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Bayesian Learning

1663 directly classified papers

Papers per year

Papers

Bayesian Hierarchical Words Representation Learning ACL 2020
Scalable Approximate MCMC Algorithms for the Horseshoe Prior JMLR 2020
Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysis IJCAI 2020
Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation NIPS 2020
Efficient Low Rank Gaussian Variational Inference for Neural Networks NIPS 2020
Greedy inference with structure-exploiting lazy maps NIPS 2020
BayesRace: Learning to race autonomously using prior experience CORL 2020
Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics L4DC 2020
Automated Scalable Bayesian Inference via Hilbert Coresets JMLR 2019
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness NIPS 2019
Chasing Ghosts: Instruction Following as Bayesian State Tracking NIPS 2019
Automatic Bayesian Density Analysis AAAI 2019
Multi-task Learning for Aggregated Data using Gaussian Processes NIPS 2019
Nonparametric Bayesian Aggregation for Massive Data JMLR 2019
Offline Contextual Bayesian Optimization NIPS 2019
Bayesian Optimization with Unknown Search Space NIPS 2019
Differentially Private Bayesian Linear Regression NIPS 2019
Bayesian Generative Active Deep Learning ICML 2019
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop Planning IJCAI 2019
Unobserved Is Not Equal to Non-existent: Using Gaussian Processes to Infer Immediate Rewards Across Contexts IJCAI 2019
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations ICML 2019
End-to-End Probabilistic Inference for Nonstationary Audio Analysis ICML 2019
OpenMarkov, an Open-Source Tool for Probabilistic Graphical Models IJCAI 2019
Cutset Bayesian Networks: A New Representation for Learning Rao-Blackwellised Graphical Models IJCAI 2019
Bayesian Parameter Estimation for Nonlinear Dynamics Using Sensitivity Analysis IJCAI 2019