New method corrects biased comparisons in two-group data.
problem Unreliable inferences from biased sampling.
method Developed an inference method resilient to sampling biases.
result Controls false positives under moderate bias levels.
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48 results for “two-group data”
Solves a long-standing problem on step-two groups with exact formulas.
problem Long-standing Gaveau--Brockett open problem on step-two groups.
method Combining Varadhan's formulas, heat kernel, and operator convexity.
result Exact formula for Carnot--Carathéodory distance on step-two groups.
A new test assesses text similarity between two groups of documents.
problem Comparing similarity between two groups of documents.
method Neural network-based language models estimate entropy, and a test statistic derived from an estimation-and-inference framework is used.
result The proposed test maintains the nominal Type one error rate while offering greater power compared to existing methods.
We learn the structure of a Markov Network between two groups of random variables from joint observations. Since modelling and learning the full MN structure may be hard, learning the links between two groups directly may be a preferable option. We introduce a novel concept called the \emph{partitioned ratio} whose fac…
Bayesian method models binary response and covariates for two groups, estimating causal relationships.
problem Estimating causal relationships between binary response and covariates in observational data.
method Gaussian DAG-probit model with MCMC sampling for posterior distribution estimation.
result Validated method on simulated and real datasets, showing value of grouping variable in causality.
The paper develops adaptive confidence intervals for Efron's Gaussian two-groups model with unknown contamination.
problem Developing robust uncertainty quantification for Efron's Gaussian two-groups model with unknown contamination fraction.
method The approach involves Fourier-based certification procedures to find minimax-optimal adaptive confidence intervals.
result The minimax-optimal length of adaptive confidence intervals is polynomially worse than when contamination fraction is known.
Characterizes GM-groups via sub-Riemannian geometry properties.
problem Characterizing step-two Carnot groups via sub-Riemannian geometry.
method Sub-Riemannian geometric properties, including squared distance, cut locus, optimal synthesis.
result Characterization of GM-groups and exact expression of for classical cut locus.
Scientists develop a model to identify treatment responders from non-responders.
problem Analyzing samples that respond to treatment in studies.
method Causal two-groups (C2G) model, empirical Bayes procedures.
result The C2G model controls false discovery rate and has near-optimal power.
Develops a new test for comparing two groups' densities, showing minimax optimality.
problem Comparing probability densities between two groups.
method Probabilistic tensor product smoothing spline framework for joint density modeling; penalized likelihood ratio test for interaction testing.
result Proposed test is minimax optimal and outperforms conventional approaches.
The paper confirms two groups of gamma-ray bursts using a new nonparametric metric.
problem Determining the number of inherent groups in gamma-ray bursts.
method A new nonparametric interpoint distance-based measure, combined with clustering methods.
result Confirms two groups of short and long gamma-ray bursts.
This article considers the problem of multi-group classification in the setting where the number of variables is larger than the number of observations . Several methods have been proposed in the literature that address this problem, however their variable selection performance is either unknown or suboptimal to…
Generalizes symmetries of curved manifolds.
problem Maximizing symmetries in curved manifolds.
method Replaces torus with abelian group, generalizes results.
result Generalizes symmetry results for positively curved manifolds.
Geodesics and curvature of semidirect product groups with right invariant metrics are determined. In the special case of an isometric semidirect product, the curvature is shown to be the sum of the curvature of the two groups. A series of examples, like the magnetic extension of a group, are then considered.
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimationcs.LG
Reduces selection bias in estimating individual treatment effects.
problem Selection bias in counterfactual reasoning.
method Auto-encoder with regularized loss based on Pearson Correlation Coefficient.
result Improves performance in estimating individual treatment effects.
We construct unitary modular categories for a general class of coset conformal field theories based on our previous study of these theories in the algebraic quantum field theory framework using subfactor theory. We also consider the calculations of the corresponding 3-manifold invariants. It is shown that under certain…
We prove that the palindromic width of HNN extension of a group by proper associated subgroups is infinite. We also prove that the palindromic width of the amalgamated free product of two groups via a proper subgroup is infinite (except when the amalgamated subgroup has index two in each of the factors). Combining thes…
We consider the problem of high-dimensional classification between the two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, we propose to perform simultaneous variable selection and linear dimension reduction on original data, with the subsequent application of quadr…
Study compares ML and DL methods for autism classification.
problem Classifying autism from healthy individuals using rs-fMRI data.
method Developed classification frameworks for rs-fMRI connectivity patterns.
result Best model achieved 71% accuracy on multisite ABIDE I data.
