Bayesian & Probabilistic

The Fine Print on Tempered Posteriors ACML 2023 The Hardness of Reasoning about Probabilities and Causality IJCAI 2023 The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data NeurIPS 2023 The neural dynamics of word recognition and integration EMNLP 2023 The probability flow ODE is provably fast NeurIPS 2023 The Unreasonable Effectiveness of Deep Evidential Regression AAAI 2023 Thin and deep Gaussian processes NeurIPS 2023 Thompson Sampling with Less Exploration is Fast and Optimal ICML 2023 Topological Hidden Markov Models JMLR 2023 Topological Obstructions and How to Avoid Them NeurIPS 2023 TopWORDS-Poetry: Simultaneous Text Segmentation and Word Discovery for Classical Chinese Poetry via Bayesian Inference EMNLP 2023 Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms NeurIPS 2023 Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework AISTATS 2023 Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent NeurIPS 2023 Tractable Control for Autoregressive Language Generation ICML 2023 Training Normalizing Flows from Dependent Data ICML 2023 Transformed Gaussian Processes for Characterizing a Model’s Discrepancy ACML 2023 Transport Reversible Jump Proposals AISTATS 2023 Two Views of Constrained Differential Privacy: Belief Revision and Update AAAI 2023 Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo NeurIPS 2023 Unbiased learning of deep generative models with structured discrete representations NeurIPS 2023 Uncertain Evidence in Probabilistic Models and Stochastic Simulators ICML 2023 Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data ACL 2023 Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding EMNLP 2023 Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition AISTATS 2023