Bayesian Inference

Hamiltonian Monte Carlo with Energy Conserving Subsampling JMLR 2019 Harmonizable mixture kernels with variational Fourier features AISTATS 2019 Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness IJCAI 2019 High Dimensional Bayesian Optimization via Supervised Dimension Reduction IJCAI 2019 Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets IJCAI 2019 Hybrid Models with Deep and Invertible Features ICML 2019 Hyperparameter Learning via Distributional Transfer NeurIPS 2019 Hyper-parameter Tuning under a Budget Constraint IJCAI 2019 Hyperprior Induced Unsupervised Disentanglement of Latent Representations AAAI 2019 Identification, Interpretability, and Bayesian Word Embeddings NAACL 2019 Implicit Deep Latent Variable Models for Text Generation EMNLP 2019 Implicit Posterior Variational Inference for Deep Gaussian Processes NeurIPS 2019 Importance Weighted Hierarchical Variational Inference NeurIPS 2019 Improving Quadrature for Constrained Integrands AISTATS 2019 Incorporating Context and External Knowledge for Pronoun Coreference Resolution NAACL 2019 Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression IJCAI 2019 Information asymmetry in KL-regularized RL ICLR 2019 Information-Theoretic Confidence Bounds for Reinforcement Learning NeurIPS 2019 Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates NeurIPS 2019 Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process NAACL 2019 Interleave Variational Optimization with Monte Carlo Sampling: A Tale of Two Approximate Inference Paradigms AAAI 2019 Interpretable Almost-Exact Matching for Causal Inference AISTATS 2019 Interpretable Word Embeddings via Informative Priors EMNLP 2019 Interpreting Black Box Predictions using Fisher Kernels AISTATS 2019 Invertible Convolutional Flow NeurIPS 2019