PROBE algorithm efficiently solves sparse high-dimensional linear regression.
problem Sparse high-dimensional linear regression models with complex parameter spaces.
method Partitioned empirical Bayes ECM algorithm for computationally efficient MAP estimation.
result PROBE algorithm provides robust and efficient coordinate-wise optimization.
Machine learning predicts Sunn Pest migration and nymphal stages for better pesticide application timing.
problem Improving pest control timing through accurate forecasting of Sunn Pest life-cycle phases.
method Correlated climate data with Sunn Pest life-cycle phases using decision trees and random forest models.
result Highly accurate predictions of migration and nymphal stages for better pesticide application timing.
Classification problem in liability insurance using machine learning models: a comparative studystat.AP
Study classifies liability insurance policies using machine learning.
problem Classifying liability insurance policies with or without claims.
method Used machine learning models like nearest neighbour and logistic regression on Actuarial Challenge dataset.
result Models accurately classified policies into claims and non-claims groups.
Two groups with specific limit sets in hyperbolic spaces are identified.
problem Identifying convex cocompact subgroups with specific limit sets in real hyperbolic spaces.
method Examples of subgroups generated by reflections and rotations with limit sets as Pontryagin spheres and Menger curves.
result Examples of convex cocompact subgroups with limit sets as Pontryagin spheres and Menger curves are found.
We introduce the palindromic automorphism group and the palindromic Torelli group of a right-angled Artin group A_G. The palindromic automorphism group Pi A_G is related to the principal congruence subgroups of GL(n,Z) and to the hyperelliptic mapping class group of an oriented surface, and sits inside the centraliser …
The understanding of complex social or economic systems is an important scientific challenge. Here we present a comprehensive study of the Spanish Stock Exchange showing that most financial firms trading in that market are characterized by a resulting strategy and can be classified in groups of firms with different spe…
The group of volume preserving diffeomorphisms, the group of symplectomorphisms and the group of contactomorphisms constitute the classical groups of diffeomorphisms. The first homology groups of the compactly supported identity components of the first two groups have been computed by Thurston and Banyaga, respectively…
A Z-structure on a group G, defined by M. Bestvina, is a pair (\hat{X}, Z) of spaces such that \hat{X} is a compact ER, Z is a Z-set in \hat{X}, G acts properly and cocompactly on X=\hat{X}\Z, and the collection of translates of any compact set in X forms a null sequence in \hat{X}. It is natural to ask whether a given…
This paper analyzes correlations in patterns of trading of different members of the London Stock Exchange. The collection of strategies associated with a member institution is defined by the sequence of signs of net volume traded by that institution in hour intervals. Using several methods we show that there are signif…
The study examines how bias affects hypothesis formation in neural networks.
problem Characterizing the impact of bias on hypothesis formation in neural networks.
method An automated data-driven projection pursuit neural network to extract and select features for binary classification.
result The refinement of a working hypothesis converges to a robust multivariate perception of data.
On Simple Mean-Field Stochastic Model of Market Dynamicscond-mat.stat-mech
We propose a simple stochastic model of market behavior. Dividing market participants into two groups: trend-followers and fundamentalists, we derive the general form of a stochastic equation of market dynamics. The model has two characteristic time scales: the time of changes of market environment and the characterist…
Optimal scoring framework for kernel classification with feature selection.
problem Two-group classification problem.
method Optimal scoring framework, structured sparsity using weighted kernels, automated parameter selection.
result Superior classification performance compared to existing nonparametric classifiers.
Develops a fair post-processing method for student success predictions.
problem Ensuring fairness in predictive student models for educational applications.
method Uses the MADD metric to improve model fairness while maintaining accuracy.
result Successfully improved fairness of predictive models for student success.
Paper discusses sliced generative models for improved sample discrimination.
problem Improving sample discrimination in generative models.
method One-dimensional slicing of AutoEncoder-based generative models, focusing on normality tests and classical distances.
result The second group of methods based on classical distances gives a faster decrease rate of Fréchet Inception Distance (FID).
