Bayesian & Probabilistic

The Sample Complexity of Self-Verifying Bayesian Active Learning AISTATS 2011 Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction NeurIPS 2011 Uniqueness of Belief Propagation on Signed Graphs NeurIPS 2011 Variational Gaussian Process Dynamical Systems NeurIPS 2011 Variational Learning for Recurrent Spiking Networks NeurIPS 2011 A Bayesian Approach to Concept Drift NeurIPS 2010 A Bayesian Framework for Figure-Ground Interpretation NeurIPS 2010 Accounting for network effects in neuronal responses using L1 regularized point process models NeurIPS 2010 A Log-Domain Implementation of the Diffusion Network in Very Large Scale Integration NeurIPS 2010 A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping AISTATS 2010 An Approximate Inference Approach to Temporal Optimization in Optimal Control NeurIPS 2010 An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models ACML 2010 A New Probabilistic Model for Rank Aggregation NeurIPS 2010 A POMDP Extension with Belief-dependent Rewards NeurIPS 2010 Approximate Inference by Compilation to Arithmetic Circuits NeurIPS 2010 Approximate inference in continuous time Gaussian-Jump processes NeurIPS 2010 Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation JMLR 2010 A rational decision making framework for inhibitory control NeurIPS 2010 A Study of Approximate Inference in Probabilistic Relational Models ACML 2010 A unified model of short-range and long-range motion perception NeurIPS 2010 Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting NeurIPS 2010 A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation AISTATS 2010 Batch Bayesian Optimization via Simulation Matching NeurIPS 2010 Bayesian Action-Graph Games NeurIPS 2010 Bayesian Gaussian Process Latent Variable Model AISTATS 2010