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Stochastic Processes

2667 directly classified papers

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Papers

Automated Inference with Adaptive Batches AISTATS 2017
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems NIPS 2017
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering NIPS 2017
Collapsed variational Bayes for Markov jump processes NIPS 2017
Scalable Variational Inference for Dynamical Systems NIPS 2017
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible ICML 2017
Random projection design for scalable implicit smoothing of randomly observed stochastic processes AISTATS 2017
Distribution of Gaussian Process Arc Lengths AISTATS 2017
Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds AISTATS 2017
Stochastic Gradient Monomial Gamma Sampler ICML 2017
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning ICML 2017
Stabilized Sparse Online Learning for Sparse Data JMLR 2017
Stochastic Gradient Descent as Approximate Bayesian Inference JMLR 2017
Variational Particle Approximations JMLR 2017
Max-value Entropy Search for Efficient Bayesian Optimization ICML 2017
Fast Bayesian Intensity Estimation for the Permanental Process ICML 2017
Fractional Langevin Monte Carlo: Exploring Levy Driven Stochastic Differential Equations for Markov Chain Monte Carlo ICML 2017
Stochastic Bouncy Particle Sampler ICML 2017
Improving Gibbs Sampler Scan Quality with DoGS ICML 2017
Stochastic Gradient MCMC Methods for Hidden Markov Models ICML 2017
Algorithmic Stability and Hypothesis Complexity ICML 2017
Learning Deep Latent Gaussian Models with Markov Chain Monte Carlo ICML 2017
Measuring Sample Quality with Kernels ICML 2017
Zonotope Hit-and-run for Efficient Sampling from Projection DPPs ICML 2017
Probabilistic Path Hamiltonian Monte Carlo ICML 2017