Bayesian Inference

Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds NeurIPS 2020 Optimizing Dynamic Structures with Bayesian Generative Search ICML 2020 Order-Dependent Event Models for Agent Interactions IJCAI 2020 PAC-Bayes Analysis Beyond the Usual Bounds NeurIPS 2020 PAC-Bayesian Bound for the Conditional Value at Risk NeurIPS 2020 PAC-Bayes Learning Bounds for Sample-Dependent Priors NeurIPS 2020 Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning AAAI 2020 Parametric Gaussian Process Regressors ICML 2020 Partial Multi-Label Learning with Noisy Label Identification AAAI 2020 Particle Filter Recurrent Neural Networks AAAI 2020 PEP: Parameter Ensembling by Perturbation NeurIPS 2020 Pessimism About Unknown Unknowns Inspires Conservatism COLT 2020 Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning ICLR 2020 Planning from Images with Deep Latent Gaussian Process Dynamics L4DC 2020 Point process models for sequence detection in high-dimensional neural spike trains NeurIPS 2020 Posterior-Guided Neural Architecture Search AAAI 2020 Posterior Re-calibration for Imbalanced Datasets NeurIPS 2020 Posterior sampling strategies based on discretized stochastic differential equations for machine learning applications JMLR 2020 Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles UAI 2020 Prediction of High Risk of Deviations in Home Care Deliveries PGM 2020 Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm NeurIPS 2020 Prior-aware Composition Inference for Spectral Topic Models AISTATS 2020 Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption AAAI 2020 Privacy-Preserving Gradient Boosting Decision Trees AAAI 2020 Private Bayesian Persuasion with Sequential Games AAAI 2020