Normalizing Constant Estimation with Gaussianized Bridge Sampling. - He Jia; Uros Seljak (AABI, 2019)

Posterior inference unchained with EL_2O. - Uros Seljak; Byeonghee Yu (CoRR, 2019)

Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics. - Chirag Modi 002; Uros Seljak (CoRR, 2019)

Uncertainty Quantification with Generative Models. - Vanessa Böhm; François Lanusse; Uros Seljak (CoRR, 2019)

Probabilistic Auto-Encoder. - Vanessa Böhm; Uros Seljak (CoRR, 2020)

Sliced Iterative Generator. - Biwei Dai; Uros Seljak (CoRR, 2020)

Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning. - Biwei Dai; Uros Seljak (CoRR, 2020)

Unsupervised in-distribution anomaly detection of new physics through conditional density estimation. - George Stein; Uros Seljak; Biwei Dai (CoRR, 2020)

Sliced Iterative Normalizing Flows. - Biwei Dai; Uros Seljak (ICML, 2021)

FlowPM: Distributed TensorFlow implementation of the FastPM cosmological N-body solver. - Chirag Modi 002; François Lanusse; Uros Seljak (Astron. Comput., 2021)

Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference. - Richard D. P. Grumitt; Biwei Dai; Uros Seljak (NeurIPS, 2022)

Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis. - Biwei Dai; Uros Seljak (CoRR, 2022)

A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series. - George Stein; Uros Seljak; Vanessa Böhm; G. Aldering; P. Antilogus; C. Aragon; S. Bailey; C. Baltay; Sébastien Bongard; K. Boone; C. Buton; Y. Copin; et al. (CoRR, 2022)

Statistical Significance Testing for Mixed Priors: A Combined Bayesian and Frequentist Analysis. - Jakob Robnik; Uros Seljak (Entropy, 2022)

Probabilistic Autoencoder. - Vanessa M. Boehm; Uros Seljak (Trans. Mach. Learn. Res., 2022)

pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology. - Minas Karamanis; David Nabergoj; Florian Beutler; John A. Peacock; Uros Seljak (J. Open Source Softw., 2022)

Multiscale Flow for Robust and Optimal Cosmological Analysis. - Biwei Dai; Uros Seljak (CoRR, 2023)

Deterministic Langevin Unconstrained Optimization with Normalizing Flows. - James M. Sullivan; Uros Seljak (CoRR, 2023)

Microcanonical Hamiltonian Monte Carlo. - Jakob Robnik; Giuseppe Bruno De Luca; Eva Silverstein; Uros Seljak (J. Mach. Learn. Res., 2023)

Fluctuation without dissipation: Microcanonical Langevin Monte Carlo. - Jakob Robnik; Uros Seljak (AABI, 2024)

Persistent Sampling: Unleashing the Potential of Sequential Monte Carlo. - Minas Karamanis; Uros Seljak (CoRR, 2024)

Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks. - Emanuel Sommer; Jakob Robnik; Giorgi Nozadze; Uros Seljak; David Rügamer (ICLR, 2025)

Initial Conditions from Galaxies: Machine-Learning Subgrid Correction to Standard Reconstruction. - Liam Parker; Adrian E. Bayer; Uros Seljak (CoRR, 2025)

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo. - Minas Karamanis; Uros Seljak (Stat. Comput., 2025)