Bayesian & Probabilistic

Learning Unnormalized Statistical Models via Compositional Optimization ICML 2023 Leveraging Active Learning to Minimise SRL Annotation Across Corpora ACL 2023 Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision AISTATS 2023 Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression NeurIPS 2023 Lifted Inference with Linear Order Axiom AAAI 2023 Linear Time GPs for Inferring Latent Trajectories from Neural Spike Trains ICML 2023 Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution CVPR 2023 Local Risk Bounds for Statistical Aggregation COLT 2023 Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples ICLR 2023 MAP: Multimodal Uncertainty-Aware Vision-Language Pre-Training Model CVPR 2023 MARS: Meta-learning as Score Matching in the Function Space ICLR 2023 Martingale Posterior Neural Processes ICLR 2023 Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference ICML 2023 Maximum likelihood estimation in Gaussian process regression is ill-posed JMLR 2023 Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction ICLR 2023 MIND at SemEval-2023 Task 11: From Uncertain Predictions to Subjective Disagreement SemEval 2023 Minimax-Bayes Reinforcement Learning AISTATS 2023 Minimum Description Length Control ICLR 2023 Mixed-Effect Thompson Sampling AISTATS 2023 Mixtures of All Trees AISTATS 2023 Model-based Causal Discovery for Zero-Inflated Count Data JMLR 2023 Model-based Validation as Probabilistic Inference L4DC 2023 Modeling Human Trust and Reliance in AI-Assisted Decision Making: A Markovian Approach AAAI 2023 Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder NeurIPS 2023 Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes ICML 2023