Probabilistic Modeling

Quasi-Bayes meets Vines NeurIPS 2024 Response Time Improves Gaussian Process Models for Perception and Preferences UAI 2024 Robust Gaussian Processes via Relevance Pursuit NeurIPS 2024 Robustly Train Normalizing Flows via KL Divergence Regularization AAAI 2024 Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction AAAI 2024 ROME: Robust Multi-Modal Density Estimator IJCAI 2024 Sample-Constrained Black Box Optimization for Audio Personalization AAAI 2024 Sampling from the Mean-Field Stationary Distribution COLT 2024 Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits NeurIPS 2024 Scores for Learning Discrete Causal Graphs with Unobserved Confounders AAAI 2024 Secure Distributed Sparse Gaussian Process Models Using Multi-Key Homomorphic Encryption AAAI 2024 Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling CVPR 2024 SimCalib: Graph Neural Network Calibration Based on Similarity between Nodes AAAI 2024 Soft Learning Probabilistic Circuits PGM 2024 Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation NeurIPS 2024 Sparse Bayesian Generative Modeling for Compressive Sensing NeurIPS 2024 Sparse Graphical Linear Dynamical Systems JMLR 2024 Sparse Variational Student-t Processes AAAI 2024 SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite Space AAAI 2024 Spectral Learning of Shared Dynamics Between Generalized-Linear Processes NeurIPS 2024 Stable Implementation of Probabilistic ODE Solvers JMLR 2024 Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence NeurIPS 2024 Stochastic Concept Bottleneck Models NeurIPS 2024 Structural Inference of Dynamical Systems with Conjoined State Space Models NeurIPS 2024 Subtractive Mixture Models via Squaring: Representation and Learning ICLR 2024