Width-Based Planning for General Video-Game Playing. - Tomas Geffner; Hector Geffner (AIIDE, 2015)

Compact Policies for Fully Observable Non-Deterministic Planning as SAT. - Tomas Geffner; Hector Geffner (ICAPS, 2018)

Using Large Ensembles of Control Variates for Variational Inference. - Tomas Geffner; Justin Domke (NeurIPS, 2018)

A Rule for Gradient Estimator Selection, with an Application to Variational Inference. - Tomas Geffner; Justin Domke (AISTATS, 2020)

Approximation Based Variance Reduction for Reparameterization Gradients. - Tomas Geffner; Justin Domke (NeurIPS, 2020)

On the difficulty of unbiased alpha divergence minimization. - Tomas Geffner; Justin Domke (ICML, 2021)

MCMC Variational Inference via Uncorrected Hamiltonian Annealing. - Tomas Geffner; Justin Domke (NeurIPS, 2021)

Empirical Evaluation of Biased Methods for Alpha Divergence Minimization. - Tomas Geffner; Justin Domke (CoRR, 2021)

Variational Inference with Locally Enhanced Bounds for Hierarchical Models. - Tomas Geffner; Justin Domke (ICML, 2022)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Miltiadis Allamanis; Cheng Zhang 005 (CoRR, 2022)

Score Modeling for Simulation-based Inference. - Tomas Geffner; George Papamakarios; Andriy Mnih (CoRR, 2022)

A Dual Control Variate for doubly stochastic optimization and black-box variational inference. - Xi Wang; Tomas Geffner; Justin Domke (CoRR, 2022)

Langevin Diffusion Variational Inference. - Tomas Geffner; Justin Domke (AISTATS, 2023)

Compositional Score Modeling for Simulation-Based Inference. - Tomas Geffner; George Papamakarios; Andriy Mnih (ICML, 2023)

Joint control variate for faster black-box variational inference. - Xi Wang; Tomas Geffner; Justin Domke (AISTATS, 2024)

Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization. - Siyi Gu; Minkai Xu; Alexander S. Powers; Weili Nie; Tomas Geffner; Karsten Kreis; Jure Leskovec; Arash Vahdat; Stefano Ermon (CoRR, 2024)

Deep End-to-end Causal Inference. - Tomas Geffner; Javier Antorán; Adam Foster 001; Wenbo Gong 001; Chao Ma 019; Emre Kiciman; Amit Sharma 007; Angus Lamb; Martin Kukla; Nick Pawlowski; Agrin Hilmkil; Joel Jennings; et al. (Trans. Mach. Learn. Res., 2024)