Bayesian Learning

An Empirical Evaluation of Thompson Sampling NeurIPS 2011 An ideal observer model for identifying the reference frame of objects NeurIPS 2011 Approximate inference for the loss-calibrated Bayesian AISTATS 2011 Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints NeurIPS 2011 Bayesian Bias Mitigation for Crowdsourcing NeurIPS 2011 Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery NeurIPS 2011 Contextual Gaussian Process Bandit Optimization NeurIPS 2011 Convergence Rates of Efficient Global Optimization Algorithms JMLR 2011 Crowdclustering NeurIPS 2011 EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning NeurIPS 2011 Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron NeurIPS 2011 Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference AISTATS 2011 Gaussian process modulated renewal processes NeurIPS 2011 Gaussian Process Training with Input Noise NeurIPS 2011 Generalized Beta Mixtures of Gaussians NeurIPS 2011 Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent NeurIPS 2011 Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices NeurIPS 2011 Inference in continuous-time change-point models NeurIPS 2011 Inferring Interaction Networks using the IBP applied to microRNA Target Prediction NeurIPS 2011 Infinite Latent SVM for Classification and Multi-task Learning NeurIPS 2011 Kernel Bayes' Rule NeurIPS 2011 Learning to Learn with Compound HD Models NeurIPS 2011 Learning unbelievable probabilities NeurIPS 2011 Logistic Stick-Breaking Process JMLR 2011 MAP Inference for Bayesian Inverse Reinforcement Learning NeurIPS 2011