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Found 11 papers, 2 papers with code
Predictive Inference in Multi-environment Scenarios
We address the challenge of constructing valid confidence intervals and sets in problems of prediction across multiple environments.
Spectrum-Aware Adjustment: A New Debiasing Framework with Applications to Principal Component Regression
This approach struggles when (i) covariates are non-Gaussian with heavy tails or asymmetric distributions, (ii) rows of the design exhibit heterogeneity or dependencies, and (iii) reliable feature covariance estimates are lacking.
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
We prove a new generalization bound that shows for any class of linear predictors in Gaussian space, the Rademacher complexity of the class and the training error under any continuous loss $\ell$ can control the test error under all Moreau envelopes of the loss $\ell$.
Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
In the regression setting, we provide theoretical guidelines based on an analytical transition point to determine whether it is more beneficial to merge or to ensemble for boosting with linear learners.
A New Central Limit Theorem for the Augmented IPW Estimator: Variance Inflation, Cross-Fit Covariance and Beyond
In recent times, inference for the ATE in the presence of high-dimensional covariates has been extensively studied.
High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models
We provide a "path-wise" characterization of the overlap between the output of the Langevin algorithm and the planted signal.
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations
We study an adversarial loss function for $k$ domains and precisely characterize its limiting behavior as $k$ grows, formalizing and proving the intuition, backed by experiments, that observing data from a larger number of domains helps.
Abstracting Fairness: Oracles, Metrics, and Interpretability
Our principal conceptual result is an extraction procedure that learns the underlying truth; moreover, the procedure can learn an approximation to this truth given access to a weak form of the oracle.
A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-$\ell_1$-Norm Interpolated Classifiers
This paper establishes a precise high-dimensional asymptotic theory for boosting on separable data, taking statistical and computational perspectives.
The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression
This paper rigorously establishes that the existence of the maximum likelihood estimate (MLE) in high-dimensional logistic regression models with Gaussian covariates undergoes a sharp `phase transition'.
The Likelihood Ratio Test in High-Dimensional Logistic Regression Is Asymptotically a Rescaled Chi-Square
When used for the purpose of statistical inference, logistic models produce p-values for the regression coefficients by using an approximation to the distribution of the likelihood-ratio test.
