Bayesian & Probabilistic

Compositional Score Modeling for Simulation-Based Inference ICML 2023 Compressing Tabular Data via Latent Variable Estimation ICML 2023 Computing Divergences between Discrete Decomposable Models AAAI 2023 Conditionally Strongly Log-Concave Generative Models ICML 2023 Conditional Matrix Flows for Gaussian Graphical Models NeurIPS 2023 Conditional score-based diffusion models for Bayesian inference in infinite dimensions NeurIPS 2023 Confidence and Uncertainty Assessment for Distributional Random Forests JMLR 2023 Conformal Prediction with Missing Values ICML 2023 Consistent Model-based Clustering using the Quasi-Bernoulli Stick-breaking Process JMLR 2023 Constrained Causal Bayesian Optimization ICML 2023 Constrained Efficient Global Optimization of Expensive Black-box Functions ICML 2023 Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars NeurIPS 2023 Context-Specific Causal Discovery for Categorical Data Using Staged Trees AISTATS 2023 Contextual Gaussian Process Bandits with Neural Networks NeurIPS 2023 Contextual Stochastic Block Model: Sharp Thresholds and Contiguity JMLR 2023 Continuously Parameterized Mixture Models ICML 2023 Continuous Mixtures of Tractable Probabilistic Models AAAI 2023 Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition AISTATS 2023 Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders ICML 2023 Critical Initialization of Wide and Deep Neural Networks using Partial Jacobians: General Theory and Applications NeurIPS 2023 Deep Stochastic Processes via Functional Markov Transition Operators NeurIPS 2023 Delayed Feedback in Kernel Bandits ICML 2023 Delving Into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling CVPR 2023 DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding EMNLP 2023 Deriving Language Models from Masked Language Models ACL 2023