5 papers
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…
I-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers
Ritwik Vashistha, Arya Farahi
As probabilistic models continue to permeate various facets of our society and contribute to scientific advancements, it becomes a necessity to go beyond traditional metrics such a…
Effect Heterogeneity with Earth Observation in Randomized Controlled Trials: Exploring the Role of Data, Model, and Evaluation Metric Choice
Connor T. Jerzak, Ritwik Vashistha, Adel Daoud
Many social and environmental phenomena are associated with macroscopic changes in the built environment, captured by satellite imagery on a global scale and with daily temporal re…