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Bayesian Inference

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Papers

Continual Repeated Annealed Flow Transport Monte Carlo ICML 2022
POEM: Out-of-Distribution Detection with Posterior Sampling ICML 2022
Variational Inference for Infinitely Deep Neural Networks ICML 2022
Constrained Discrete Black-Box Optimization using Mixed-Integer Programming ICML 2022
Validating Causal Inference Methods ICML 2022
On the Practicality of Deterministic Epistemic Uncertainty ICML 2022
Spectral Representation of Robustness Measures for Optimization Under Input Uncertainty ICML 2022
A Unified View on PAC-Bayes Bounds for Meta-Learning ICML 2022
Function-space Inference with Sparse Implicit Processes ICML 2022
Continual Learning via Sequential Function-Space Variational Inference ICML 2022
A General Recipe for Likelihood-free Bayesian Optimization ICML 2022
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders ICML 2022
Distributionally-Aware Kernelized Bandit Problems for Risk Aversion ICML 2022
Sequential and Parallel Constrained Max-value Entropy Search via Information Lower Bound ICML 2022
Efficient Distributionally Robust Bayesian Optimization with Worst-case Sensitivity ICML 2022
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses ICML 2022
A Completely Tuning-Free and Robust Approach to Sparse Precision Matrix Estimation ICML 2022
Path-Gradient Estimators for Continuous Normalizing Flows ICML 2022
Nonparametric Embeddings of Sparse High-Order Interaction Events ICML 2022
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization ICML 2022
Easy Variational Inference for Categorical Models via an Independent Binary Approximation ICML 2022
Variational nearest neighbor Gaussian process ICML 2022
Importance Weighted Kernel Bayes’ Rule ICML 2022
Accurate Quantization of Measures via Interacting Particle-based Optimization ICML 2022
Langevin Monte Carlo for Contextual Bandits ICML 2022