Unified framework for global and local two-sample conditional distribution testing.
problem Testing equality of two conditional distributions.
method Distance and kernel methods, conditional U-statistics, local bootstrap.
result Developed reliable global and local tests.
The paper introduces localized conformal p-values for conditional testing problems.
problem Addressing conditional testing problems in statistics.
method Localized conformal p-values defined by inverting prediction intervals.
result Proposes procedures for conditional outlier detection and label screening with FDR and FWER control.
Develops a test for conditional local independence of counting processes.
problem Testing the hypothesis of conditional local independence among continuous time stochastic processes.
method Introduces a new functional parameter, the Local Covariance Measure (LCM), and proposes a test called (X)-LCT using nonparametric estimators and sample splitting or cross-fitting.
result The (X)-LCT test can be controlled uniformly with modest rates, and it works well without restrictive parametric assumptions.
Develops tests for conditional symmetry under group actions.
problem Testing conditional symmetry in distributions under group actions.
method Nonparametric randomization tests with kernel methods and asymptotic consistency.
result Tests achieve finite-sample Type I error control and power.
MixCIT tests conditional independence for mixed data types efficiently and reliably.
problem Testing conditional independence for mixed data types, especially when at least one is continuous.
method Graph-based test statistic comparing kernel similarities, debiased local-polynomial approach for continuous variables.
result Unified, efficient, and statistically guaranteed solution across heterogeneous data types.
Conditional independence testing is a fundamental problem underlying causal discovery and a particularly challenging task in the presence of nonlinear and high-dimensional dependencies. Here a fully non-parametric test for continuous data based on conditional mutual information combined with a local permutation scheme …
Fast nonparametric conditional independence testing via two-stage regression
problem Fast nonparametric conditional independence testing
method BLITZ (Broad-to-Local Independence Testing via residualiZation)
result Better null calibration than fast kernel, random-feature, and regression-based competitors
Paper introduces a new test for conditional independence using weighted partial copulas.
problem Testing conditional independence between variables.
method The approach uses a weighted partial copula function and a bootstrap procedure to compute regions of rejection.
result The proposed test has competitive power compared to existing methods.
New method tests independence with single nonstationary time series.
problem Testing independence in nonstationary nonlinear time series.
method Time-varying nonlinear regression, local long-run covariance estimation, strong Gaussian approximation.
result First framework for conditional independence testing with a single realization of a nonstationary nonlinear process.
Polynomial delay algorithm tests causal models with hidden variables.
problem Testing causal models with hidden variables in polynomial delay.
method c-component local Markov property (C-LMP) and polynomial delay algorithm.
result First algorithm for poly-delay testing of CIs in causal graphs with hidden variables.
Alternative hypothesis tests for class-conditional noise using local maximum likelihood.
problem Assessing label noise in supervised learning datasets.
method Proposes hypothesis tests based on local maximum likelihood estimation for nonparametric logistic regression.
result Shows improved applicability and flexibility of the proposed tests compared to parametric approaches.
CoLT assesses neural posterior estimates by detecting discrepancies across conditioning inputs.
problem Validating neural posterior estimates from limited data.
method Conditional Localization Test (CoLT) learns a localization function to detect strong deviations.
result CoLT provides rigorous guarantees and practical scalability for comparing true and neural posterior distributions.
FastKCI speeds up KCI tests for causal inference on large datasets.
problem Cubic computational complexity of kernel-based conditional independence tests.
method Mixture-of-experts approach with parallel Gaussian process inference.
result Substantial computational speedups with maintained statistical power.
New method tests CMI using deep neural networks for high-dimensional data.
problem Testing conditional mean independence in high-dimensional settings.
method Population CMI measure and bootstrap-based testing with deep generative neural networks.
result Strong empirical performance and versatility in various scenarios.
New test for conditional independence using GNNs avoids estimating conditional distributions.
problem Testing conditional independence of X and Y given Z. method Proposes a non-parametric testing procedure using GNNs to sample from marginal conditional distributions.
result Test statistic is doubly robust against GNN approximation errors.
New theory for local parameterization of deep ReLU networks.
problem Determining local parameters of deep ReLU neural networks.
method Introducing local lifting operators and charts of a manifold, deriving necessary and sufficient conditions for local identifiability.
result Sharp and testable conditions for local identifiability of deep ReLU networks.
