variational inference

Identifiable Feature Learning for Spatial Data with Nonlinear ICA AISTATS 2024 Idiographic Personality Gaussian Process for Psychological Assessment NeurIPS 2024 Improved off-policy training of diffusion samplers NeurIPS 2024 Improved Particle Approximation Error for Mean Field Neural Networks NeurIPS 2024 Improved sampling via learned diffusions ICLR 2024 Improving Non-Transferable Representation Learning by Harnessing Content and Style ICLR 2024 Inducing Clusters Deep Kernel Gaussian Process for Longitudinal Data AAAI 2024 Inferring stochastic low-rank recurrent neural networks from neural data NeurIPS 2024 Inflationary Flows: Calibrated Bayesian Inference with Diffusion-Based Models NeurIPS 2024 Information Processing Equalities and the Information–Risk Bridge JMLR 2024 Joint control variate for faster black-box variational inference AISTATS 2024 Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models ICLR 2024 Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood ICLR 2024 Learning Sparse Codes with Entropy-Based ELBOs AISTATS 2024 Learning to Maximize Mutual Information for Chain-of-Thought Distillation ACL 2024 Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS 2024 LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraints ICLR 2024 Minimizing Convex Functionals over Space of Probability Measures via KL Divergence Gradient Flow AISTATS 2024 Modeling Random Networks with Heterogeneous Reciprocity JMLR 2024 Multimodal Molecular Pretraining via Modality Blending ICLR 2024 Mutual Information Estimation via f-Divergence and Data Derangements NeurIPS 2024 Mutual Information Estimation via Normalizing Flows NeurIPS 2024 Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling NeurIPS 2024 Neural Markov Random Field for Stereo Matching CVPR 2024 Neural structure learning with stochastic differential equations ICLR 2024