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Found 7 papers, 6 papers with code
A Library of Mirrors: Deep Neural Nets in Low Dimensions are Convex Lasso Models with Reflection Features
We prove that training neural networks on 1-D data is equivalent to solving a convex Lasso problem with a fixed, explicitly defined dictionary matrix of features.
Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series
We introduce Bellman Conformal Inference (BCI), a framework that wraps around any time series forecasting models and provides approximately calibrated prediction intervals.
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks
We establish a permutation invariance condition that enables the validity of CP on graph data and provide an exact characterization of the test-time coverage.
Conformal Inference for Online Prediction with Arbitrary Distribution Shifts
We consider the problem of forming prediction sets in an online setting where the distribution generating the data is allowed to vary over time.
Testing for Outliers with Conformal p-values
We then introduce a new method to compute p-values that are both valid conditionally on the training data and independent of each other for different test points; this paves the way to stronger type-I error guarantees.
Derandomizing Knockoffs
Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously controlling the number or fraction of false positives.
Feature Importance
Variable Selection
Methodology
Applications
Metropolized Knockoff Sampling
Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives.
Methodology
