scalable inference

Hybrid Autoregressive Inference for Scalable Multi-Hop Explanation Regeneration AAAI 2022 Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing ACL 2022 Scalable Gaussian Process Variational Autoencoders AISTATS 2021 SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes ICML 2021 Inter-domain Deep Gaussian Processes ICML 2020 Scalable Exact Inference in Multi-Output Gaussian Processes ICML 2020 Scalable Gaussian Process Regression Networks IJCAI 2020 Embarrassingly Parallel Inference for Gaussian Processes JMLR 2019 Exact Gaussian Processes on a Million Data Points NeurIPS 2019 Generic Inference in Latent Gaussian Process Models JMLR 2019 Nonparametric Bayesian Aggregation for Massive Data JMLR 2019 SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models NeurIPS 2019 GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration NeurIPS 2018 Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models AISTATS 2018 Product Kernel Interpolation for Scalable Gaussian Processes AISTATS 2018 Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition AISTATS 2018 Scalable Generalized Dynamic Topic Models AISTATS 2018 Scaling the Poisson GLM to massive neural datasets through polynomial approximations NeurIPS 2018 Learning Scalable Deep Kernels with Recurrent Structure JMLR 2017 PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference NeurIPS 2017 Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes NeurIPS 2017 Scalable Variational Inference for Super Resolution Microscopy AISTATS 2017 d-VMP: Distributed Variational Message Passing PGM 2016 One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities NeurIPS 2016 Scalable MCMC for Mixed Membership Stochastic Blockmodels AISTATS 2016