A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New insights into CI tests reveal key factors for practical performance.
Hypothesis testing for graphs has been an important tool in applied research fields for more than two decades, and still remains a challenging problem as one often needs to draw inference from few replicates of large graphs. Recent studies in statistics and learning theory have provided some theoretical insights about …
Nonparametric tests via kernel embedding of distributions have witnessed a great deal of practical successes in recent years. However, statistical properties of these tests are largely unknown beyond consistency against a fixed alternative. To fill in this void, we study here the asymptotic properties of goodness-of-fi…
Improved A/B testing by leveraging system similarities.
New method combines Bloom filters and belief propagation for efficient group testing.
Proposes practical kernel tests for -divergences with theoretical guarantees.
Robust tests control type I error under data corruption.
In several realistic situations, an interactive learning agent can practice and refine its strategy before going on to be evaluated. For instance, consider a student preparing for a series of tests. She would typically take a few practice tests to know which areas she needs to improve upon. Based of the scores she obta…
Testing (conditional) independence of multivariate random variables is a task central to statistical inference and modelling in general - though unfortunately one for which to date there does not exist a practicable workflow. State-of-art workflows suffer from the need for heuristic or subjective manual choices, high c…
Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenario…
Develops a statistical test for IV, improving feature selection reliability.
This paper introduces differentially private permutation tests for hypothesis testing.
Private two-sample tests under LDP achieve minimax rates for multinomial and continuous data.
Estimates peeking effects in p-values to correct bias.
We present a new, practical algorithm to test whether a knot complement contains a closed essential surface. This property has important theoretical and algorithmic consequences; however, systematically testing it has until now been infeasibly slow, and current techniques only apply to specific families of knots. As a …
A new method reduces computational costs for testing RF variable importance measures.
Improves A/B testing for long-term outcomes in dynamic systems.
The Normal Means problem plays a fundamental role in many areas of modern high-dimensional statistics, both in theory and practice. And the Empirical Bayes (EB) approach to solving this problem has been shown to be highly effective, again both in theory and practice. However, almost all EB treatments of the Normal Mean…
This paper focusses on "safe" screening techniques for the LASSO problem. Motivated by the need for low-complexity algorithms, we propose a new approach, dubbed "joint" screening test, allowing to screen a set of atoms by carrying out one single test. The approach is particularized to two different sets of atoms, respe…
We review the main "omnibus procedures" for goodness-of-fit testing for copulas: tests based on the empirical copula process, on probability integral transformations, on Kendall's dependence function, etc, and some corresponding reductions of dimension techniques. The problems of finding asymptotic distribution-free te…
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life cycle, including design, implementation, tuning, testing, and deploymen…
Current clinical practice to monitor patients' health follows either regular or heuristic-based lab test (e.g. blood test) scheduling. Such practice not only gives rise to redundant measurements accruing cost, but may even lead to unnecessary patient discomfort. From the computational perspective, heuristic-based test …
Detects dense subhypergraphs in heterogeneous random hypergraphs.
The paper improves A/B testing for non-Gaussian data, ensuring reliable results with large sample sizes.
The paper improves confidence intervals for test error using cross-validation.
Develops a new method for neural network significance testing without strict constraints.
sig-MMD tests compare path distributions using kernel methods.
Paper proposes a robust hypothesis testing method using Sinkhorn distance.
New estimate reduces overfitting risk in machine learning models.
The statistical analysis of discrete data has been the subject of extensive statistical research dating back to the work of Pearson. In this survey we review some recently developed methods for testing hypotheses about high-dimensional multinomials. Traditional tests like the test and the likelihood ratio test ca…
New group testing method uses Belief Propagation for accurate screening.
Paper proposes a framework to detect distribution shifts using embedding space geometry.
Tests for equivariance in non-parametric regression models.
We propose a nonparametric sequential test that aims to address two practical problems pertinent to online randomized experiments: (i) how to do a hypothesis test for complex metrics; (ii) how to prevent type error inflation under continuous monitoring. The proposed test does not require knowledge of the underlying…
Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non-parametric setting, but many investigators cannot use KCIT with large datasets because the test sca…
New framework limits testing algorithmic stability under computational constraints.
This paper investigates the utilization of maximum and average distance correlations for multivariate independence testing. We characterize their consistency properties in high-dimensional settings with respect to the number of marginally dependent dimensions, compare the advantages of each test statistic, examine thei…
Reduces test set maintenance effort by 80-100%.
Research suggests using deep learning for better recommendation systems.
Semi-supervised method boosts two-sample testing with covariate data.
The ERI is a new index for measuring exam readiness.
Proposes a modified Morgan-Pitman test for evaluating variances in machine learning models.
Study tests financial market efficiency using random number generator tests.
New methods needed to evaluate uncertainty estimates in neural networks.
Simple methods combine statistical tests for out-of-distribution detection.
Randomization tests rely on simple data transformations and possess an appealing robustness property. In addition to being finite-sample valid if the data distribution is invariant under the transformation, these tests can be asymptotically valid under a suitable studentization of the test statistic, even if the invari…
Bayesian methods improve group testing for identifying infected patients.
We consider the problem of learning causal relationships from relational data. Existing approaches rely on queries to a relational conditional independence (RCI) oracle to establish and orient causal relations in such a setting. In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against …