Bayesian Inference
Generalization in the Face of Adaptivity: A Bayesian Perspective
However, differential privacy's worst-case nature entails scaling such noise to the range of the queries even for
Trust Y our: Gradient-based Intervention Targeting for Causal Discovery
Often, observational data alone is not enough to uniquely identify a system's causal structure.
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization Clรฉment Bรฉnard 1 Brian Staber 1 Sรฉbastien Da Veiga 2 1
Stein thinning is a promising algorithm proposed by Riabiz et al. [2022] for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradient of the log-target distribution, and is thus well-suited for Bayesian inference.
Credal Marginal MAP
Marginal MAP is a widely applicable mixed inference task that identifies the most likely assignment for a subset of variables (called MAP variables).
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Previous work has focused primarily on developing ML-based "meta-learners" that can provide point estimates of the conditional
ExPT: Synthetic Pretraining for Few-Shot Experimental Design
In this work, we address the more challenging yet realistic setting of few-shot experimental design, where only a few labeled data points of input designs and their corresponding values are available.
