Bayesian & Probabilistic

Training stochastic stabilized supralinear networks by dynamics-neutral growth NeurIPS 2022 Transfer Learning with Gaussian Processes for Bayesian Optimization AISTATS 2022 Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces NeurIPS 2022 Triangulation candidates for Bayesian optimization NeurIPS 2022 Truncated proposals for scalable and hassle-free simulation-based inference NeurIPS 2022 Trusted Multi-View Deep Learning with Opinion Aggregation AAAI 2022 Truth-Tracking via Approval Voting: Size Matters AAAI 2022 Two-way Sparse Network Inference for Count Data AISTATS 2022 Uncertainty-Guided Probabilistic Transformer for Complex Action Recognition CVPR 2022 Uncertainty Modeling in Generative Compressed Sensing ICML 2022 Uncertainty Quantification for Bayesian Optimization AISTATS 2022 Understanding the Variance Collapse of SVGD in High Dimensions ICLR 2022 Uniform deconvolution for Poisson Point Processes JMLR 2022 Using Mixed-Effects Models to Learn Bayesian Networks from Related Data Sets PGM 2022 VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming NeurIPS 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 Weakly Supervised Word Segmentation for Computational Language Documentation ACL 2022 Weak Separation in Mixture Models and Implications for Principal Stratification AISTATS 2022 Weighted Gaussian Process Bandits for Non-stationary Environments AISTATS 2022 Who did it? Identifying the Most Likely Origins of Events PGM 2022