Stochastic Processes

Learning Graphs with a Few Hubs ICML 2014 Learning without concentration COLT 2014 Linear and Parallel Learning of Markov Random Fields ICML 2014 Lower Bounds for the Gibbs Sampler over Mixtures of Gaussians ICML 2014 Memory (and Time) Efficient Sequential Monte Carlo ICML 2014 Modeling Correlated Arrival Events with Latent Semi-Markov Processes ICML 2014 On Sparse Gaussian Chain Graph Models NeurIPS 2014 Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics AISTATS 2014 Projecting Markov Random Field Parameters for Fast Mixing NeurIPS 2014 Randomized Experimental Design for Causal Graph Discovery NeurIPS 2014 Rectangular Tiling Process ICML 2014 Spherical Hamiltonian Monte Carlo for Constrained Target Distributions ICML 2014 Analyzing Hogwild Parallel Gaussian Gibbs Sampling NeurIPS 2013 Annealing between distributions by averaging moments NeurIPS 2013 Approximate Gaussian process inference for the drift function in stochastic differential equations NeurIPS 2013 Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions NeurIPS 2013 Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC NeurIPS 2013 Blind Signal Separation in the Presence of Gaussian Noise COLT 2013 Computing the Stationary Distribution Locally NeurIPS 2013 Fast Determinantal Point Process Sampling with Application to Clustering NeurIPS 2013 How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal NeurIPS 2013 Locally Adaptive Bayesian Multivariate Time Series NeurIPS 2013 Modeling Information Propagation with Survival Theory ICML 2013 Predictive PAC Learning and Process Decompositions NeurIPS 2013 Sensor Selection in High-Dimensional Gaussian Trees with Nuisances NeurIPS 2013