Many practical applications such as gene expression analysis, multi-task learning, image recognition, signal processing, and medical data analysis pursue a sparse solution for the feature selection purpose and particularly favor the nonzeros \emph{evenly} distributed in different groups. The exclusive sparsity norm has…
On-device research index
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
Trend · papers per month
48 results for “exclusive groups”
Exclusive Group Lasso improves feature selection in correlated biological data.
problem Correlated features hinder Lasso performance in biological classification problems.
method Proposes and solves the exclusive group Lasso, combining stability selection and random group allocation.
result Exclusive Group Lasso outperforms Lasso in comprehensive selection of informative features.
New method selects variables in groups with few nonzeros, improving support recovery.
problem Structured variable selection with sparse patterns across groups.
method Composite norm and proximal algorithm for exclusive group sparsity.
result Asymptotic consistency in signed support recovery under conventional assumptions.
Exclusive Lasso improves survival prediction in cancer datasets.
problem Enhanced survival prediction in cancer datasets with high-dimensional genomic and clinical data.
method Proposes Exclusive Lasso regularization for feature selection in Cox regression models for grouped variables.
result Demonstrates improved survival prediction performance using Exclusive Lasso compared to standard Cox regression.
The girth of a finitely generated group G is the supremum of the girth of Cayley graphs for G over all finite generating sets. Let G be a finitely generated subgroup of the mapping class group Mod(S), where S is a compact orientable surface. Then, either G is virtually abelian or it has infinite girth; moreover, if we …
Various measures can be used to estimate bias or unfairness in a predictor. Previous work has already established that some of these measures are incompatible with each other. Here we show that, when groups differ in prevalence of the predicted event, several intuitive, reasonable measures of fairness (probability of p…
Research quantifies financial exclusion risks in UK, focusing on cash infrastructure and socio-economic factors.
problem Localised financial exclusion in the UK as cash infrastructure declines.
method Developed a composite indicator using various input variables.
result Financial exclusion is more prevalent in deprived communities and affluent areas.
Joint Exclusivitymath.ST
Introduces joint exclusivity (JE), a new form of negative dependence.
problem Negative dependence structures in probability distributions.
method Defines JE by exclusion of the interior of the non-negative orthant, establishes necessary and sufficient conditions for existence, proposes a canonical construction.
result Sharp necessary and sufficient condition for existence of JE random vectors with prescribed marginals.
We consider the symmetric exclusion process on suitable random grids that approximate a compact Riemannian manifold. We prove that a class of random walks on these random grids converge to Brownian motion on the manifold. We then consider the empirical density field of the symmetric exclusion process and prove that it …
Random groups prove length constraints on product of conjugates.
problem Quantify products of conjugates in random groups.
method Sharp van Kampen diagram argument and boundary block-counting.
result Prove a sharp inequality for products of conjugates in random groups.
En nous basant sur les résultats d'Arthur annoncés dans \cite[§30]{Arthur} nous démontrons les conjectures énoncées dans \cite{IMRN,BC,SMF} dans le cas des groupes orthogonaux à l'exclusion des groupes de type . En ce qui concerne ces derniers, nous annonçons la démonstration -- encore en préparation - que …
Extends conformal prediction to contrastive learning for better coverage of positive samples.
problem Lack of principled guarantees on coverage in contrastive learning.
method Introduces minimum-volume covering sets with learnable constraints.
result Improves inclusion-exclusion trade-offs in positive and negative samples.
We derive the canonical forms of super Riemannian metrics and the local isometry groups of such metrics. For certain super metrics we also compute the simply connected covering groups of the local isometry groups and interpret these as local spin groups of the super metric. Using a generalization of a Theorem of Rogers…
Study of SO(3)-irreducible geometry in complex 5D and ternary Pauli exclusion principle.
problem Exploring SO(3)-irreducible geometry in complex 5D.
method Defined a ternary skew-symmetric tensor, split the 10D space into irreducible SO(3) subspaces, found invariants and defined geometric structures.
result Defined a SO(3)-irreducible geometric structure on a 5D complex Hermitian manifold.
The availability of large microarray data has led to a growing interest in biclustering methods in the past decade. Several algorithms have been proposed to identify subsets of genes and conditions according to different similarity measures and under varying constraints. In this paper we focus on the exclusive row bicl…
Gromov proposed to extract the (differential) geometric content of a sub-riemannian space exclusively from its Carnot-Carathéodory distance. One of the most striking features of a regular sub-riemannian space is that it has at any point a metric tangent space with the algebraic structure of a Carnot group, hence a homo…
The paper proposes a model to learn disentangled representations using mutual information.
problem Learning disentangled representations from shared and exclusive attributes.
method Mutual information maximization for shared attributes and minimization for disentanglement.
result The proposed model outperforms state-of-the-art models in representation disentanglement.
Exclusive Topic Modelingstat.ML
ETM identifies field-specific keywords in text classification.
problem Unsupervised text classification with field-specific keywords.
method Weighted Lasso penalty and pairwise Kullback-Leibler divergence penalty for topic separation.
result ETM improves topic coherence by 22% and 10% compared to LDA.
