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Variational Inference

767 directly classified papers

Papers per year

Papers

KernelMatmul: Scaling Gaussian Processes to Large Time Series AAAI 2025
Bayesian Data Sketching for Varying Coefficient Regression Models JMLR 2025
Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction AAAI 2025
Resource-Rational Noisy-Channel Language Processing: Testing the Effect of Algorithmic Constraints on Inferences EMNLP 2025
Debiased Active Learning with Variational Gradient Rectifier AAAI 2025
Scalable and Adaptive Variational Bayes Methods for Hawkes Processes JMLR 2025
RPMIL: Rethinking Uncertainty-Aware Probabilistic Multiple Instance Learning for Whole Slide Pathology Diagnosis IJCAI 2025
Asset Pricing with Contrastive Adversarial Variational Bayes IJCAI 2025
Enhancing NER by Harnessing Multiple Datasets with Conditional Variational Autoencoders ACL 2025
Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets EMNLP 2025
A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrödinger Equation AAAI 2025
A Variational Approach for Mitigating Entity Bias in Relation Extraction ACL 2025
Causal View of Time Series Imputation: Some Identification Results on Missing Mechanism IJCAI 2025
Stochasticity-aware No-Reference Point Cloud Quality Assessment IJCAI 2025
Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable AISTATS 2024
Explainable Origin-Destination Crowd Flow Interpolation via Variational Multi-Modal Recurrent Graph Auto-Encoder AAAI 2024
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels AISTATS 2024
Riemannian Laplace Approximation with the Fisher Metric AISTATS 2024
Mixed variational flows for discrete variables AISTATS 2024
Variational Resampling AISTATS 2024
Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds AISTATS 2024
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference AISTATS 2024
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS 2024
Stochastic Approximation with Biased MCMC for Expectation Maximization AISTATS 2024
Learning Sparse Codes with Entropy-Based ELBOs AISTATS 2024