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Variational Inference

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TitleStatusHype
Logit Disagreement: OoD Detection with Bayesian Neural Networks—0
Longitudinal Deep Kernel Gaussian Process Regression—0
Loss as the Inconsistency of a Probabilistic Dependency Graph: Choose Your Model, Not Your Loss Function—0
Loss function based second-order Jensen inequality and its application to particle variational inference—0
Lossless Compression using Continuously-Indexed Normalizing Flows—0
Low Complexity Approximate Bayesian Logistic Regression for Sparse Online Learning—0
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel—0
Low-Multi-Rank High-Order Bayesian Robust Tensor Factorization—0
Machine Learning and the Future of Bayesian Computation—0
MAGI: Multi-Annotated Explanation-Guided Learning—0
Marginal Likelihood Gradient for Bayesian Neural Networks—0
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap—0
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems—0
Markov Chain Monte Carlo Policy Optimization—0
Markovian Score Climbing: Variational Inference with KL(p||q)—0
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data—0
Matched bipartite block model with covariates—0
Matrix Inversion free variational inference in Conditional Student's T Processes—0
MaxEntropy Pursuit Variational Inference—0
Maximizing submodular functions using probabilistic graphical models—0
Max-Margin Nonparametric Latent Feature Models for Link Prediction—0
MCMC-driven learning—0
MCMC-Interactive Variational Inference—0
MCMC Variational Inference via Uncorrected Hamiltonian Annealing—0
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes—0
Mean-field Variational Inference via Wasserstein Gradient Flow—0
Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models—0
Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations—0
Measurement error models: from nonparametric methods to deep neural networks—0
Measuring Systematic Risk with Neural Network Factor Model—0
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning—0
Memoized Online Variational Inference for Dirichlet Process Mixture Models—0
Message Passing Stein Variational Gradient Descent—0
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory—0
Meta-Learning for Variational Inference—0
Meta-Learning Divergences of Variational Inference—0
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference—0
Metropolis-Hastings view on variational inference and adversarial training—0
MISSO: Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex and Nonsmooth Problems—0
Mixed Membership Stochastic Blockmodels—0
Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement—0
Mixture of neural operator experts for learning boundary conditions and model selection—0
Mixture Representation Learning with Coupled Autoencoding Agents—0
Mixture weights optimisation for Alpha-Divergence Variational Inference—0
Model Informed Flows for Bayesian Inference of Probabilistic Programs—0
Modeling Item Response Theory with Stochastic Variational Inference—0
Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field—0
Modeling Overlapping Communities with Node Popularities—0
Modeling Randomly Observed Spatiotemporal Dynamical Systems—0
Modeling Randomly Walking Volatility with Chained Gamma Distributions—0
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