approximate inference

Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms AAAI 2019 Learning Diverse Bayesian Networks AAAI 2019 Lifted Message Passing for Hybrid Probabilistic Inference IJCAI 2019 One-Shot Marginal MAP Inference in Markov Random Fields UAI 2019 On Lifted Inference Using Neural Embeddings AAAI 2019 Spectral Approximate Inference ICML 2019 Thompson Sampling and Approximate Inference NeurIPS 2019 Train and Test Tightness of LP Relaxations in Structured Prediction JMLR 2019 Accelerating Cross-Validation in Multinomial Logistic Regression with ℓ_1-Regularization JMLR 2018 Bucket Renormalization for Approximate Inference ICML 2018 Collective Entity Disambiguation with Structured Gradient Tree Boosting NAACL 2018 Gradient Estimators for Implicit Models ICLR 2018 Graphical model inference: Sequential Monte Carlo meets deterministic approximations NeurIPS 2018 Lifted Weighted Mini-Bucket NeurIPS 2018 Optimality of Approximate Inference Algorithms on Stable Instances AISTATS 2018 Recasting Gradient-Based Meta-Learning as Hierarchical Bayes ICLR 2018 Scalable approximate Bayesian inference for particle tracking data ICML 2018 Scaling the Poisson GLM to massive neural datasets through polynomial approximations NeurIPS 2018 Sound Abstraction and Decomposition of Probabilistic Programs ICML 2018 Stein Variational Gradient Descent Without Gradient ICML 2018 Streamlining Variational Inference for Constraint Satisfaction Problems NeurIPS 2018 Variational Bayesian dropout: pitfalls and fixes ICML 2018 A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI ICML 2017 A Learning Error Analysis for Structured Prediction with Approximate Inference NeurIPS 2017 A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation JMLR 2017