Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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- Article
Works matching IS 15324435 AND DT 2017 AND VI 18 AND IP 99-123
Results: 25
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2
Probabilistic Line Searches for Stochastic Optimization.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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3
Kernel Partial Least Squares for Stationary Data.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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4
Target Curricula via Selection of Minimum Feature Sets: a Case Study in Boolean Networks.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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5
Bayesian Network Learning via Topological Order.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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6
Regularized Estimation and Testing for High-Dimensional Multi-Block Vector-Autoregressive Models.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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7
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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- Article
8
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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9
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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10
Computational Limits of A Distributed Algorithm for Smoothing Spline.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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11
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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12
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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13
Optimal Dictionary for Least Squares Representation.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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14
Confidence Sets with Expected Sizes for Multiclass Classification.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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15
Second-Order Stochastic Optimization for Machine Learning in Linear Time.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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16
Distributed Stochastic Variance Reduced Gradient Methods by Sampling Extra Data with Replacement.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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17
Document Neural Autoregressive Distribution Estimation.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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18
Approximation Vector Machines for Large-scale Online Learning.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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19
Stability of Controllers for Gaussian Process Dynamics.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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20
Clustering with Hidden Markov Model on Variable Blocks.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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21
Online Learning to Rank with Top-k Feedback.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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22
Accelerating Stochastic Composition Optimization.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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23
Classiffication of Time Sequences using Graphs of Temporal Constraints.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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24
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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25
Efficient Sampling from Time-Varying Log-Concave Distributions.
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- Journal of Machine Learning Research, 2017, v. 18, n. 99-123, p. 1
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