Stochastic Processes

Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC NeurIPS 2011 Switch-Reset Models : Exact and Approximate Inference AISTATS 2011 TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning NeurIPS 2011 The Fixed Points of Off-Policy TD NeurIPS 2011 The Indian Buffet Process: An Introduction and Review JMLR 2011 Variational Gaussian Process Dynamical Systems NeurIPS 2011 A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra AISTATS 2010 A Log-Domain Implementation of the Diffusion Network in Very Large Scale Integration NeurIPS 2010 Approximate inference in continuous time Gaussian-Jump processes NeurIPS 2010 Approximate parameter inference in a stochastic reaction-diffusion model AISTATS 2010 Bayesian variable order Markov models AISTATS 2010 Belief space planning assuming maximum likelihood observations RSS 2010 Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning NeurIPS 2010 Dependent Indian Buffet Processes AISTATS 2010 Dynamic Infinite Relational Model for Time-varying Relational Data Analysis NeurIPS 2010 Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks NeurIPS 2010 Fractionally Predictive Spiking Neurons NeurIPS 2010 Functional form of motion priors in human motion perception NeurIPS 2010 Gaussian sampling by local perturbations NeurIPS 2010 Identifying graph-structured activation patterns in networks NeurIPS 2010 Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices NeurIPS 2010 Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains NeurIPS 2010 Learning Networks of Stochastic Differential Equations NeurIPS 2010 Learning the Structure of Deep Sparse Graphical Models AISTATS 2010 Linear readout from a neural population with partial correlation data NeurIPS 2010