Geffner, Tomas

22 publications

ICML 2025 Adaptive Flow Matching for Resolving Small-Scale Physics Stathi Fotiadis, Noah D Brenowitz, Tomas Geffner, Yair Cohen, Michael Pritchard, Arash Vahdat, Morteza Mardani

ICLRW 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 Kucukbenli

ICML 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 Kucukbenli

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

ICLRW 2025 Learning Straight Flows by Learning Curved Interpolants Shiv Shankar, Tomas Geffner

ICLR 2025 ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids Hannes Stark, Bowen Jing, Tomas Geffner, Jason Yim, Tommi Jaakkola, Arash Vahdat, Karsten Kreis

ICLR 2025 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 Kucukbenli, Arash Vahdat, Karsten Kreis

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

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

TMLR 2024 Deep End-to-End Causal Inference Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang

AISTATS 2024 Joint Control Variate for Faster Black-Box Variational Inference Xi Wang, Tomas Geffner, Justin Domke

NeurIPSW 2023 Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction Using Diffusion Models Xuejin Zhang, Tomas Geffner, Matt McPartlon, Mehmet Akdel, Dylan Abramson, Graham Holt, Alexander Goncearenco, Luca Naef, Michael Bronstein

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

AISTATS 2023 Langevin Diffusion Variational Inference Tomas Geffner, Justin Domke

NeurIPSW 2022 Deep End-to-End Causal Inference Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

NeurIPSW 2022 Score Modeling for Simulation-Based Inference Tomas Geffner, George Papamakarios, Andriy Mnih

ICML 2022 Variational Inference with Locally Enhanced Bounds for Hierarchical Models Tomas Geffner, Justin Domke

NeurIPS 2021 MCMC Variational Inference via Uncorrected Hamiltonian Annealing Tomas Geffner, Justin Domke

ICML 2021 On the Difficulty of Unbiased Alpha Divergence Minimization Tomas Geffner, Justin Domke

AISTATS 2020 A Rule for Gradient Estimator Selection, with an Application to Variational Inference Tomas Geffner, Justin Domke

NeurIPS 2020 Approximation Based Variance Reduction for Reparameterization Gradients Tomas Geffner, Justin Domke

NeurIPS 2018 Using Large Ensembles of Control Variates for Variational Inference Tomas Geffner, Justin Domke