Stochastic Processes

Fast MCMC Sampling for Markov Jump Processes and Extensions JMLR 2013 Gaussian Process Kernels for Pattern Discovery and Extrapolation ICML 2013 Inferring neural population dynamics from multiple partial recordings of the same neural circuit NeurIPS 2013 Intrinsic Characterization of Dynamic Surfaces CVPR 2013 Learning Stochastic Inverses NeurIPS 2013 Locally Adaptive Bayesian Multivariate Time Series NeurIPS 2013 Lower Bounds and Selectivity of Weak-Consistent Policies in Stochastic Multi-Armed Bandit Problem JMLR 2013 Mixed Optimization for Smooth Functions NeurIPS 2013 Noise-Enhanced Associative Memories NeurIPS 2013 ODE parameter inference using adaptive gradient matching with Gaussian processes AISTATS 2013 Online Learning of Dynamic Parameters in Social Networks NeurIPS 2013 On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations NeurIPS 2013 Parsing epileptic events using a Markov switching process model for correlated time series ICML 2013 Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models JMLR 2013 Predictive PAC Learning and Process Decompositions NeurIPS 2013 Predictive Simulation Framework of Stochastic Diffusion Model for Identifying Top-K Influential Nodes ACML 2013 Probabilistic Movement Primitives NeurIPS 2013 Projecting Ising Model Parameters for Fast Mixing NeurIPS 2013 Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs JMLR 2013 Real-Time Inference for a Gamma Process Model of Neural Spiking NeurIPS 2013 Recovering Line-Networks in Images by Junction-Point Processes CVPR 2013 Segregating Event Streams and Noise with a Markov Renewal Process Model JMLR 2013 Slice Sampling Particle Belief Propagation ICCV 2013 Solving inverse problem of Markov chain with partial observations NeurIPS 2013 Sparse Subspace Denoising for Image Manifolds CVPR 2013