Bayesian & Probabilistic

Conformal Bayesian Computation NeurIPS 2021 Consistency of Gaussian Process Regression in Metric Spaces JMLR 2021 Context-Aware Online Collective Inference for Templated Graphical Models ICML 2021 Continuous-time edge modelling using non-parametric point processes NeurIPS 2021 Contrastive Divergence Learning is a Time Reversal Adversarial Game ICLR 2021 Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness JMLR 2021 Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization ICLR 2021 Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities NeurIPS 2021 Cost-aware Graph Generation: A Deep Bayesian Optimization Approach AAAI 2021 Counterexample Guided RL Policy Refinement Using Bayesian Optimization NeurIPS 2021 Coupled Gradient Estimators for Discrete Latent Variables NeurIPS 2021 Couplings for Multinomial Hamiltonian Monte Carlo AISTATS 2021 Credal Self-Supervised Learning NeurIPS 2021 Cross-structural Factor-topic Model: Document Analysis with Sophisticated Covariates ACML 2021 Deconditional Downscaling with Gaussian Processes NeurIPS 2021 De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation ACL 2021 Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging CVPR 2021 Deep kernel processes ICML 2021 Deep Probabilistic Canonical Correlation Analysis AAAI 2021 De-randomizing MCMC dynamics with the diffusion Stein operator NeurIPS 2021 Detecting Anomalous Event Sequences with Temporal Point Processes NeurIPS 2021 DiBS: Differentiable Bayesian Structure Learning NeurIPS 2021 Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NeurIPS 2021 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport ICML 2021 Differentially Private Regression and Classification with Sparse Gaussian Processes JMLR 2021