Bayesian & Probabilistic

Fourier Representations for Black-Box Optimization over Categorical Variables AAAI 2022 FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting JMLR 2022 Function Classes for Identifiable Nonlinear Independent Component Analysis NeurIPS 2022 Function-space Inference with Sparse Implicit Processes ICML 2022 Gardner formula for Ising perceptron models at small densities COLT 2022 Gaussian Copula Embeddings NeurIPS 2022 Gaussian Process Bandit Optimization with Few Batches AISTATS 2022 Gaussian Process Bandits with Aggregated Feedback AAAI 2022 Gaussian Process Boosting JMLR 2022 Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits JMLR 2022 Gauss-Legendre Features for Gaussian Process Regression JMLR 2022 Generalization Gap in Amortized Inference NeurIPS 2022 Generalized Kernel Thinning ICLR 2022 Generalizing Bayesian Optimization with Decision-theoretic Entropies NeurIPS 2022 Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder ICML 2022 Generative Flow Networks for Discrete Probabilistic Modeling ICML 2022 Giga-scale Kernel Matrix-Vector Multiplication on GPU NeurIPS 2022 Globally Gated Deep Linear Networks NeurIPS 2022 GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models NeurIPS 2022 Gradient Estimation with Discrete Stein Operators NeurIPS 2022 Graph Structure Learning with Variational Information Bottleneck AAAI 2022 Grassmann Stein Variational Gradient Descent AISTATS 2022 Hardness of Maximum Likelihood Learning of DPPs COLT 2022 Hedging as Reward Augmentation in Probabilistic Graphical Models NeurIPS 2022 High-dimensional Additive Gaussian Processes under Monotonicity Constraints NeurIPS 2022