Stochastic gradient descent for non-smooth optimization: Convergence results and optimal averaging schemes
Ohad Shamir and Tong Zhang · 2013
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Nearly optimal minimax estimator for high dimensional sparse linear regression
Li Zhang · 2013
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Private empirical risk minimization, revisited
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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Fingerprinting codes and the price of approximate differential privacy
Mark Bun, Jonathan Ullman, and Salil Vadhan · 2014
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Analyze gauss: optimal bounds for privacy-preserving principal component analysis
Cynthia Dwork, Kunal Talwar, Abhradeep Thakurta, and Li Zhang · 2014
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(near) dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Thakurta · 2014
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Private multiplicative weights beyond linear queries
Jonathan Ullman · 2014
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