6 papers
Minimum Distance Summaries for Robust Neural Posterior Estimation
Sherman Khoo, Dennis Prangle, Song Liu +1
Simulation-based inference (SBI) enables amortized Bayesian inference by first training a neural posterior estimator (NPE) on prior-simulator pairs, typically through low-dimension…
Direct Fisher Score Estimation for Likelihood Maximization
Sherman Khoo, Yakun Wang, Song Liu +1
We study the problem of likelihood maximization when the likelihood function is intractable but model simulations are readily available. We propose a sequential, gradient-based opt…
Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs
Masaki Adachi, Anita Yang, Yakun Wang +1
Guided-diffusion black-box optimization (BO) has shown strong empirical performance on structured design problems such as molecules and crystals, but its regret behavior remains po…
Learning to Rank for Selected Configuration Interaction
Wan Nie, Songwei Liu, Yingying Yu +2
The accurate description of electron correlation is a central challenge in computational chemistry, with selected configuration interaction (SCI) emerging as a powerful tool to app…
Factorized neural posterior estimation for rapid and reliable inference of parameterized post-Einsteinian deviation parameters in gravitational waves
Yong-Xin Zhang, Tian-Yang Sun, Chun-Yu Xiong +5
The direct detection of gravitational waves (GWs) by LIGO has strikingly confirmed general relativity (GR), but testing GR via GWs requires estimating parameterized post-Einsteinia…
Self-sufficient Independent Component Analysis via KL Minimizing Flows
Song Liu
We study the problem of learning disentangled signals from data using non-linear Independent Component Analysis (ICA). Motivated by advances in self-supervised learning, we propose…