Bayesian & Probabilistic

MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models UAI 2020 Matérn Gaussian Processes on Riemannian Manifolds NeurIPS 2020 Matrix Inference and Estimation in Multi-Layer Models NeurIPS 2020 Meta-Amortized Variational Inference and Learning AAAI 2020 Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization ICLR 2020 Meta-Learning PAC-Bayes Priors in Model Averaging AAAI 2020 Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes NeurIPS 2020 Meta-trained agents implement Bayes-optimal agents NeurIPS 2020 Missing Values in Multiple Joint Inference of Gaussian Graphical Models PGM 2020 Mitigating Gender Bias Amplification in Distribution by Posterior Regularization ACL 2020 Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables NeurIPS 2020 Mixed-Membership Stochastic Block Models for Weighted Networks UAI 2020 Modeling Ambiguity with Many Annotators and Self-Assessments of Annotator Certainty COLING 2020 Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows NeurIPS 2020 Modeling Probabilistic Commitments for Maintenance Is Inherently Harder than for Achievement AAAI 2020 Modeling Shared responses in Neuroimaging Studies through MultiView ICA NeurIPS 2020 Modulating Surrogates for Bayesian Optimization ICML 2020 Monotonic Gaussian Process Flows AISTATS 2020 MRFMap: Online Probabilistic 3D Mapping using Forward Ray Sensor Models RSS 2020 Multi-Hypothesis Machine Translation Evaluation ACL 2020 Multi-objective Bayesian Optimization using Pareto-frontier Entropy ICML 2020 Multiplicative Gaussian Particle Filter AISTATS 2020 Multi-Speaker Text-to-Speech Synthesis Using Deep Gaussian Processes Interspeech 2020 Multi-task Causal Learning with Gaussian Processes NeurIPS 2020 NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations AAAI 2020