Key papers

• Invertible generative models for inverse problems: mitigating representation error and dataset bias (2019)
  Muhammad Asim et al.
  8.66
• Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Prediction (2026)
  Yurui Zheng et al.
  5.01
• Influence Diagnostics in High-dimensional M-estimation: Precise Asymptotics (2026)
  Hugo Cui
  5.01
• RELTA-SGLD: Relative-Growth Localized Taming for Nonconvex Stochastic-Gradient Langevin Learning (2026)
  Yiwei Zhou et al.
  5.01
• Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery (2026)
  Chuyao Zhang et al.
  4.39
• Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts (2026)
  Ali Maghami et al.
  4.39
• Boltzmann-Expected Molecular Design with Decoupled Annealing Flows (2026)
  Selma Moqvist et al.
  4.39
• Non--negative matrix factorization using the \textit{R} package \textsf{nnmf} (2026)
  Volkan Sevin\c{c} et al.
  4.39
• Active Inference as a Convex Markov Decision Process (2026)
  Nikola Milosevic et al.
  4.39
• Quantum Kernels and the Cross-Section of Stock Returns: Anatomy of a Vanishing Advantage (2026)
  Junchi Shen
  4.39
• Statistical Inference for Rank Allocation in Low-Rank Adaptation (2026)
  Yihang Gao et al.
  4.39
• Adaptive Bayesian Online Learning via Expert Aggregation (2026)
  Jungbin Jun et al.
  4.39
• Adaptive deep nonparametric regression from dependent data under covariate shift (2026)
  William Kengne et al.
  4.39
• Missing Data Imputation under Manifold Hypothesis (2026)
  Zelong Bi et al.
  2.00
• A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods (2026)
  Shota Saito et al.
  1.89
• Enhanced Generative Model Evaluation with Clipped Density and Coverage (2025)
  Nicolas Salvy et al.
  1.33
• A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation (2025)
  Xinran Song and Tianyu Chen and Mingyuan Zhou
  1.22
• Compositional Model based Fisher Vector Coding for Image Classification (2016)
  Lingqiao Liu et al.
  —
• Ladder Variational Autoencoders (2016)
  Casper Kaae S{\o}nderby et al.
  —
• R\'enyi Divergence Variational Inference (2016)
  Yingzhen Li et al.
  —
• Discriminative Regularization for Generative Models (2016)
  Alex Lamb et al.
  —
• Auxiliary Deep Generative Models (2016)
  Lars Maal{\o}e et al.
  —
• Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series (2016)
  Maximilian Soelch et al.
  —
• A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features (2016)
  Tim Salimans
  —
• Overdispersed Black-Box Variational Inference (2016)
  Francisco J. R. Ruiz et al.
  —
• Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors (2016)
  Christos Louizos and Max Welling
  —
• A latent-observed dissimilarity measure (2016)
  Yasushi Terazono (1) ((1) Graduate School of Information Science and Technology et al.
  —
• Gaussian Copula Variational Autoencoders for Mixed Data (2016)
  Suwon Suh and Seungjin Choi
  —
• Towards Conceptual Compression (2016)
  Karol Gregor et al.
  —
• Gaussian variational approximation with sparse precision matrices (2016)
  Linda S. L. Tan and David J. Nott
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• Variational hybridization and transformation for large inaccurate noisy-or networks (2016)
  Yusheng Xie et al.
  —
• Stick-Breaking Variational Autoencoders (2016)
  Eric Nalisnick and Padhraic Smyth
  —
• Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data (2016)
  Maximilian Karl et al.
  —
• Relevant sparse codes with variational information bottleneck (2016)
  Matthew Chalk et al.
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• Asymptotically exact inference in differentiable generative models (2016)
  Matthew M. Graham and Amos J. Storkey
  —
• Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions (2016)
  Christian Gruhl et al.
  —
• Improving Variational Inference with Inverse Autoregressive Flow (2016)
  Diederik P. Kingma et al.
  —
• Automatic Variational ABC (2016)
  Alexander Moreno et al.
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• Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model (2016)
  Fariba Yousefi et al.
  —
• Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages (2016)
  Yin Cheng Ng et al.
  —
• Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units (2016)
  Stefanos Eleftheriadis et al.
  —
• Inverting Variational Autoencoders for Improved Generative Accuracy (2016)
  Ian Gemp et al.
  —
• Learning Boltzmann Machine with EM-like Method (2016)
  Jinmeng Song et al.
  —
• Discrete Variational Autoencoders (2016)
  Jason Tyler Rolfe
  —
• Language as a Latent Variable: Discrete Generative Models for Sentence Compression (2016)
  Yishu Miao and Phil Blunsom
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• Variational Inference with Hamiltonian Monte Carlo (2016)
  Christopher Wolf et al.
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• A Non-generative Framework and Convex Relaxations for Unsupervised Learning (2016)
  Elad Hazan et al.
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• The Generalized Reparameterization Gradient (2016)
  Francisco J. R. Ruiz and Michalis K. Titsias and David M. Blei
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• Truncated Variational Expectation Maximization (2016)
  J\"org L\"ucke
  —
• Deep Variational Canonical Correlation Analysis (2016)
  Weiran Wang et al.
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• Random Feature Expansions for Deep Gaussian Processes (2016)
  Kurt Cutajar et al.
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• Stochastic Variational Deep Kernel Learning (2016)
  Andrew Gordon Wilson et al.
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• Categorical Reparameterization with Gumbel-Softmax (2016)
  Eric Jang et al.
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• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders (2016)
  Nat Dilokthanakul et al.
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• Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering (2016)
  Zhuxi Jiang et al.
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• Boosting Variational Inference (2016)
  Fangjian Guo et al.
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• Variational Boosting: Iteratively Refining Posterior Approximations (2016)
  Andrew C. Miller et al.
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• Variational Fourier features for Gaussian processes (2016)
  James Hensman et al.
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• Robust Variational Inference (2016)
  Michael Figurnov et al.
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• Two Methods For Wild Variational Inference (2016)
  Qiang Liu et al.
  —