Stochastic Processes

Cross-sectional Learning of Extremal Dependence among Financial Assets NeurIPS 2019 Daytime Sleepiness Level Prediction Using Respiratory Information IJCAI 2019 Deep Exploration via Randomized Value Functions JMLR 2019 Deep Gaussian Processes with Importance-Weighted Variational Inference ICML 2019 DeepOBS: A Deep Learning Optimizer Benchmark Suite ICLR 2019 Deep Optimal Stopping JMLR 2019 Deep Random Splines for Point Process Intensity Estimation of Neural Population Data NeurIPS 2019 DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis AAAI 2019 Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control IJCAI 2019 Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate NeurIPS 2019 Differentially Private Markov Chain Monte Carlo NeurIPS 2019 Discovering Temporal Patterns from Insurance Interaction Data AAAI 2019 Distributed Community Detection via Metastability of the 2-Choices Dynamics AAAI 2019 Distribution-Interpolation Trade off in Generative Models ICLR 2019 Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation NeurIPS 2019 DPPy: DPP Sampling with Python JMLR 2019 Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function AISTATS 2019 Efficient Diversified Mini-Batch Selection using Variable High-layer Features ACML 2019 Energy-Inspired Models: Learning with Sampler-Induced Distributions NeurIPS 2019 Ensemble Machine Learning for Estimating Fetal Weight at Varying Gestational Age AAAI 2019 Estimating Convergence of Markov chains with L-Lag Couplings NeurIPS 2019 Estimating the Mixing Time of Ergodic Markov Chains COLT 2019 Exploring k out of Top ρ Fraction of Arms in Stochastic Bandits AISTATS 2019 Exponentially convergent stochastic k-PCA without variance reduction NeurIPS 2019 Fast and Accurate Stochastic Gradient Estimation NeurIPS 2019