Bayesian & Probabilistic

Conditional Temporal Neural Processes with Covariance Loss ICML 2021 Conditional Variational Capsule Network for Open Set Recognition ICCV 2021 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 Context-dependent Networks in Multivariate Time Series: Models, Methods, and Risk Bounds in High Dimensions JMLR 2021 Context-Specific Likelihood Weighting AISTATS 2021 Contextual Dropout: An Efficient Sample-Dependent Dropout Module ICLR 2021 Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors AISTATS 2021 Continuous Latent Process Flows NeurIPS 2021 Contraction L1-Adaptive Control using Gaussian Processes L4DC 2021 Contrastive Active Inference NeurIPS 2021 Contrastively Disentangled Sequential Variational Autoencoder NeurIPS 2021 Contrastive Neural Processes for Self-Supervised Learning ACML 2021 Convergence behavior of belief propagation: estimating regions of attraction via Lyapunov functions UAI 2021 Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness JMLR 2021 Cost-aware Graph Generation: A Deep Bayesian Optimization Approach AAAI 2021 Counterfactual Mean Embeddings JMLR 2021 Coupling Macro-Sector-Micro Financial Indicators for Learning Stock Representations with Less Uncertainty AAAI 2021 DAGSurv: Directed Ayclic Graph Based Survival Analysis Using Deep Neural Networks ACML 2021 Dangers of Bayesian Model Averaging under Covariate Shift NeurIPS 2021 Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo JMLR 2021 Deconditional Downscaling with Gaussian Processes NeurIPS 2021 Deep Bucket Elimination IJCAI 2021 DeepGEM: Generalized Expectation-Maximization for Blind Inversion NeurIPS 2021