Conformal Prediction via Regression-as-Classification: A Flexible Approach for Handling Complex Output Distributions
A novel approach to conformal prediction for regression that converts the regression problem into a classification problem, allowing the use of flexible classification-based conformal prediction techniques to handle complex output distributions such as heteroscedasticity and bimodality.
Overparameterized Multiple Linear Regression as Hyper-Curve Fitting: Insights into Predictive Power and Regularization
The application of the fixed-effect multiple linear regression model to an overparameterized dataset is equivalent to fitting the data with a hyper-curve parameterized by a single scalar parameter. This equivalence allows for a predictor-focused approach, where each predictor is described by a function of the chosen parameter, enabling the identification and removal of noisy or improper predictors to improve the predictive power of the linear model.
Large Language Models Demonstrate Surprising Regression Capabilities Without Additional Training
Large language models like GPT-4, Claude 3, and DBRX can perform linear and non-linear regression tasks effectively using only in-context examples, without any additional training or gradient updates.
Accounting for Misspecification Uncertainties in Underparametrized Regression Models
Misspecification uncertainties must be accounted for in underparametrized regression models to avoid severe underestimation of parameter uncertainties.
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