Optimization

No-regret Algorithms for Online Convex Programs NeurIPS 2006 Optimal Rules for Programmed Stochastic Self-Assembly RSS 2006 Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems JMLR 2006 Parameter Expanded Variational Bayesian Methods NeurIPS 2006 QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines JMLR 2006 Qualitative Hybrid Control of Dynamic Bipedal Walking RSS 2006 Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension NeurIPS 2006 Real-time adaptive information-theoretic optimization of neurophysiology experiments NeurIPS 2006 Simplifying Mixture Models through Function Approximation NeurIPS 2006 Sparse Boosting JMLR 2006 Sparse Representation for Signal Classification NeurIPS 2006 Stability of K-Means Clustering NeurIPS 2006 Step Size Adaptation in Reproducing Kernel Hilbert Space JMLR 2006 Streamwise Feature Selection JMLR 2006 Structured Prediction, Dual Extragradient and Bregman Projections JMLR 2006 Support Vector Machines on a Budget NeurIPS 2006 The Interplay of Optimization and Machine Learning Research JMLR 2006 The Robustness-Performance Tradeoff in Markov Decision Processes NeurIPS 2006 Training Conditional Random Fields for Maximum Labelwise Accuracy NeurIPS 2006 Active Coevolutionary Learning of Deterministic Finite Automata JMLR 2005 A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs JMLR 2005 Clustering with Bregman Divergences JMLR 2005 Convergence Theorems for Generalized Alternating Minimization Procedures JMLR 2005 Core Vector Machines: Fast SVM Training on Very Large Data Sets JMLR 2005 Efficient Margin Maximizing with Boosting JMLR 2005