Variational Inference

Monte Carlo Variational Auto-Encoders ICML 2021 Moreau-Yosida f-divergences ICML 2021 Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis JMLR 2021 Neural Relation Inference for Multi-dimensional Temporal Point Processes via Message Passing Graph IJCAI 2021 On Empirical Bayes Variational Autoencoder: An Excess Risk Bound COLT 2021 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes ICML 2021 On the difficulty of unbiased alpha divergence minimization ICML 2021 On Variational Inference in Biclustering Models ICML 2021 Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation ICML 2021 Probabilistic Tensor Decomposition of Neural Population Spiking Activity NeurIPS 2021 Probabilistic Transformer For Time Series Analysis NeurIPS 2021 Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent NeurIPS 2021 PSD Representations for Effective Probability Models NeurIPS 2021 Pseudo-Marginal Hamiltonian Monte Carlo JMLR 2021 Quantifying Variational Approximation for Log-Partition Function COLT 2021 Quasi-Monte Carlo Quasi-Newton in Variational Bayes JMLR 2021 Sampling with Trusthworthy Constraints: A Variational Gradient Framework NeurIPS 2021 Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network ICML 2021 Scalable Bayesian Inverse Reinforcement Learning ICLR 2021 Scalable gradient matching based on state space Gaussian Processes ACML 2021 Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition ICML 2021 Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty Regularization AAAI 2021 SHPOS: A Theoretical Guaranteed Accelerated Particle Optimization Sampling Method IJCAI 2021 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data ICML 2021 Simple and Effective VAE Training with Calibrated Decoders ICML 2021