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arXiv research

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.

168,742 papers · 148 categories

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48 results for multiple subgroups

Proposes a method to learn fair predictors for multiple subgroups with limited data.

problem Fairness and accuracy issues in learning from multiple subgroups with limited data.
method Formulates a bilevel objective to learn subgroup-specific predictors and a fair predictor that is close to all of them.
result The method effectively controls group sufficiency and generalization error, improving fairness and accuracy.

New algorithm tackles subgroup fairness in AI with multiple sensitive attributes.

problem Heavy computational burdens and data sparsity in subgroup fairness for multiple sensitive attributes.
method Doubly Regressing Adversarial learning (DRAF) for subgroup fairness, focusing on subgroups with sufficient sample sizes and marginal fairness.
result DRAF algorithm reduces a surrogate fairness gap for supIPM with less computation than directly reducing supIPM.

Study shows Steinberg representation's multiplicity in cohomology of congruence subgroups.

problem Analyzing multiplicity of Steinberg representation in cohomology of congruence subgroups.
method Computation of cohomology of SS-arithmetic groups outside a linear range of degrees.
result Multiplicity of Steinberg representation is 1 in top-degree cohomology.

Sparse GFA identifies disease factors in FTD subgroups.

problem Heterogeneity in neurological disorders hinders understanding and treatment.
method Sparse Group Factor Analysis (GFA) with regularised horseshoe priors.
result Identified latent disease factors differentially expressed in FTD subgroups.

cMCA uses contrastive learning to identify latent subgroups in political party data.

problem Identifying latent subgroups within political party data.
method Contrastive learning applied to multiple correspondence analysis (MCA).
result cMCA identifies latent subgroups not seen by traditional methods.

We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled representation learning literature, we propose an algorithm for learning compact representations of datasets that are useful for reconstruc…

2019-06-06abs ↗pdf ↗

The paper tackles fairness in forecasting and learning linear dynamical systems.

problem Under-representation bias in training data for multiple subgroups.
method Introducing subgroup-fair and instant-fair learning of LDS from multiple trajectories of varying lengths, using hierarchies of convexifications of non-commutative polynomial optimisation problems.
result Empirical results show both the beneficial impact of fairness considerations on statistical performance and encouraging effects of exploiting sparsity on run time.

A new algorithm COVA-FC improves subgroup-fair clustering efficiency.

problem Challenges in making cluster assignments independent of sensitive attributes in subgroups.
method Defining a subgroup-fairness gap, deriving a covariance-based surrogate, and introducing a continuous relaxation for efficient optimization.
result COVA-FC achieves competitive cost-fairness trade-offs and improves computational efficiency.

Proposes a method to detect anomalies in multi-subgroup normal data.

problem Anomaly detection with limited labeled anomalies and multi-subgroup normal data.
method Learn multi-normal prototypes with deep embedding clustering and contrastive learning. Estimate the likelihood of unlabeled samples being normal during training.
result Superior performance compared to state-of-the-art methods on various datasets.

In the Pioneer 100 (P100) Wellness Project (Price and others, 2017), multiple types of data are collected on a single set of healthy participants at multiple timepoints in order to characterize and optimize wellness. One way to do this is to identify clusters, or subgroups, among the participants, and then to tailor pe…

2019-01-12abs ↗pdf ↗

Study of modular representations in homology of congruence subgroups.

problem Understanding modular representations in homology of congruence subgroups.
method Analysis of sequences of modular representations of symplectic and special linear groups over finite fields.
result Established periodic representation stability in the sense of Church--Farb.

Method provides statistical guarantees for identifying subgroups in ML studies.

problem Bias and noise in estimating conditional average treatment effects (CATE).
method Develops uniform confidence bands (GATES) for estimating group average treatment effects (GATEs).
result Identifies subgroups with statistical guarantees, regardless of effect size.

The study finds the minimum number of critical points for functionals on Frechet spaces and Finsler manifolds.

problem Finding the minimum number of critical points for functionals on Frechet spaces and Finsler manifolds.
method Applying the Lusternik-Schnirelmann category to evaluate the minimal number of critical points for Keller Cc1 C_c^1 -functionals on Frechet spaces and Finsler manifolds.
result The minimal number of critical points is determined by the Lusternik-Schnirelmann category.

Let GG be a compact connected semisimple Lie group, let KK be a closed subgroup of GG, let ΓΓ be a finite subgroup of GG, and let ττ be a finite-dimensional representation of KK. For ππ in the unitary dual G^\widehat G of GG, denote by nΓ(π)n_Γ(π) its multiplicity in L2(Γ\G)L^2(Γ\backslash G). We prove a strong multip…

2018-04-23abs ↗pdf ↗

CAPITAL algorithm identifies optimal patient subgroups for better treatment.

problem Identify maximum number of patients benefiting from better treatment.
method Constrained Policy Tree Search (CAPITAL) algorithm to find optimal subgroup selection rule (SSR).
result Maximizes the number of patients with enhanced treatment effects.

