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Bayesian Inference

5,566 papers

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Papers

Automatic structured variational inference AISTATS 2021
Improving predictions of Bayesian neural nets via local linearization AISTATS 2021
Scalable Constrained Bayesian Optimization AISTATS 2021
Efficient Computation and Analysis of Distributional Shapley Values AISTATS 2021
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation AISTATS 2021
Mean-Variance Analysis in Bayesian Optimization under Uncertainty AISTATS 2021
Measure Transport with Kernel Stein Discrepancy AISTATS 2021
Inference in Stochastic Epidemic Models via Multinomial Approximations AISTATS 2021
Hyperparameter Transfer Learning with Adaptive Complexity AISTATS 2021
Amortized Bayesian Prototype Meta-learning: A New Probabilistic Meta-learning Approach to Few-shot Image Classification AISTATS 2021
Feedback Coding for Active Learning AISTATS 2021
Shadow Manifold Hamiltonian Monte Carlo AISTATS 2021
Why did the distribution change? AISTATS 2021
Sparse Algorithms for Markovian Gaussian Processes AISTATS 2021
γ-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator AISTATS 2021
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations AISTATS 2021
Reinforcement Learning in Parametric MDPs with Exponential Families AISTATS 2021
No-regret Algorithms for Multi-task Bayesian Optimization AISTATS 2021
Moment-Based Variational Inference for Stochastic Differential Equations AISTATS 2021
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming AISTATS 2021
A Variational Inference Approach to Learning Multivariate Wold Processes AISTATS 2021
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference AISTATS 2021
Context-Specific Likelihood Weighting AISTATS 2021
Misspecification in Prediction Problems and Robustness via Improper Learning AISTATS 2021
Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning AISTATS 2021