Artificial Intelligence

Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities NeurIPS 2011 Learning Rules from Incomplete Examples via Implicit Mention Models ACML 2011 Learning to Agglomerate Superpixel Hierarchies NeurIPS 2011 Learning to Learn with Compound HD Models NeurIPS 2011 Learning to Search Efficiently in High Dimensions NeurIPS 2011 Learning Transformation Models for Ranking and Survival Analysis JMLR 2011 Learning unbelievable probabilities NeurIPS 2011 Load Balancing for Mobility-on-Demand Systems RSS 2011 Logistic Stick-Breaking Process JMLR 2011 Lower Bounds for Passive and Active Learning NeurIPS 2011 Machine Learning Markets AISTATS 2011 Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds NeurIPS 2011 MAP Inference for Bayesian Inverse Reinforcement Learning NeurIPS 2011 Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning NeurIPS 2011 Maximum Covariance Unfolding : Manifold Learning for Bimodal Data NeurIPS 2011 Maximum Margin Multi-Instance Learning NeurIPS 2011 Message-Passing for Approximate MAP Inference with Latent Variables NeurIPS 2011 Minimax Algorithm for Learning Rotations COLT 2011 Missing Information Impediments to Learnability COLT 2011 Mixed Cumulative Distribution Networks AISTATS 2011 Model-Based Proprioceptive State Estimation for Spring-Mass Running RSS 2011 Modelling Genetic Variations using Fragmentation-Coagulation Processes NeurIPS 2011 Monotone multi-armed bandit allocations COLT 2011 Monte Carlo Pose Estimation with Quaternion Kernels and the Bingham Distribution RSS 2011 Monte Carlo Value Iteration with Macro-Actions NeurIPS 2011