Uros Seljak | DeepAI

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Featured Co-authors

Francois Lanusse
11 publications
George Stein
8 publications
He Jia
6 publications
Biwei Dai
6 publications
Chirag Modi
5 publications
M. Kowalski
5 publications
Vanessa Böhm
4 publications
G. Bruno De Luca
3 publications
Eva Silverstein
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Jakob Robnik
3 publications
Adrian E. Bayer
3 publications

∙ 09/20/2023

Flow Annealed Kalman Inversion for Gradient-Free Inference in Bayesian Inverse Problems

For many scientific inverse problems we are required to evaluate an expe...

0 Richard D. P. Grumitt, et al. ∙

∙ 07/18/2023

Field-Level Inference with Microcanonical Langevin Monte Carlo

Field-level inference provides a means to optimally extract information ...

0 Adrian E. Bayer, et al. ∙

∙ 06/07/2023

Multiscale Flow for Robust and Optimal Cosmological Analysis

We propose Multiscale Flow, a generative Normalizing Flow that creates s...

0 Biwei Dai, et al. ∙

∙ 03/31/2023

Microcanonical Langevin Monte Carlo

We propose a method for sampling from an arbitrary distribution exp[-S()...

0 Jakob Robnik, et al. ∙

∙ 12/16/2022

Microcanonical Hamiltonian Monte Carlo

We develop Microcanonical Hamiltonian Monte Carlo (MCHMC), a class of mo...

0 Jakob Robnik, et al. ∙

∙ 07/15/2022

A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series

We construct a physically-parameterized probabilistic autoencoder (PAE) ...

0 George Stein, et al. ∙

∙ 05/27/2022

Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference

We propose a general purpose Bayesian inference algorithm for expensive ...

0 Uros Seljak, et al. ∙

∙ 02/10/2022

Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis

Our universe is homogeneous and isotropic, and its perturbations obey tr...

0 Biwei Dai, et al. ∙

∙ 12/17/2021

MUSE: Marginal Unbiased Score Expansion and Application to CMB Lensing

We present the marginal unbiased score expansion (MUSE) method, an algor...

0 Marius Millea, et al. ∙

∙ 08/13/2021

Self-Calibrating the Look-Elsewhere Effect: Fast Evaluation of the Statistical Significance Using Peak Heights

In experiments where one searches a large parameter space for an anomaly...

0 Adrian E. Bayer, et al. ∙

∙ 12/21/2020

Unsupervised in-distribution anomaly detection of new physics through conditional density estimation

Anomaly detection is a key application of machine learning, but is gener...

0 George Stein, et al. ∙

∙ 10/06/2020

Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning

The goal of generative models is to learn the intricate relations betwee...

0 Biwei Dai, et al. ∙

∙ 07/27/2020

The look-elsewhere effect from a unified Bayesian and frequentist perspective

When searching over a large parameter space for anomalies such as events...

0 Adrian E. Bayer, et al. ∙

∙ 07/01/2020

Sliced Iterative Generator

We introduce the Sliced Iterative Generator (SIG), an iterative generati...

0 Biwei Dai, et al. ∙

∙ 06/09/2020

Probabilistic Auto-Encoder

We introduce the Probabilistic Auto-Encoder (PAE), a generative model wi...

0 Vanessa Böhm, et al. ∙

∙ 12/12/2019

Normalizing Constant Estimation with Gaussianized Bridge Sampling

Normalizing constant (also called partition function, Bayesian evidence,...

0 He Jia, et al. ∙

∙ 10/22/2019

Uncertainty Quantification with Generative Models

We develop a generative model-based approach to Bayesian inverse problem...

0 Vanessa Böhm, et al. ∙

∙ 10/16/2019

Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics

A common statistical problem in econometrics is to estimate the impact o...

0 Chirag Modi, et al. ∙

∙ 01/14/2019

Posterior inference unchained with EL_2O

Statistical inference of analytically non-tractable posteriors is a diff...

0 Uros Seljak, et al. ∙

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