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Publication search results
found 82 matches
- 2024
- Xi Wang, Tomas Geffner, Justin Domke:
Joint control variate for faster black-box variational inference. AISTATS 2024: 1639-1647 - Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke:
Simulation-Based Stacking. AISTATS 2024: 4267-4275 - Abhinav Agrawal, Justin Domke:
Understanding and mitigating difficulties in posterior predictive evaluation. CoRR abs/2405.19747 (2024) - 2023
- Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon:
U-Statistics for Importance-Weighted Variational Inference. Trans. Mach. Learn. Res. 2023 (2023) - Tomas Geffner, Justin Domke:
Langevin Diffusion Variational Inference. AISTATS 2023: 576-593 - Justin Domke, Robert M. Gower, Guillaume Garrigos:
Provable convergence guarantees for black-box variational inference. NeurIPS 2023 - Yuling Yao, Justin Domke:
Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier. NeurIPS 2023 - Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon:
U-Statistics for Importance-Weighted Variational Inference. CoRR abs/2302.13918 (2023) - Javier Burroni, Justin Domke, Daniel Sheldon:
Sample Average Approximation for Black-Box VI. CoRR abs/2304.06803 (2023) - Yuling Yao, Justin Domke:
Discriminative calibration. CoRR abs/2305.14593 (2023) - Justin Domke, Guillaume Garrigos, Robert M. Gower:
Provable convergence guarantees for black-box variational inference. CoRR abs/2306.03638 (2023) - Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke:
Simulation based stacking. CoRR abs/2310.17009 (2023) - 2022
- Ga Wu
, Justin Domke, Scott Sanner:
Arbitrary conditional inference in variational autoencoders via fast prior network training. Mach. Learn. 111(7): 2537-2559 (2022) - Jinlin Lai, Justin Domke, Daniel Sheldon:
Variational Marginal Particle Filters. AISTATS 2022: 875-895 - Tomas Geffner, Justin Domke:
Variational Inference with Locally Enhanced Bounds for Hierarchical Models. ICML 2022: 7310-7323 - Tomas Geffner, Justin Domke:
Variational Inference with Locally Enhanced Bounds for Hierarchical Models. CoRR abs/2203.04432 (2022) - Tomas Geffner, Justin Domke:
Langevin Diffusion Variational Inference. CoRR abs/2208.07743 (2022) - Xi Wang, Tomas Geffner, Justin Domke:
A Dual Control Variate for doubly stochastic optimization and black-box variational inference. CoRR abs/2210.07290 (2022) - 2021
- Tomas Geffner, Justin Domke:
On the difficulty of unbiased alpha divergence minimization. ICML 2021: 3650-3659 - Abhinav Agrawal, Justin Domke:
Amortized Variational Inference for Simple Hierarchical Models. NeurIPS 2021: 21388-21399 - Tomas Geffner, Justin Domke:
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. NeurIPS 2021: 639-651 - Justin Domke:
An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations. CoRR abs/2103.01030 (2021) - Tomas Geffner, Justin Domke:
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization. CoRR abs/2105.06587 (2021) - Tomas Geffner, Justin Domke:
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. CoRR abs/2107.04150 (2021) - Jinlin Lai, Daniel Sheldon, Justin Domke:
Variational Marginal Particle Filters. CoRR abs/2109.15134 (2021) - Abhinav Agrawal, Justin Domke:
Amortized Variational Inference for Simple Hierarchical Models. CoRR abs/2111.03144 (2021) - 2020
- Tomas Geffner, Justin Domke:
A Rule for Gradient Estimator Selection, with an Application to Variational Inference. AISTATS 2020: 1803-1812 - Justin Domke:
Provable Smoothness Guarantees for Black-Box Variational Inference. ICML 2020: 2587-2596 - Abhinav Agrawal, Daniel Sheldon, Justin Domke:
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. NeurIPS 2020 - Tomas Geffner, Justin Domke:
Approximation Based Variance Reduction for Reparameterization Gradients. NeurIPS 2020
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