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Probabilistic Modeling

1884 directly classified papers

Papers per year

Papers

Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling AAAI 2023
Integrating Random Effects in Deep Neural Networks JMLR 2023
An Annotated Graph Model with Differential Degree Heterogeneity for Directed Networks JMLR 2023
Variational Wasserstein Barycenters with C-cyclical Monotonicity Regularization AAAI 2023
Bayesian Structure Scores for Probabilistic Circuits AISTATS 2023
C-NTPP: Learning Cluster-Aware Neural Temporal Point Process AAAI 2023
Nearest Neighbor Dirichlet Mixtures JMLR 2023
Semiparametric Inference Using Fractional Posteriors JMLR 2023
Knowledge Graph Embedding by Normalizing Flows AAAI 2023
Neural Spline Search for Quantile Probabilistic Modeling AAAI 2023
GFlowNet Foundations JMLR 2023
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks AAAI 2023
Continuous Mixtures of Tractable Probabilistic Models AAAI 2023
Learning to love diligent trolls: Accounting for rater effects in the dialogue safety task EMNLP 2023
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study EMNLP 2023
Stochastic Generative Flow Networks UAI 2023
Input uncertainty propagation through trained neural networks ICML 2023
Transport Elliptical Slice Sampling AISTATS 2023
Probabilistic Conformal Prediction Using Conditional Random Samples AISTATS 2023
ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images CVPR 2023
Single Image Depth Prediction Made Better: A Multivariate Gaussian Take CVPR 2023
COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence IJCAI 2022
Non-Cheating Teaching Revisited: A New Probabilistic Machine Teaching Model IJCAI 2022
Reconstructing Diffusion Networks from Incomplete Data IJCAI 2022
Learning General Gaussian Mixture Model with Integral Cosine Similarity IJCAI 2022