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2020 – today
- 2023
[c10]Tomas Geffner, Justin Domke:
Langevin Diffusion Variational Inference. AISTATS 2023: 576-593- [c9]Tomas Geffner, George Papamakarios, Andriy Mnih:
Compositional Score Modeling for Simulation-Based Inference. ICML 2023: 11098-11116 - 2022
- [c8]Tomas Geffner, Justin Domke:
Variational Inference with Locally Enhanced Bounds for Hierarchical Models. ICML 2022: 7310-7323 - [i12]Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang:
Deep End-to-end Causal Inference. CoRR abs/2202.02195 (2022) - [i11]Tomas Geffner, Justin Domke:
Variational Inference with Locally Enhanced Bounds for Hierarchical Models. CoRR abs/2203.04432 (2022) - [i10]Tomas Geffner, Justin Domke:
Langevin Diffusion Variational Inference. CoRR abs/2208.07743 (2022) - [i9]Tomas Geffner, George Papamakarios, Andriy Mnih:
Score Modeling for Simulation-based Inference. CoRR abs/2209.14249 (2022) - [i8]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
- [c7]Tomas Geffner, Justin Domke:
On the difficulty of unbiased alpha divergence minimization. ICML 2021: 3650-3659 - [c6]Tomas Geffner, Justin Domke:
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. NeurIPS 2021: 639-651 - [i7]Tomas Geffner, Justin Domke:
Empirical Evaluation of Biased Methods for Alpha Divergence Minimization. CoRR abs/2105.06587 (2021) - [i6]Tomas Geffner, Justin Domke:
MCMC Variational Inference via Uncorrected Hamiltonian Annealing. CoRR abs/2107.04150 (2021) - 2020
- [c5]Tomas Geffner, Justin Domke:
A Rule for Gradient Estimator Selection, with an Application to Variational Inference. AISTATS 2020: 1803-1812 - [c4]Tomas Geffner, Justin Domke:
Approximation Based Variance Reduction for Reparameterization Gradients. NeurIPS 2020 - [i5]Tomas Geffner, Justin Domke:
Approximation Based Variance Reduction for Reparameterization Gradients. CoRR abs/2007.14634 (2020) - [i4]Tomas Geffner, Justin Domke:
On the Difficulty of Unbiased Alpha Divergence Minimization. CoRR abs/2010.09541 (2020)
2010 – 2019
- 2019
- [i3]Tomas Geffner, Justin Domke:
A Rule for Gradient Estimator Selection, with an Application to Variational Inference. CoRR abs/1911.01894 (2019) - 2018
- [c3]Tomas Geffner, Hector Geffner:
Compact Policies for Fully Observable Non-Deterministic Planning as SAT. ICAPS 2018: 88-96 - [c2]Tomas Geffner, Justin Domke:
Using Large Ensembles of Control Variates for Variational Inference. NeurIPS 2018: 9982-9992 - [i2]Tomas Geffner, Hector Geffner:
Compact Policies for Fully-Observable Non-Deterministic Planning as SAT. CoRR abs/1806.09455 (2018) - [i1]Tomas Geffner, Justin Domke:
Using Large Ensembles of Control Variates for Variational Inference. CoRR abs/1810.12482 (2018) - 2015
- [c1]Tomas Geffner, Hector Geffner:
Width-Based Planning for General Video-Game Playing. AIIDE 2015: 23-29
Coauthor Index
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last updated on 2023-09-27 22:41 CEST by the dblp team
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