Search Results for author:
Found 24 papers, 18 papers with code
Posterior Conformal Prediction
This article introduces a new method, posterior conformal prediction (PCP), which generates prediction intervals with both marginal and approximate conditional validity for clusters (or subgroups) naturally discovered in the data.
RandALO: Out-of-sample risk estimation in no time flat
Estimating out-of-sample risk for models trained on large high-dimensional datasets is an expensive but essential part of the machine learning process, enabling practitioners to optimally tune hyperparameters.
Can Unconfident LLM Annotations Be Used for Confident Conclusions?
We introduce Confidence-Driven Inference: a method that combines LLM annotations and LLM confidence indicators to strategically select which human annotations should be collected, with the goal of producing accurate statistical estimates and provably valid confidence intervals while reducing the number of human annotations needed.
Large language model validity via enhanced conformal prediction methods
These methods work by filtering claims from the LLM's original response if a scoring function evaluated on the claim fails to exceed a threshold calibrated via split conformal prediction.
Boosted Conformal Prediction Intervals
This paper introduces a boosted conformal procedure designed to tailor conformalized prediction intervals toward specific desired properties, such as enhanced conditional coverage or reduced interval length.
Active Statistical Inference
This means that for the same number of collected samples, active inference enables smaller confidence intervals and more powerful p-values.
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.
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.
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.
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.
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
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.
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.
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.
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).
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.
Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.
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
Dual-Reference Design for Holographic Coherent Diffraction Imaging
A new reference design is introduced for holographic coherent diffraction imaging.
Deep Knockoffs
This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models.
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.
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
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.
