Bayesian Inference

Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level AAAI 2023 Learning to Transform for Generalizable Instance-wise Invariance ICCV 2023 Learning Treatment Effects from Observational and Experimental Data AISTATS 2023 Learning via Wasserstein-Based High Probability Generalisation Bounds NeurIPS 2023 Learn to Accumulate Evidence from All Training Samples: Theory and Practice ICML 2023 Length Does Matter: Summary Length can Bias Summarization Metrics EMNLP 2023 Leveraging Demonstrations to Improve Online Learning: Quality Matters ICML 2023 Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision AISTATS 2023 Likelihood Ratio Confidence Sets for Sequential Decision Making NeurIPS 2023 Local Justice and Machine Learning: Modeling and Inferring Dynamic Ethical Preferences toward Allocations AAAI 2023 Local Risk Bounds for Statistical Aggregation COLT 2023 Low Tree-Rank Bayesian Vector Autoregression Models JMLR 2023 Markovian Gaussian Process Variational Autoencoders ICML 2023 MARS: Masked Automatic Ranks Selection in Tensor Decompositions AISTATS 2023 Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference ICML 2023 Massively parallel reweighted wake-sleep UAI 2023 Meta-Auxiliary Learning for Future Depth Prediction in Videos WACV 2023 Meta-Uncertainty in Bayesian Model Comparison AISTATS 2023 MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape Recovery ICCV 2023 Microcanonical Hamiltonian Monte Carlo JMLR 2023 Mini-batching error and adaptive Langevin dynamics JMLR 2023 Minimax-Bayes Reinforcement Learning AISTATS 2023 Mixed-Effect Thompson Sampling AISTATS 2023 Mixed-Variable Black-Box Optimisation Using Value Proposal Trees AAAI 2023 MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under nonparametrized geometrical variability NeurIPS 2023