variational approximation

Online Bernstein-von Mises theorem JMLR 2026 Aligning Multimodal Representations through an Information Bottleneck ICML 2025 Learning Likelihood-Free Reference Priors ICML 2025 Learning Optimal Multimodal Information Bottleneck Representations ICML 2025 A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression JMLR 2024 A Framework for Improving the Reliability of Black-box Variational Inference JMLR 2024 Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference AISTATS 2024 Wasserstein Proximal Coordinate Gradient Algorithms JMLR 2024 Calibrating the Rigged Lottery: Making All Tickets Reliable ICLR 2023 Learning in temporally structured environments ICLR 2023 Learning with Explanation Constraints NeurIPS 2023 A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One JMLR 2022 Iterative Alignment Flows AISTATS 2022 On the Robustness to Misspecification of α-posteriors and Their Variational Approximations JMLR 2022 Statistical Optimality and Stability of Tangent Transform Algorithms in Logit Models JMLR 2022 A variational approximation for analyzing the dynamics of panel data UAI 2021 No MCMC for me: Amortized sampling for fast and stable training of energy-based models ICLR 2021 Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty UAI 2020 Personalized Input-Output Hidden Markov Models for Disease Progression Modeling MLHC 2020 Structure Learning from Related Data Sets with a Hierarchical Bayesian Score PGM 2020 CompareLDA: A Topic Model for Document Comparison AAAI 2019 Minimax Online Prediction of Varying Bernoulli Process under Variational Approximation ACML 2019 Learning to Explain: An Information-Theoretic Perspective on Model Interpretation ICML 2018 Robust Locally-Linear Controllable Embedding AISTATS 2018 Temporal alignment and latent Gaussian process factor inference in population spike trains NeurIPS 2018