We exhibit sufficient conditions such that components of a multidimensional SDE giving rise to a local martingale M are strict local martingales or martingales. We assume that the equations have diffusion coefficients of the form σ(Mt,vt), with vt being a stochastic volatility term.
Estimates and tests treatment effects on entire outcome distributions.
problem Treatment effects on entire outcome distributions, not just averages.
method Proposes a novel estimand and doubly robust estimator, develops a test.
result First test with provably valid type 1 error guarantees in this setting.
Scientific discovery is limited by hypothesis redundancy, and hybrid methods can exploit non-local exploration.
problem Limitation of scientific discovery due to hypothesis redundancy.
method Hybrid discovery systems combining structured local search with LLM-generated non-local proposals.
result Hybrid methods can exploit non-local exploration when three geometric conditions co-occur.
New findings control FDR for online testing methods under positive dependence.
problem Maintaining FDR control for online testing methods under positive dependence.
method Developed new methods to control FDR for online testing procedures under positive dependence.
result SAFFRON and LORD control FDR under positive dependence, not just conditional superuniformity.
Detects which features have shifted in data distributions.
problem Identifying which specific features have caused a distribution shift.
method Formalizes the problem as multiple conditional distribution hypothesis tests, proposes non-parametric and parametric statistical tests, and uses a test statistic based on the density model score function.
result Demonstrates methods for identifying when and where a shift occurs in multivariate time-series data.
Many mathematical imaging problems are posed as non-convex optimization problems. When numerically tractable global optimization procedures are not available, one is often interested in testing ex post facto whether or not a locally convergent algorithm has found the globally optimal solution. When the problem is formu…
New method uses machine learning to improve statistical inference.
problem Performing inference on conditional functionals with scarce labeled data.
method Combines localization with prediction-based variance reduction.
result Valid and sharp confidence intervals for conditional functionals.
Develops non-parametric tests for group symmetry in data.
problem Lack of statistical tests for group symmetry in data.
method Formulates and implements non-parametric tests for distributional symmetry under specified groups.
result Develops tests for conditional invariance/equivariance and applies them to real-world data.
CP-ROC bands improve graph classification accuracy and uncertainty quantification.
problem Uncertainty quantification and robustness to distributional shifts in graph classification.
method Conditional Prediction ROC (CP-ROC) bands for graph classification, developed for TGNNs and adaptable to GNNs.
result Statistically guaranteed coverage for CP-ROC under local exchangeability condition, improving prediction reliability.
The paper analyzes the power of MX CI tests and finds likelihood-based statistics most powerful.
problem Testing conditional independence under model-X assumptions.
method Conditional randomization test (CRT) and MX knockoffs.
result Likelihood-based statistics are most powerful in MX CI tests.
New exact tests detect changepoints in binary and count data, especially when normal approximations fail.
problem Detecting changepoints in multichannel binary and count data.
method Exact tests combining two-sample conditional tests with multiplicity correction.
result Exact tests are much more powerful than asymptotic tests in various settings.
We provide a formulation for Local Support Vector Machines (LSVMs) that generalizes previous formulations, and brings out the explicit connections to local polynomial learning used in nonparametric estimation literature. We investigate the simplest type of LSVMs called Local Linear Support Vector Machines (LLSVMs). For…
We consider the matrix completion problem with a deterministic pattern of observed entries. In this setting, we aim to answer the question: under what condition there will be (at least locally) unique solution to the matrix completion problem, i.e., the underlying true matrix is identifiable. We answer the question fro…
Tests if vertices in graphs have the same latent positions.
problem Testing equality of latent positions in random graphs.
method Empirical Mahalanobis distances from spectral embeddings.
result Test statistics follow chi-square distributions under null and local alternatives.
Develops model-free methods for event history analysis and efficient covariate adjustment.
problem Estimating treatment effects while accounting for confounding and understanding event history.
method Model-free prediction techniques, Local Covariance Measure (LCM), Debiased Outcome-adapted Propensity Estimator (DOPE), Aalen Covariance Measure (ACM).
result Demonstrates the effectiveness and robustness of the proposed methods in various settings.
We present a novel family of nonparametric omnibus tests of the hypothesis that two unknown but estimable functions are equal in distribution when applied to the observed data structure. We developed these tests, which represent a generalization of the maximum mean discrepancy tests described in Gretton et al. [2006], …
Develops a method for reverse stress testing in multivariate scenarios.
problem Reconstructing a multivariate stress scenario from a single exogenous shock.
method Maximizing conditional density under three distributional assumptions.
result Simulated scenarios are economically coherent and reproduce risk-reward asymmetry.
