arXiv research

The paper reviews exchangeability and its implications for conformal prediction and rank tests.

Paper extends multivariate rank tests for robust subspace detection.

Max-rank improves multiple testing in conformal prediction.

Sharp bounds derived for test error of finite-rank kernel ridge regression.

Weighted log-rank tests are arguably the most widely used tests by practitioners for the two-sample problem in the context of right-censored data. Many approaches have been considered to make weighted log-rank tests more robust against a broader family of alternatives, among them, considering linear combinations of wei…

The paper designs tests for comparing ranked preference data and finds significant differences.

StatLoRA uses statistical inference to allocate ranks in LoRA fine-tuning, improving performance.

New gossip algorithms improve robustness of rank-based statistics in decentralized systems.

The statistical comparison of multiple algorithms over multiple data sets is fundamental in machine learning. This is typically carried out by the Friedman test. When the Friedman test rejects the null hypothesis, multiple comparisons are carried out to establish which are the significant differences among algorithms. …

New method tests independence using ROC analysis and bipartite ranking.

Framework benchmarks optimizers on multiple criteria.

Proposes a new method to improve Bayesian computation accuracy using flexible classification.

Paper develops a new test for high-dimensional matrix-valued data.

Hypothesis tests are a crucial statistical tool for data mining and are the workhorse of scientific research in many fields. Here we present a differentially private analogue of the classic Wilcoxon signed-rank hypothesis test, which is used when comparing sets of paired (e.g., before-and-after) data values. We present…

There has been an increasing interest in testing the equality of large Pearson's correlation matrices. However, in many applications it is more important to test the equality of large rank-based correlation matrices since they are more robust to outliers and nonlinearity. Unlike the Pearson's case, testing the equality…

Optimal test for detecting signal in noisy matrix model.

New GoF test improves change point detection in multivariate time series.

This paper improves model robustness to underrepresented groups using ranking metrics and reweighting.

Optimizes COVID-19 testing policy using a Multi-Armed Bandit approach.

This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The results show that model interpolation, though simple, achieves the best results on all the open test sets where the test data is very different …

We introduce a general non-parametric independence test between right-censored survival times and covariates, which may be multivariate. Our test statistic has a dual interpretation, first in terms of the supremum of a potentially infinite collection of weight-indexed log-rank tests, with weight functions belonging to …

The paper optimizes A/B tests by balancing lift and cost in large-scale settings.

This paper tackles ranking-based performance normalization for optimization algorithms.

The problem of frequent pattern mining has been studied quite extensively for various types of data, including sets, sequences, and graphs. Somewhat surprisingly, another important type of data, namely rank data, has received very little attention in data mining so far. In this paper, we therefore addresses the problem…

Improved GoF statistics using entropy-regularized optimal transport for multivariate rank.

New methods ensure feature importance rankings are correct with high probability.

A method for rank verification in multivariate Gaussian data, improving on existing approaches.

We implement a method for re-ranking top-10 results of a state-of-the-art question answering (QA) system. The goal of our re-ranking approach is to improve the answer selection given the user question and the top-10 candidates. We focus on improving deployed QA systems that do not allow re-training or re-training comes…

We consider the weak detection problem in a rank-one spiked Wigner data matrix where the signal-to-noise ratio is small so that reliable detection is impossible. We propose a hypothesis test on the presence of the signal by utilizing the linear spectral statistics of the data matrix. The test is data-driven and does no…

Improves detection of low-rank signals from noisy data matrices.

ELM detects mislabels in Finnish academic publication ranks.

We show that for the problem of testing if a matrix $A \in F^{n \times n}$ has rank at most $d$, or requires changing an $ε$-fraction of entries to have rank at most $d$, there is a non-adaptive query algorithm making $\widetilde{O}(d^2/ε)$ queries. Our algorithm works for any field $F$. This improves upon the previous…

Develops a new fairness learning approach for multi-task regression models.

Deep networks learn clean structure before memorizing corrupted labels, leaving a spectral signature in gradient centered scatter.

The study examines denoising and noisy-input regression under distribution shift, revealing double descent behavior and insights for data augmentation.

A new framework for paired-sample testing in high-dimensional data.

MOSAIC detects change points in dynamic networks with low-rank and sparse changes.

We develop a pivotal test to assess the statistical significance of the feature variables in a single-layer feedforward neural network regression model. We propose a gradient-based test statistic and study its asymptotics using nonparametric techniques. Under technical conditions, the limiting distribution is given by …

In this paper we consider general rank minimization problems with rank appearing in either objective function or constraint. We first establish that a class of special rank minimization problems has closed-form solutions. Using this result, we then propose penalty decomposition methods for general rank minimization pro…

Low-rank approximations of data matrices are an important dimensionality reduction tool in machine learning and regression analysis. We consider the case of categorical variables, where it can be formulated as the problem of finding low-rank approximations to Boolean matrices. In this paper we give what is to the best …

New algorithm achieves almost exact graph matching in almost quadratic time.

The paper improves spectral ranking methods for diverse comparison graphs.

This paper presents a multi-dimensional computational method to predict the spatial variation data inside and across multiple dies of a wafer. This technique is based on tensor computation. A tensor is a high-dimensional generalization of a matrix or a vector. By exploiting the hidden low-rank property of a high-dimens…

Proposes a method to infer ranking properties and top-K rankings with uncertainty quantification.

We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at alpha-…

Optimizes tensor rank selection for neural network compression.

While autonomous vehicle (AV) technology has shown substantial progress, we still lack tools for rigorous and scalable testing. Real-world testing, the $\textit{de-facto}$ evaluation method, is dangerous to the public. Moreover, due to the rare nature of failures, billions of miles of driving are needed to statisticall…

A new method for uplift modeling using learning-to-rank techniques.