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Probabilistic Modeling

2591 directly classified papers

Papers per year

Papers

Uncertain Evidence in Probabilistic Models and Stochastic Simulators ICML 2023
Undirected Probabilistic Model for Tensor Decomposition NIPS 2023
FaDIn: Fast Discretized Inference for Hawkes Processes with General Parametric Kernels ICML 2023
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification ICML 2023
Vector-Valued Control Variates ICML 2023
Not all Strongly Rayleigh Distributions Have Small Probabilistic Generating Circuits ICML 2023
Thin and deep Gaussian processes NIPS 2023
Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation NIPS 2023
Input uncertainty propagation through trained neural networks ICML 2023
Probabilistic Attention-to-Influence Neural Models for Event Sequences ICML 2023
Quantitative Universal Approximation Bounds for Deep Belief Networks ICML 2023
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation ICML 2023
Taking the neural sampling code very seriously: A data-driven approach for evaluating generative models of the visual system NIPS 2023
Tractable Control for Autoregressive Language Generation ICML 2023
The neural dynamics of word recognition and integration EMNLP 2023
Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process ICML 2023
Approximate inference of marginals using the IBIA framework NIPS 2023
Survival Permanental Processes for Survival Analysis with Time-Varying Covariates NIPS 2023
Neural Diffusion Processes ICML 2023
Learning-augmented count-min sketches via Bayesian nonparametrics JMLR 2023
Multi-view Collaborative Gaussian Process Dynamical Systems JMLR 2023
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations ICML 2023
Bayesian Learning via Q-Exponential Process NIPS 2023
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference NIPS 2023
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks NIPS 2023