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Bayesian Inference

5,566 papers

Papers per year

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Papers

Bayesian Image Classification with Deep Convolutional Gaussian Processes AISTATS 2020
DYNOTEARS: Structure Learning from Time-Series Data AISTATS 2020
GP-VAE: Deep Probabilistic Time Series Imputation AISTATS 2020
Validated Variational Inference via Practical Posterior Error Bounds AISTATS 2020
Fixed-confidence guarantees for Bayesian best-arm identification AISTATS 2020
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory AISTATS 2020
Distributionally Robust Bayesian Quadrature Optimization AISTATS 2020
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC AISTATS 2020
On casting importance weighted autoencoder to an EM algorithm to learn deep generative models AISTATS 2020
Deep Structured Mixtures of Gaussian Processes AISTATS 2020
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization AISTATS 2020
Domain-Liftability of Relational Marginal Polytopes AISTATS 2020
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning AISTATS 2020
Bandit optimisation of functions in the Matérn kernel RKHS AISTATS 2020
Approximate Inference with Wasserstein Gradient Flows AISTATS 2020
On Thompson Sampling for Smoother-than-Lipschitz Bandits AISTATS 2020
Safe-Bayesian Generalized Linear Regression AISTATS 2020
On Pruning for Score-Based Bayesian Network Structure Learning AISTATS 2020
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization AISTATS 2020
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout AISTATS 2020
Robust Optimisation Monte Carlo AISTATS 2020
Fast Markov chain Monte Carlo algorithms via Lie groups AISTATS 2020
Doubly Sparse Variational Gaussian Processes AISTATS 2020
Bayesian Reinforcement Learning via Deep, Sparse Sampling AISTATS 2020
Black-Box Inference for Non-Linear Latent Force Models AISTATS 2020