PROVABLE GRADIENT VARIANCE GUARANTEES FOR BLACK-BOX VARIATIONAL INFERENCE

von Domke, Justin
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 1 of 20 (2020)

PROVABLE SMOOTHNESS GUARANTEES FOR BLACK-BOX VARIATIONAL INFERENCE

von Domke, Justin
Veröffentlicht in International Conference on Machine Learning (37. : 2020 : Online) 37th International Conference on Machine Learning (ICML 2020) ; Part 4 of 15 (2021)

Amortized Variational Inference for Simple Hierarchical Models

von Agrawal, Abhinav
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 26 of 36 (2022)

Variational Inference with Locally Enhanced Bounds for Hierarchical Models

von Geffner, Tomas
Veröffentlicht in International Conference on Machine Learning (39. : 2022 : Baltimore, Md.; Online) International Conference on Machine Learning (ICML 2022) ; Part 9 of 33 (2023)

APPROXIMATION BASED VARIANCE REDUCTION FOR REPARAMETERIZATION GRADIENTS

von Geffner, Tomas
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 4 of 27 (2021)

ON THE DIFFICULTY OF UNBIASED ALPHA DIVERGENCE MINIMIZATION

von GEFFNER, TOMAS
Veröffentlicht in International Conference on Machine Learning (38. : 2021 : Online) International Conference on Machine Learning (ICML 2021 ; Part 5 of 16 (2022)

MCMC Variational Inference via Uncorrected Hamiltonian Annealing

von Geffner, Tomas
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 2 of 36 (2022)

DIVIDE AND COUPLE: USING MONTE CARLO VARIATIONAL OBJECTIVES FOR POSTERIOR APPROXIMATION

von Domke, Justin
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 1 of 20 (2020)

ADVANCES IN BLACK-BOX VI: NORMALIZING FLOWS, IMPORTANCE WEIGHTING, AND OPTIMIZATION

von Agrawal, Abhinav
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 21 of 27 (2021)