Method discovers local independence in systems with continuous variables.
problem Applying Context-Specific Independence (CSI) to continuous variables is impractical.
method Neural contextual decomposition (NCD) learns partition of joint outcome space.
result NCD successfully discovers local independence in synthetic and real-world systems.
The paper refines NOTEARS for learning Bayesian networks, improving accuracy and efficiency.
problem Learning Bayesian networks from continuous optimization.
method Generalized algebraic characterizations and Karush-Kuhn-Tucker (KKT) conditions for optimization.
result Local search post-processing improves structural Hamming distance by a factor of 2 or more.
The paper tests semantic importance in opaque models using betting.
problem Precise statistical guarantees for semantic concepts in black-box models.
method Formalizes global and local statistical importance via conditional independence and SKIT.
result Shows effectiveness and flexibility of the framework on various models.
New methods test discrete distributions faster with local privacy constraints.
problem Testing discrete distributions under local differential privacy constraints.
method Efficient randomized algorithms and test procedures, both non-interactive and interactive.
result Faster separation rates in interactive privacy mechanisms.
Characterizes solutions to Z-critical equations on surfaces using effective conditions.
problem Characterizing solutions to Z-critical equations on compact Kähler surfaces.
method Uses effective conditions and Picard number bounds to characterize solutions.
result Characterizes optimally destabilizing curves for Donaldson's J-equation and deformed Hermitian Yang-Mills equation.
The paper uses deep learning to detect asset price bubbles in tech stocks.
problem Detecting financial asset price bubbles using deep learning.
method Deep learning techniques applied to call option prices for financial asset bubbles detection.
result The proposed deep learning algorithm provides a theoretical foundation for positive and continuous stochastic asset price processes.
This paper offers a distribution-free method for post-detection changepoint localization.
problem Locating the exact time of a change in distribution after a sequential detection procedure.
method A distribution-free framework using conformal test martingales for sequential change detection and post-detection inference.
result Valid post-detection coverage guarantees and non-asymptotic bounds on confidence set size.
Proposes DR-ME test for interpretable distributional treatment effects.
problem Detects invisible differences in treatment effects on distributional outcomes.
method Semiparametrically efficient finite-location test using kernel witnesses and orthogonal features.
result DR-ME reveals causal-discrepancy coordinates and has noncentral chi-square local power.
Formula proves invariant matches for smooth and orbifold test configurations.
problem Proving equivalence of Donaldson-Futaki invariant and Futaki invariant.
method Equivariant localization formula.
result Invariant matches for smooth and orbifold test configurations.
Current system thermal-hydraulic codes have limited credibility in simulating real plant conditions, especially when the geometry and boundary conditions are extrapolated beyond the range of test facilities. This paper proposes a data-driven approach, Feature Similarity Measurement FFSM), to establish a technical basis…
New test detects local changes in high-dimensional Gaussian graphical models online.
problem Detecting abrupt changes in high-dimensional Gaussian graphical models in real-time.
method Develops a novel test based on the ℓ∞ norm of the normalized covariance matrix.
result Demonstrates good performance in various experimental settings.
We analyze the dynamical properties of a tetrahedron transformation on the space of non-degenerate tetrahedra which can be identified with the non-compact globally symmetric 8-dimensional space $\mbox{Sl}(3,\mathbb{R}) / \mbox{So}(3,\mathbb{R})$. We establish the existence of a local attractor which coincides with th…
New test for point processes without strong model assumptions.
problem Testing local independence in point processes without strong model assumptions.
method Expansion similar to Volterra expansions to represent marginalized intensities.
result Approximation of true marginalized intensity arbitrarily well.
In this paper we discuss the basket options valuation for a jump-diffusion model. The underlying asset prices follow some correlated local volatility diffusion processes with systematic jumps. We derive a forward partial integral differential equation (PIDE) for general stochastic processes and use the asymptotic expan…
We consider the goodness-of-fit testing problem of distinguishing whether the data are drawn from a specified distribution, versus a composite alternative separated from the null in the total variation metric. In the discrete case, we consider goodness-of-fit testing when the null distribution has a possibly growing or…