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Found 19 papers, 14 papers with code
Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.
With Malice Towards None: Assessing Uncertainty via Equalized Coverage
An important factor to guarantee a fair use of data-driven recommendation systems is that we should be able to communicate their uncertainty to decision makers.
Cross-Prediction-Powered Inference
We show that cross-prediction is consistently more powerful than an adaptation of prediction-powered inference in which a fraction of the labeled data is split off and used to train the model.
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control
We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees.
Deep Knockoffs
This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.
Classification with Valid and Adaptive Coverage
Conformal inference, cross-validation+, and the jackknife+ are hold-out methods that can be combined with virtually any machine learning algorithm to construct prediction sets with guaranteed marginal coverage.
Conformal Inference of Counterfactuals and Individual Treatment Effects
At the moment, much emphasis is placed on the estimation of the conditional average treatment effect via flexible machine learning algorithms.
Selection by Prediction with Conformal p-values
Decision making or scientific discovery pipelines such as job hiring and drug discovery often involve multiple stages: before any resource-intensive step, there is often an initial screening that uses predictions from a machine learning model to shortlist a few candidates from a large pool.
A comparison of some conformal quantile regression methods
We compare two recently proposed methods that combine ideas from conformal inference and quantile regression to produce locally adaptive and marginally valid prediction intervals under sample exchangeability (Romano et al., 2019; Kivaranovic et al., 2019).
Statistical Inference for Fairness Auditing
Our methods can be used to flag subpopulations affected by model underperformance, and certify subpopulations for which the model performs adequately.
Active Statistical Inference
This means that for the same number of collected samples, active inference enables smaller confidence intervals and more powerful p-values.
Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets
Deep neural networks and other sophisticated machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions.
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
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.
Robust subspace clustering
Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space.
Dual-Reference Design for Holographic Coherent Diffraction Imaging
A new reference design is introduced for holographic coherent diffraction imaging.
The limits of distribution-free conditional predictive inference
We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally.
Statistics Theory Statistics Theory
SLOPE - Adaptive variable selection via convex optimization
SLOPE, short for Sorted L-One Penalized Estimation, is the solution to \[\min_{b\in\mathbb{R}^p}\frac{1}{2}\Vert y-Xb\Vert _{\ell_2}^2+\lambda_1\vert b\vert _{(1)}+\lambda_2\vert b\vert_{(2)}+\cdots+\lambda_p\vert b\vert_{(p)},\] where $\lambda_1\ge\lambda_2\ge\cdots\ge\lambda_p\ge0$ and $\vert b\vert_{(1)}\ge\vert b\vert_{(2)}\ge\cdots\ge\vert b\vert_{(p)}$ are the decreasing absolute values of the entries of $b$.
Methodology
