Unsupervised Domain Adaptation with Copula Models

We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive densities beyond the common exponential family, (b) we show how to leverage Sklar's theorem, the essence of the copula formulation relating the joint density to the copula dependency functions, to find effective feature mappings that mitigate the domain mismatch. By transforming the data to a copula domain, we show on a number of benchmark datasets (including human emotion estimation), and using different regression models for prediction, that we can achieve a more robust and accurate estimation of target labels, compared to recently proposed feature transformation (adaptation) methods.Comment: IEEE International Workshop On Machine Learning for Signal Processing 201

A Semiparametric Gaussian Copula Regression Model for Predicting Financial Risks from Earnings Calls

Earnings call summarizes the financial performance of a company, and it is an important indicator of the future financial risks of the company. We quantitatively study how earnings calls are correlated with the financial risks, with a special fo-cus on the financial crisis of 2009. In par-ticular, we perform a text regression task: given the transcript of an earnings call, we predict the volatility of stock prices from the week after the call is made. We pro-pose the use of copula: a powerful statis-tical framework that separately models the uniform marginals and their complex mul-tivariate stochastic dependencies, while not requiring any prior assumptions on the distributions of the covariate and the de-pendent variable. By performing probabil-ity integral transform, our approach moves beyond the standard count-based bag-of-words models in NLP, and improves pre-vious work on text regression by incor-porating the correlation among local fea-tures in the form of semiparametric Gaus-sian copula. In experiments, we show that our model significantly outperforms strong linear and non-linear discriminative baselines on three datasets under various settings.