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Optimization

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Papers

Learning Polyhedral Classifiers Using Logistic Function ACML 2010
Ellipsoidal Support Vector Machines ACML 2010
Optimal Allocation Strategies for the Dark Pool Problem AISTATS 2010
On the Impact of Kernel Approximation on Learning Accuracy AISTATS 2010
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines AISTATS 2010
Semi-Supervised Learning via Generalized Maximum Entropy AISTATS 2010
Boosted Optimization for Network Classification AISTATS 2010
Learning Nonlinear Dynamic Models from Non-sequenced Data AISTATS 2010
Learning Bayesian Network Structure using LP Relaxations AISTATS 2010
Structured Sparse Principal Component Analysis AISTATS 2010
Fast Active-set-type Algorithms for $l1$-regularized Linear Regression AISTATS 2010
Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach AISTATS 2010
The Feature Selection Path in Kernel Methods AISTATS 2010
Descent Methods for Tuning Parameter Refinement AISTATS 2010
Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net AISTATS 2010
Exploiting Feature Covariance in High-Dimensional Online Learning AISTATS 2010
Inductive Principles for Restricted Boltzmann Machine Learning AISTATS 2010
Exploiting Within-Clique Factorizations in Junction-Tree Algorithms AISTATS 2010
A Regularization Approach to Nonlinear Variable Selection AISTATS 2010
Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials AISTATS 2010
Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching AISTATS 2010
On the Convergence Properties of Contrastive Divergence AISTATS 2010
Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation AISTATS 2010
Bundle Methods for Regularized Risk Minimization JMLR 2010
Approximate Tree Kernels JMLR 2010