conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Bayesian Inference

4,821 papers

Papers per year

Papers

An Interpretable and Sample Efficient Deep Kernel for Gaussian Process UAI 2020
Amortized Bayesian Optimization over Discrete Spaces UAI 2020
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation UAI 2020
Deep Sigma Point Processes UAI 2020
Layering-MCMC for Structure Learning in Bayesian Networks UAI 2020
Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models UAI 2020
Graphical continuous Lyapunov models UAI 2020
Dueling Posterior Sampling for Preference-Based Reinforcement Learning UAI 2020
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models UAI 2020
Active Learning of Conditional Mean Embeddings via Bayesian Optimisation UAI 2020
Flexible Prior Elicitation via the Prior Predictive Distribution UAI 2020
Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles UAI 2020
Probabilistic Safety for Bayesian Neural Networks UAI 2020
Distortion estimates for approximate Bayesian inference UAI 2020
Flexible Approximate Inference via Stratified Normalizing Flows UAI 2020
OCEAN: Online Task Inference for Compositional Tasks with Context Adaptation UAI 2020
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving WACV 2020
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference WACV 2020
3D semi-supervised learning with uncertainty-aware multi-view co-training WACV 2020
Exact Gaussian Processes on a Million Data Points NIPS 2019
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates NIPS 2019
Bayesian Optimization under Heavy-tailed Payoffs NIPS 2019
Reliable training and estimation of variance networks NIPS 2019
Differentially Private Markov Chain Monte Carlo NIPS 2019
Universal Boosting Variational Inference NIPS 2019