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Bayesian Inference

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Papers

Model-based Reinforcement Learning for Continuous Control with Posterior Sampling ICML 2021
Streaming Bayesian Deep Tensor Factorization ICML 2021
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design ICML 2021
Variational Data Assimilation with a Learned Inverse Observation Operator ICML 2021
Bayesian Quadrature on Riemannian Data Manifolds ICML 2021
On the difficulty of unbiased alpha divergence minimization ICML 2021
Differentially Private Quantiles ICML 2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions ICML 2021
Rate-Distortion Analysis of Minimum Excess Risk in Bayesian Learning ICML 2021
STRODE: Stochastic Boundary Ordinary Differential Equation ICML 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning ICML 2021
What Are Bayesian Neural Network Posteriors Really Like? ICML 2021
Instance-Optimal Compressed Sensing via Posterior Sampling ICML 2021
Objective Bound Conditional Gaussian Process for Bayesian Optimization ICML 2021
Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding ICML 2021
Marginalized Stochastic Natural Gradients for Black-Box Variational Inference ICML 2021
Isometric Gaussian Process Latent Variable Model for Dissimilarity Data ICML 2021
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits ICML 2021
A Differentiable Point Process with Its Application to Spiking Neural Networks ICML 2021
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes ICML 2021
Bias-Robust Bayesian Optimization via Dueling Bandits ICML 2021
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? ICML 2021
Offline Reinforcement Learning with Fisher Divergence Critic Regularization ICML 2021
Differentially Private Bayesian Inference for Generalized Linear Models ICML 2021
Meta-Thompson Sampling ICML 2021