Stochastic Processes

Switch-Reset Models : Exact and Approximate Inference AISTATS 2011 A novel family of non-parametric cumulative based divergences for point processes NeurIPS 2010 Approximate inference in continuous time Gaussian-Jump processes NeurIPS 2010 Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning NeurIPS 2010 Copula Processes NeurIPS 2010 Fractionally Predictive Spiking Neurons NeurIPS 2010 Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices NeurIPS 2010 REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization AISTATS 2010 Risk Bounds for Levy Processes in the PAC-Learning Framework AISTATS 2010 Throttling Poisson Processes NeurIPS 2010 A General Projection Property for Distribution Families NeurIPS 2009 A Stochastic approximation method for inference in probabilistic graphical models NeurIPS 2009 Construction of Nonparametric Bayesian Models from Parametric Bayes Equations NeurIPS 2009 Know Thy Neighbour: A Normative Theory of Synaptic Depression NeurIPS 2009 Maximum likelihood trajectories for continuous-time Markov chains NeurIPS 2009 Non-stationary continuous dynamic Bayesian networks NeurIPS 2009 Perceptual Multistability as Markov Chain Monte Carlo Inference NeurIPS 2009 Riffled Independence for Ranked Data NeurIPS 2009 Sensitivity analysis in HMMs with application to likelihood maximization NeurIPS 2009 Short-Term Depression in VLSI Stochastic Synapse NeurIPS 2008 The Gaussian Process Density Sampler NeurIPS 2008 The Mondrian Process NeurIPS 2008 Collective Inference on Markov Models for Modeling Bird Migration NeurIPS 2007 Inferring Elapsed Time from Stochastic Neural Processes NeurIPS 2007 Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains NeurIPS 2007