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OASIS: Observation-Aware Simulation-Based Inference via Distributional Matching
Arya Farahi, Conghao Zhou, Ritwik Vashistha
We introduce OASIS, a simulation-based inference framework for scientific settings where observations are distorted by measurement error, selection effects, and other survey-specif…
Nonparametric Deconvolution and Denoising using Simulation Based Inference
Ritwik Vashistha, Abhra Sarkar, Arya Farahi
Latent signals are often obscured by measurement noise, yet encode the underlying laws and dynamics of complex systems; learning both the signals and their distributions remains a…
Convolutional Maximum Mean Discrepancy for Inference in Noisy Data
Ritwik Vashistha, Jeff M. Phillips, Abhra Sarkar +1
Modern data analyses frequently encounter settings where samples of variables are contaminated by measurement error. Ignoring measurement noise can substantially degrade statistica…
Simulation-Based Inference via Regression Projection and Batched Discrepancies
Arya Farahi, Jonah Rose, Paul Torrey
We analyze a lightweight simulation-based inference method that infers simulator parameters using only a regression-based projection of the observed data. After fitting a surrogate…