We are concerned with obtaining well-calibrated output distributions from regression
models. Such distributions allow us to quantify the uncertainty that the model has regarding …

We propose a simple, practical, and intuitive approach for domain adaptation in
reinforcement learning. Our approach stems from the idea that the agent's experience in the …

Given a set of empirical observations, conditional density estimation aims to capture the
statistical relationship between a conditional variable $\mathbf {x} $ and a dependent …

There is a growing demand for nonparametric conditional density estimators (CDEs) in fields
such as astronomy and economics. In astronomy, for example, one can dramatically improve …

In some applications (eg, in cosmology and economics), the regression E [Z| x] is not
adequate to represent the association between a predictor x and a response Z because of …

In dynamic decision-making scenarios across business and healthcare, leveraging sample
trajectories from diverse populations can significantly enhance reinforcement learning (RL) …

Given samples from two densities p and q, density ratio estimation (DRE) is the problem of
estimating the ratio p/q. Two popular discriminative approaches to DRE are KL importance …

A large literature finds evidence that pricing kernels nonparametrically estimated from option
prices and historical returns are not monotonically decreasing in market index returns. We …

Kernel conditional mean embeddings (CMEs) offer a powerful framework for representing
conditional distribution, but they often face scalability and expressiveness challenges. In this …

We address the problem of unsupervised domain adaptation when the source domain
differs from the target domain because of a shift in the distribution of a latent subgroup …