The paper studies properties of group relations induced by compatible coarse structures.
problem Properties of asymptotic resemblance relations on groups.
method Generalization of asymptotic dimension and introduction of set theoretic coupling.
result Groups with compatible coarse structures that admit a set theoretic coupling are asymptotic equivalent.
Agents Play Mix-gamephysics.soc-ph
In mix-game which is an extension of minority game, there are two groups of agents; group1 plays the majority game, but the group2 plays the minority game. This paper studies the change of the average winnings of agents and volatilities vs. the change of mixture of agents in mix-game model. It finds that the correlatio…
The paper examines A/B tests in recommendation systems to detect biased algorithm comparisons due to shared data.
problem Bias in comparing recommendation algorithms due to shared data.
method Formalized as a multi-armed bandit problem, analyzed the sign of difference-in-means estimator vs true GTE.
result Data sharing can lead to biased comparisons of recommendation algorithms, and a detection procedure is proposed.
Feature noise causes loss discrepancies across groups even with equal data.
problem Loss discrepancies observed in learning procedures across different groups.
method Characterized the effect of feature noise on loss discrepancy in linear regression.
result Feature noise leads to loss discrepancy even when groups have equal data.
Discovering and clustering subspaces in high-dimensional data is a fundamental problem of machine learning with a wide range of applications in data mining, computer vision, and pattern recognition. Earlier methods divided the problem into two separate stages of finding the similarity matrix and finding clusters. Simil…
Tucker decomposition is the cornerstone of modern machine learning on tensorial data analysis, which have attracted considerable attention for multiway feature extraction, compressive sensing, and tensor completion. The most challenging problem is related to determination of model complexity (i.e., multilinear rank), e…
In this paper we propose a simple and efficient method to compute the ordered default time distributions in both the homogeneous case and the two-group heterogeneous case under the interacting intensity default contagion model. We give the analytical expressions for the ordered default time distributions with recursive…
Sparse Singular Value Decomposition (SVD) models have been proposed for biclustering high dimensional gene expression data to identify block patterns with similar expressions. However, these models do not take into account prior group effects upon variable selection. To this end, we first propose group-sparse SVD model…
Study on 4-manifolds for special Kähler metrics with constant Ricci determinant.
problem Existence of complete cohomogeneity one Kähler metrics with zero Ricci determinant.
method Analysis of specific Lie groups and solutions to associated ODE systems.
result Complete classification for and existence results for and .
Two groups with same profinite completion have different co-Hopfian properties.
problem Understanding co-Hopfian properties in residually finite groups.
method Using a specific construction involving a finitely presented acyclic group with trivial profinite completion.
result Found two groups with same profinite completion but different co-Hopfian properties.
Study shows price bubbles can exist even with heterogeneous beliefs.
problem Equilibrium price formation in markets with different belief groups.
method Analyzes continuous time asset trading with heterogeneous investors and mean reverting asset.
result Price bubbles may not form even with heterogeneous beliefs, contrary to initial expectations.
Paper tackles robust domain adaptation without target domain data.
problem Learning domain invariant representations without target domain data.
method Integrates deep autoencoder and causal structure learning into a unified model.
result CAE learns causal representations using only source domain data.
Framework quantifies semantic similarity between groups of embeddings.
problem Quantifying semantic similarity between groups of embeddings.
method Formulates model comparison task, contrasts generative models, uses information criteria.
result Achieves competitive results in Semantic Textual Similarity tasks.
A generalized Baumslag-Solitar (GBS) group is a finitely generated group acting on a tree with infinite cyclic edge and vertex stabilizers. We show how to determine effectively the rank (minimal cardinality of a generating set) of a GBS group; as a consequence, one can compute the rank of the mapping torus of a finite …
Estimating Differential Latent Variable Graphical Models with Applications to Brain Connectivitymath.ST
Estimates differences in brain connectivity graphs using latent variables.
problem Estimating differences in latent variable graphical models.
method Two-stage procedure: initialization and convergence stages using projected alternating gradient descent.
result Nonconvex procedure outperforms existing methods on synthetic and real data.
Novel method for learning Gaussian graphical models from paired data.
problem Learning Gaussian graphical models for dependent groups.
method Introducing twin order to explore the search space more efficiently.
result The twin order makes the model space a distributive lattice, leading to more efficient model exploration.