Let K be a compact semi-simple Lie group. We classify K-invariant Kaehler structures on the space Kc/(P,P), where Kc is the complexification of K, P is a parabolic subgroup of Kc, and (P,P) the commutator subgroup. For each Kaehler structure, we study its moment map and associated pre-quantum line bundle for geometric …

1996-08-28abs ↗pdf ↗

We study the conditions for a nilpotent Lie group to be foliated into subgroups that have square integrable (relative discrete series) unitary representations, that fit together to form a filtration by normal subgroups. Then we use that filtration to construct a class of "stepwise square integrable" representations on …

2012-12-09abs ↗pdf ↗

The paper describes decompositions of geometric measures on Anosov homogeneous spaces.

problem Decomposing geometric measures on Anosov homogeneous spaces.
method Ergodic decompositions of Burger-Roblin and Bowen-Margulis-Sullivan measures.
result The space of non-trivial invariant ergodic measures is homeomorphic to a product space.

Let SS be a complex reductive group acting holomorphically on a complex Lie group NN via holomorphic automorphisms. Let K(S)SK(S)\subset S be a maximal compact subgroup. The semidirect product G:=NK(S)G := N\rtimes K(S) acts on NN via biholomorphisms. We give an explicit description of the isomorphism classes of GG-equivari…

2015-02-18abs ↗pdf ↗

For simple Lie groups, the only homogeneous manifolds G/KG/K, where KK is maximal compact subgroup,for which the phase of the scalar product of two coherent state vectors is twice the symplectic area of a geodesic triangle are the hermitian symmetric spaces. An explicit calculation of the multiplicative factor on the c…

2004-08-18abs ↗pdf ↗

The paper introduces a method to control false splits in tree-based data aggregation.

problem Identifying the correct subgroups to treat as a single entity in tree-based data.
method Introduces the 'false split rate' and proposes a multiple hypothesis testing algorithm for tree-based aggregation.
result The proposed algorithm controls the false split rate, demonstrating its effectiveness on stock volatility and taxi fare data.

Let F\mathbb{F} be a field and let GF{0}G\subset \mathbb{F}\setminus \{0\} be a multiplicative subgroup. We consider the category CobG\mathcal{Cob}_G of 33-dimensional cobordisms equipped with a representation of their fundamental group in GG, and the category VectF,±G{Vect}_{\mathbb{F},\pm G} of F\mathbb{F}-linear maps defin…

2014-03-17abs ↗pdf ↗

HIP method extended to multi-class, Poisson, and Zero-Inflated Poisson outcomes with an R Shiny app.

problem Subgroup heterogeneity in complex diseases like COPD.
method Integrating multiple data views while accounting for subgroup heterogeneity.
result Identified common and subgroup-specific markers of exacerbation frequency in males and females.

Develops a new criterion for subgroup fairness in algorithmic decision support.

problem Identifying fair recommendations in algorithms despite group-level differences.
method IJDI criterion and IJDI-Scan approach to detect and mitigate disparities.
result Identifies significant disparities in recommendations across subpopulations.

The paper explores intersectional fairness in machine learning, proving bounds on it.

problem Intersectional fairness in machine learning, especially when multiple protected attributes are involved.
method Statistical analysis and bounds on intersectional fairness, leveraging marginal fairness.
result Theoretical bounds on intersectional fairness can be computed from marginal fairness and other statistical quantities.

Let GG be a finite group with symmetric generating set SS, and let c=maxR>0B(2R)/B(R)c = \max_{R > 0} |B(2R)|/|B(R)| be the doubling constant of the corresponding Cayley graph, where B(R)B(R) denotes an RR-ball in the word-metric with respect to SS. We show that the multiplicity of the kkth eigenvalue of the Laplacian on the Cayley…

2008-06-10abs ↗pdf ↗

CRL approach improves understanding of heterogeneous treatment effects in complex diseases.

problem Estimating heterogeneous treatment effects in complex diseases.
method Causal rule learning (CRL) workflow consisting of rule discovery, selection, and analysis.
result CRL outperforms other methods in providing interpretable estimates of HTE.

Study of a torsion class in mapping class group's cohomology.

problem Understanding torsion in mapping class groups' cohomology.
method Analyzing the inclusion of mapping class groups into circle homeomorphisms and studying the pullback of Euler classes.
result Proves the existence of a torsion class in cohomology, generating a cyclic subgroup of order multiple of 4g(2g+1)(2g1)4g(2g+1)(2g-1).

Stable subgroups and the Morse boundary are two systematic approaches to collect and study the hyperbolic aspects of finitely generated groups. In this paper we unify and generalize these strategies by viewing any geodesic metric space as a countable union of stable subspaces: we show that every stable subgroup is a qu…

2016-06-01abs ↗pdf ↗

Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. Uplift modeling can be used to estimate which users are likely to benefit from …

2019-08-14abs ↗pdf ↗