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0910.0610
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Regularization Techniques for Learning with Matrices
4 October 2009
Sham Kakade
Shai Shalev-Shwartz
Ambuj Tewari
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Papers citing
"Regularization Techniques for Learning with Matrices"
50 / 56 papers shown
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Implicit Regularization in Matrix Sensing via Mirror Descent
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Adam Karczmarz
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Variational Policy Gradient Method for Reinforcement Learning with General Utilities
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Online mirror descent and dual averaging: keeping pace in the dynamic case
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Robust Online Learning for Resource Allocation -- Beyond Euclidean Projection and Dynamic Fit
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Generalization Bounds in the Predict-then-Optimize Framework
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Uniform concentration and symmetrization for weak interactions
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Exponentiated Gradient Meets Gradient Descent
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The distribution of the Lasso: Uniform control over sparse balls and adaptive parameter tuning
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Uniform Convergence of Gradients for Non-Convex Learning and Optimization
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Handling Concept Drift via Model Reuse
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Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
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Convergence of Online Mirror Descent
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Spectral Filtering for General Linear Dynamical Systems
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Optimizing Non-decomposable Measures with Deep Networks
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Bounds for Vector-Valued Function Estimation
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Tight Bounds for Approximate Carathéodory and Beyond
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The Learnability of Unknown Quantum Measurements
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Fast Rates by Transferring from Auxiliary Hypotheses
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Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality
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Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets
Dan Garber
Elad Hazan
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An Inequality with Applications to Structured Sparsity and Multitask Dictionary Learning
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Massimiliano Pontil
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A continuous-time approach to online optimization
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Multi-Task Classification Hypothesis Space with Improved Generalization Bounds
Cong Li
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Large-scale Multi-label Learning with Missing Labels
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OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage
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Guaranteed Classification via Regularized Similarity Learning
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Yiming Ying
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On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
Purushottam Kar
Bharath K. Sriperumbudur
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A Generalized Online Mirror Descent with Applications to Classification and Regression
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K. Crammer
Nicolò Cesa-Bianchi
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