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Found 5 papers, 3 papers with code
Geometry-Aware Normalizing Wasserstein Flows for Optimal Causal Inference
We leverage optimal transport and Wasserstein gradient flows to develop causal inference methodologies with minimal variance in finite-sample settings, outperforming traditional methods like TMLE and AIPW.
Adaptive Bayesian Learning with Action and State-Dependent Signal Variance
This manuscript presents an advanced framework for Bayesian learning by incorporating action and state-dependent signal variances into decision-making models.
Inference for Model Misspecification in Interest Rate Term Structure using Functional Principal Component Analysis
Level, slope, and curvature are three commonly-believed principal components in interest rate term structure and are thus widely used in modeling.
Crises Do Not Cause Lower Short-Term Growth
It is commonly believed that financial crises "lead to" lower growth of a country during the two-year recession period, which can be reflected by their post-crisis GDP growth.
Spectral Regularization: an Inductive Bias for Sequence Modeling
Various forms of regularization in learning tasks strive for different notions of simplicity.
