Deep end-to-end causal inference T Geffner, J Antoran, A Foster, W Gong, C Ma, E Kiciman, A Sharma, ... arXiv preprint arXiv:2202.02195, 2022 | 73 | 2022 |
Compact policies for fully observable non-deterministic planning as SAT T Geffner, H Geffner Twenty-Eighth International Conference on Automated Planning and Scheduling, 2018 | 49* | 2018 |
Width-based planning for general video-game playing T Geffner, H Geffner Proceedings of the AAAI Conference on Artificial Intelligence and …, 2015 | 46 | 2015 |
Using large ensembles of control variates for variational inference T Geffner, J Domke Advances in Neural Information Processing Systems 31, 2018 | 29 | 2018 |
MCMC variational inference via uncorrected Hamiltonian annealing T Geffner, J Domke Advances in Neural Information Processing Systems 34, 639-651, 2021 | 27 | 2021 |
Compositional score modeling for simulation-based inference T Geffner, G Papamakarios, A Mnih International Conference on Machine Learning, 11098-11116, 2023 | 21* | 2023 |
On the difficulty of unbiased alpha divergence minimization T Geffner, J Domke arXiv preprint arXiv:2010.09541, 2020 | 17 | 2020 |
Empirical evaluation of biased methods for alpha divergence minimization T Geffner, J Domke arXiv preprint arXiv:2105.06587, 2021 | 11 | 2021 |
Langevin diffusion variational inference T Geffner, J Domke International Conference on Artificial Intelligence and Statistics, 576-593, 2023 | 7 | 2023 |
Approximation based variance reduction for reparameterization gradients T Geffner, J Domke Advances in Neural Information Processing Systems 33, 2397-2407, 2020 | 7 | 2020 |
A Rule for Gradient Estimator Selection, with an Application to Variational Inference T Geffner, J Domke arXiv preprint arXiv:1911.01894, 2019 | 4 | 2019 |
Dual control variate for faster black-box variational inference X Wang, T Geffner, J Domke arXiv preprint arXiv:2210.07290, 2022 | 3* | 2022 |
Variational inference with locally enhanced bounds for hierarchical models T Geffner, J Domke arXiv preprint arXiv:2203.04432, 2022 | 3 | 2022 |
Bending and Binding: Predicting Protein Flexibility upon Ligand Interaction using Diffusion Models X Zhang, T Geffner, M McPartlon, M Akdel, D Abramson, G Holt, ... NeurIPS 2023 Generative AI and Biology (GenBio) Workshop, 2023 | 2 | 2023 |
PINDER: The protein interaction dataset and evaluation resource D Kovtun, M Akdel, A Goncearenco, G Zhou, G Holt, D Baugher, D Lin, ... bioRxiv, 2024.07. 17.603980, 2024 | | 2024 |
Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization S Gu, M Xu, A Powers, W Nie, T Geffner, K Kreis, J Leskovec, A Vahdat, ... arXiv preprint arXiv:2407.01648, 2024 | | 2024 |
Towards Automatic and Robust Variational Inference T Geffner | | 2024 |
Estimating the effect of an action using a machine learning model C Zhang, J Antoran, AE Foster, M Defante, S Thomas, T Geffner, ... US Patent App. 17/579,877, 2023 | | 2023 |
Bridging Sequence and Structure: Latent Diffusion for Conditional Protein Generation M McPartlon, C Marquet, T Geffner, D Kovtun, A Goncearenco, ... | | |
LATENTDOCK: Protein-Protein Docking with Latent Diffusion M McPartlon, C Marquet, T Geffner, D Kovtun, A Goncearenco, ... | | |