Variational Inference

f-Divergence Variational Inference NeurIPS 2020 From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models ICML 2020 Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective NeurIPS 2020 GenDICE: Generalized Offline Estimation of Stationary Values ICLR 2020 Generalized Zero-Shot Learning via Over-Complete Distribution CVPR 2020 Generative Semantic Hashing Enhanced via Boltzmann Machines ACL 2020 Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond NeurIPS 2020 Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows NeurIPS 2020 Interpretable Deep Gaussian Processes with Moments AISTATS 2020 Learning Agent Representations for Ice Hockey NeurIPS 2020 Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE NeurIPS 2020 Learning Robot Skills with Temporal Variational Inference ICML 2020 Learning to Learn Kernels with Variational Random Features ICML 2020 Learning under Model Misspecification: Applications to Variational and Ensemble methods NeurIPS 2020 Lifted Hybrid Variational Inference IJCAI 2020 Localizing and Amortizing: Efficient Inference for Gaussian Processes ACML 2020 Markovian Score Climbing: Variational Inference with KL(p||q) NeurIPS 2020 Meta-Amortized Variational Inference and Learning AAAI 2020 Meta-Learning with Shared Amortized Variational Inference ICML 2020 Neural Decomposition: Functional ANOVA with Variational Autoencoders AISTATS 2020 Neural Mixed Counting Models for Dispersed Topic Discovery ACL 2020 Online Neural Connectivity Estimation with Noisy Group Testing NeurIPS 2020 On the Encoder-Decoder Incompatibility in Variational Text Modeling and Beyond ACL 2020 On the Expressiveness of Approximate Inference in Bayesian Neural Networks NeurIPS 2020 Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds NeurIPS 2020