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 (NeurIPS, 2024)

Stochastic Flow Matching for Resolving Small-Scale Physics. - Stathi Fotiadis; Noah D. Brenowitz; Tomas Geffner; Yair Cohen; Michael S. Pritchard; Arash Vahdat; Morteza Mardani (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)

Proteina: Scaling Flow-based Protein Structure Generative Models. - Tomas Geffner; Kieran Didi; Zuobai Zhang; Danny Reidenbach; Zhonglin Cao; Jason Yim; Mario Geiger; Christian Dallago; Emine Küçükbenli; Arash Vahdat; Karsten Kreis (ICLR, 2025)

Truncated Consistency Models. - Sangyun Lee; Yilun Xu; Tomas Geffner; Giulia Fanti; Karsten Kreis; Arash Vahdat; Weili Nie (ICLR, 2025)

ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids. - Hannes Stärk; Bowen Jing; Tomas Geffner; Jason Yim; Tommi S. Jaakkola; Arash Vahdat; Karsten Kreis (ICLR, 2025)

Energy-Based Diffusion Language Models for Text Generation. - Minkai Xu; Tomas Geffner; Karsten Kreis; Weili Nie; Yilun Xu; Jure Leskovec; Stefano Ermon; Arash Vahdat (ICLR, 2025)

Learning Straight Flows by Learning Curved Interpolants. - Shiv Shankar; Tomas Geffner (CoRR, 2025)

La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching. - Tomas Geffner; Kieran Didi; Zhonglin Cao; Danny Reidenbach; Zuobai Zhang; Christian Dallago; Emine Küçükbenli; Karsten Kreis; Arash Vahdat (CoRR, 2025)

Efficient Molecular Conformer Generation with SO(3)-Averaged Flow Matching and Reflow. - Zhonglin Cao; Mario Geiger; Allan dos Santos Costa; Danny Reidenbach; Karsten Kreis; Tomas Geffner; Franco Pellegrini; Guoqing Zhou; Emine Küçükbenli (CoRR, 2025)