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Found 21 papers, 5 papers with code
ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models
Undirected graphical models are used extensively in the biological and social sciences to encode a pattern of conditional independences between variables, where the absence of an edge between two nodes $a$ and $b$ indicates that the corresponding two variables $X_a$ and $X_b$ are believed to be conditionally independent, after controlling for all other measured variables.
Robust PCA with compressed data
The robust principal component analysis (RPCA) problem seeks to separate low-rank trends from sparse outlierswithin a data matrix, that is, to approximate a $n\times d$ matrix $D$ as the sum of a low-rank matrix $L$ and a sparse matrix $S$. We examine the robust principal component analysis (RPCA) problem under data compression, wherethe data $Y$ is approximately given by $(L + S)\cdot C$, that is, a low-rank $+$ sparse data matrix that has been compressed to size $n\times m$ (with $m$ substantially smaller than the original dimension $d$) via multiplication witha compression matrix $C$.
The p-filter: multi-layer FDR control for grouped hypotheses
In many practical applications of multiple hypothesis testing using the False Discovery Rate (FDR), the given hypotheses can be naturally partitioned into groups, and one may not only want to control the number of false discoveries (wrongly rejected null hypotheses), but also the number of falsely discovered groups of hypotheses (we say a group is falsely discovered if at least one hypothesis within that group is rejected, when in reality the group contains only nulls).
The knockoff filter for FDR control in group-sparse and multitask regression
We propose the group knockoff filter, a method for false discovery rate control in a linear regression setting where the features are grouped, and we would like to select a set of relevant groups which have a nonzero effect on the response.
Methodology
A unified treatment of multiple testing with prior knowledge using the p-filter
There is a significant literature on methods for incorporating knowledge into multiple testing procedures so as to improve their power and precision.
Alternating minimization and alternating descent over nonconvex sets
We analyze the performance of alternating minimization for loss functions optimized over two variables, where each variable may be restricted to lie in some potentially nonconvex constraint set.
Between hard and soft thresholding: optimal iterative thresholding algorithms
Instead, a general class of thresholding operators, lying between hard thresholding and soft thresholding, is shown to be optimal with the strongest possible convergence guarantee among all thresholding operators.
Prediction Rule Reshaping
Two methods are proposed for high-dimensional shape-constrained regression and classification.
The conditional permutation test for independence while controlling for confounders
Like the conditional randomization test of Cand\`es et al. (2018), our test relies on the availability of an approximation to the distribution of $X \mid Z$.
Methodology Statistics Theory Statistics Theory
An equivalence between critical points for rank constraints versus low-rank factorizations
Two common approaches in low-rank optimization problems are either working directly with a rank constraint on the matrix variable, or optimizing over a low-rank factorization so that the rank constraint is implicitly ensured.
Optimization and Control
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
Conformal Prediction Under Covariate Shift
We extend conformal prediction methodology beyond the case of exchangeable data.
Methodology
Predictive inference with the jackknife+
This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals.
Methodology
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.
Binary classification with corrupted labels
In a binary classification problem where the goal is to fit an accurate predictor, the presence of corrupted labels in the training data set may create an additional challenge.
Black-box tests for algorithmic stability
Algorithmic stability is a concept from learning theory that expresses the degree to which changes to the input data (e. g., removal of a single data point) may affect the outputs of a regression algorithm.
Bagging Provides Assumption-free Stability
Bagging is an important technique for stabilizing machine learning models.
Iterative Approximate Cross-Validation
Cross-validation (CV) is one of the most popular tools for assessing and selecting predictive models.
Integrating Uncertainty Awareness into Conformalized Quantile Regression
The reason is that the prediction intervals of CQR do not distinguish between two forms of uncertainty: first, the variability of the conditional distribution of $Y$ given $X$ (i. e., aleatoric uncertainty), and second, our uncertainty in estimating this conditional distribution (i. e., epistemic uncertainty).
Online conformal prediction with decaying step sizes
We introduce a method for online conformal prediction with decaying step sizes.
The Limits of Assumption-free Tests for Algorithm Performance
In particular, we make a distinction between two questions: how good is an algorithm $A$ at the problem of learning from a training set of size $n$, versus, how good is a particular fitted model produced by running $A$ on a particular training data set of size $n$?
