Stochastic Processes

True Nonlinear Dynamics from Incomplete Networks AAAI 2020 Variational Inference for Sparse Gaussian Process Modulated Hawkes Process AAAI 2020 Wasserstein Control of Mirror Langevin Monte Carlo COLT 2020 Why Are Learned Indexes So Effective? ICML 2020 A Unified Mathematical Approach for Foraging and Construction Systems in a 1,000,000 Robot Swarm IJCAI 2019 Band-Limited Gaussian Processes: The Sinc Kernel NeurIPS 2019 CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling AAAI 2019 Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory NeurIPS 2019 Concentration of risk measures: A Wasserstein distance approach NeurIPS 2019 Continuous-time Models for Stochastic Optimization Algorithms NeurIPS 2019 DPPy: DPP Sampling with Python JMLR 2019 Early-Stopping of Scattering Pattern Observation with Bayesian Modeling AAAI 2019 Elliptical Perturbations for Differential Privacy NeurIPS 2019 Estimating Convergence of Markov chains with L-Lag Couplings NeurIPS 2019 Estimating the Mixing Time of Ergodic Markov Chains COLT 2019 Hamiltonian Monte Carlo with Energy Conserving Subsampling JMLR 2019 Importance Sampling Policy Evaluation with an Estimated Behavior Policy ICML 2019 Learning low-dimensional state embeddings and metastable clusters from time series data NeurIPS 2019 Learning Multiple Markov Chains via Adaptive Allocation NeurIPS 2019 Log-concave sampling: Metropolis-Hastings algorithms are fast JMLR 2019 McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds NeurIPS 2019 Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes MLHC 2019 Modelling Stopping Criteria for Search Results using Poisson Processes EMNLP 2019 MRI k-Space Motion Artefact Augmentation: Model Robustness and Task-Specific Uncertainty MIDL 2019 Neural Jump Stochastic Differential Equations NeurIPS 2019