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Probabilistic Modeling

2,252 papers

Papers per year

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Papers

Energy-based Epistemic Uncertainty for Graph Neural Networks NIPS 2024
Bidirectional Recurrence for Cardiac Motion Tracking with Gaussian Process Latent Coding NIPS 2024
Credal Learning Theory NIPS 2024
Bridge-IF: Learning Inverse Protein Folding with Markov Bridges NIPS 2024
Quasi-Bayes meets Vines NIPS 2024
Debiasing Synthetic Data Generated by Deep Generative Models NIPS 2024
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks NIPS 2024
Causal Effect Identification in a Sub-Population with Latent Variables NIPS 2024
Poisson Variational Autoencoder NIPS 2024
A versatile informative diffusion model for single-cell ATAC-seq data generation and analysis NIPS 2024
A Benchmark Suite for Evaluating Neural Mutual Information Estimators on Unstructured Datasets NIPS 2024
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling NIPS 2024
Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithm NIPS 2024
Probabilistic size-and-shape functional mixed models NIPS 2024
Stochastic Concept Bottleneck Models NIPS 2024
Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning NIPS 2024
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning NIPS 2024
Multivariate Probabilistic Time Series Forecasting with Correlated Errors NIPS 2024
Idiographic Personality Gaussian Process for Psychological Assessment NIPS 2024
Categorical Flow Matching on Statistical Manifolds NIPS 2024
Cascade of phase transitions in the training of energy-based models NIPS 2024
Preferential Normalizing Flows NIPS 2024
Bayesian Adaptive Calibration and Optimal Design NIPS 2024
A probability contrastive learning framework for 3D molecular representation learning NIPS 2024
One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently NIPS 2024