Bayesian & Probabilistic

On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models AAAI 2023 On the Calibration of Probabilistic Classifier Sets AISTATS 2023 On the Convergence of Black-Box Variational Inference NeurIPS 2023 On the Estimation of Gaussian Mixture Copula Models ICML 2023 On the geometry of Stein variational gradient descent JMLR 2023 On the Identifiability and Estimation of Causal Location-Scale Noise Models ICML 2023 On the Sublinear Regret of GP-UCB NeurIPS 2023 On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study EMNLP 2023 Optimistic Active Exploration of Dynamical Systems NeurIPS 2023 Optimizing Hyperparameters with Conformal Quantile Regression ICML 2023 Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning Approach AISTATS 2023 Overcoming Prior Misspecification in Online Learning to Rank AISTATS 2023 Particle algorithms for maximum likelihood training of latent variable models AISTATS 2023 Personalized Federated Learning under Mixture of Distributions ICML 2023 Pitfalls of Gaussians as a noise distribution in NCE ICLR 2023 Positive Distribution Pollution: Rethinking Positive Unlabeled Learning from a Unified Perspective AAAI 2023 Posterior Consistency for Bayesian Relevance Vector Machines JMLR 2023 Posterior Contraction for Deep Gaussian Process Priors JMLR 2023 Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds NeurIPS 2023 Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model AAAI 2023 Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference ICML 2023 Prediction-Oriented Bayesian Active Learning AISTATS 2023 Preferential Subsampling for Stochastic Gradient Langevin Dynamics AISTATS 2023 Probabilistic Attention-to-Influence Neural Models for Event Sequences ICML 2023 Probabilistic Conformal Prediction Using Conditional Random Samples AISTATS 2023