Variational Inference

Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS 2022 Split Hierarchical Variational Compression CVPR 2022 Stochastic Multiple Target Sampling Gradient Descent NeurIPS 2022 Surrogate Likelihoods for Variational Annealed Importance Sampling ICML 2022 Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data NeurIPS 2022 Towards Improved Zero-shot Voice Conversion with Conditional DSVAE Interspeech 2022 Truncated proposals for scalable and hassle-free simulation-based inference NeurIPS 2022 Uncertainty Modeling in Generative Compressed Sensing ICML 2022 Understanding the Variance Collapse of SVGD in High Dimensions ICLR 2022 Variational Inference for Infinitely Deep Neural Networks ICML 2022 Variational inference via Wasserstein gradient flows NeurIPS 2022 Variational Inference with Locally Enhanced Bounds for Hierarchical Models ICML 2022 Variational Model Perturbation for Source-Free Domain Adaptation NeurIPS 2022 Variational nearest neighbor Gaussian process ICML 2022 VICE: Variational Interpretable Concept Embeddings NeurIPS 2022 A Bit More Bayesian: Domain-Invariant Learning with Uncertainty ICML 2021 A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference NeurIPS 2021 A Differentiable Point Process with Its Application to Spiking Neural Networks ICML 2021 A Generalised Linear Model Framework for β-Variational Autoencoders based on Exponential Dispersion Families JMLR 2021 A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations ACL 2021 Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions NeurIPS 2021 A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models AISTATS 2021 A unified view of likelihood ratio and reparameterization gradients AISTATS 2021 A universal probabilistic spike count model reveals ongoing modulation of neural variability NeurIPS 2021 Automatic variational inference with cascading flows ICML 2021