An exclusion particle model is considered as a highly simplified model of a limit order market. Its price behavior reproduces the well known crossover from over-diffusion (Hurst exponent H>1/2) to diffusion (H=1/2) when the time horizon is increased, provided that orders are allowed to be canceled. For early times a ma…
Negative screening is one method to avoid interactions with inappropriate entities. For example, financial institutions keep investment exclusion lists of inappropriate firms that have environmental, social, and government (ESG) problems. They create their investment exclusion lists by gathering information from variou…
This paper investigates the use of distributed processing on the problem of emotion recognition from physiological sensors using a popular machine learning library on distributed mode. Specifically, we run a random forests classifier on the biosignal-data, which have been pre-processed to form exclusive groups in an un…
Spaces of polynomials are shown to be Euclidean balls.
problem Understanding the geometry of Lorentzian and real stable polynomials.
method Refined connection between symmetric exclusion process and polynomial geometry.
result Spaces of Lorentzian and real stable polynomials are homeomorphic to closed Euclidean balls.
New study analyzes security of neural network data reconstruction attacks.
problem Data reconstruction attacks pose a threat to private training data.
method Analyzes security boundary of data reconstruction attacks via neuron exclusivity state.
result Characterizes insecure/secure boundary of data reconstruction attacks.
Unified treatment of two extension problems using heat equation in Heisenberg group.
problem Two extension problems for pseudo-differential operators in Heisenberg group.
method Heat equation, fractional powers, semigroup methods.
result Explicit computation of fundamental solutions for pseudo-differential operators.
PAM models generate dependent random distributions across groups with overlapping clusters.
problem Generating dependent random distributions across multiple groups.
method Atom skipping in an infinite mixture model.
result Interpretable posterior inference of cluster exclusivity and sharing.
A Poisson convolution model for characterizing topical content with word frequency and exclusivitycs.LG
An ongoing challenge in the analysis of document collections is how to summarize content in terms of a set of inferred themes that can be interpreted substantively in terms of topics. The current practice of parametrizing the themes in terms of most frequent words limits interpretability by ignoring the differential us…
Unsupervised learning is becoming more and more important recently. As one of its key components, the autoencoder (AE) aims to learn a latent feature representation of data which is more robust and discriminative. However, most AE based methods only focus on the reconstruction within the encoder-decoder phase, which ig…
In the first part of this paper, we work out a perturbative Lagrangian formulation of semistrict higher gauge theory, that avoids the subtleties of the relationship between Lie 2-groups and algebras by relying exclusively on the structure semistrict Lie 2-algebra v and its automorphism 2-group Aut(v). Gauge transformat…
Paper tackles unfair advantages in DARTS, presenting Fair DARTS to improve neural architecture search.
problem Performance collapse in DARTS due to unfair advantages in skip connections.
method Relax exclusive competition to collaborative, let architectural weights be independent, and use zero-one loss for discretization.
result New state-of-the-art results on CIFAR-10 and ImageNet, demonstrating the effectiveness of Fair DARTS.
Discrimination via algorithmic decision making has received considerable attention. Prior work largely focuses on defining conditions for fairness, but does not define satisfactory measures of algorithmic unfairness. In this paper, we focus on the following question: Given two unfair algorithms, how should we determine…
Analyzes how inclusion/exclusion from STOXX Europe 600 Index affects company prices.
problem Understanding price dynamics of companies in STOXX Europe 600 Index.
method Used logit models and neural networks to analyze data.
result Identified independent variables affecting price changes.
Study risk-constrained Kelly optimization for mutually exclusive outcomes, proving support invariance and developing a structured algorithm.
problem Risk-constrained Kelly optimization for mutually exclusive outcomes with explicit state prices.
method Analyzes the finite mutually exclusive outcome version of risk-constrained Kelly optimization with explicit state prices, proving support invariance and developing a structured algorithm.
result Support is invariant across CRRA parameter and drawdown-surrogate parameter in the overround regime.
In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term…
Meta-learning improved by using information theory to prioritize data-driven adaptation.
problem Challenges in meta-learning due to the need for mutually-exclusive tasks.
method Designing a meta-regularization objective using information theory.
result Successfully uses data from non-mutually-exclusive tasks to efficiently adapt to novel tasks.
Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a structure learning method for learning sparse, parameter-efficient NLP models. Our method applies group lasso to rational RNNs (Peng et al.,…
Hexagonal norm double bubble problem solved with minimal configurations.
problem Finding the optimal shapes for minimizing perimeter in hexagonal geometry.
method Elementary proof and geometric exclusions to simplify minimizer search.
result Existence of minimizing sets for volume ratio parameter α in (0,1].
Method selects valid IVs from a large set using clustering and test of overidentifying restrictions.
problem Selecting valid instrumental variables from a large set of candidates.
method Agglomerative hierarchical clustering combined with a test of overidentifying restrictions.
result Achieves oracle properties when the largest group of IVs is valid.
The exclusive or (xor) function is one of the simplest examples that illustrate why nonlinear feedforward networks are superior to linear regression for machine learning applications. We review the xor representation and approximation problems and discuss their solutions in terms of probabilistic logic and associative …
Valid causal inference with invalid instruments using majority or modal valid relationships.
problem Estimating causal effects in the presence of unobserved confounding and invalid instruments.
method Ensemble of instrumental variable estimators to estimate the modal prediction, achieving accurate estimates of conditional average treatment effects.
result Valid causal inference can be achieved with a majority or modal valid instrument-response relationship.
DMTG groups tasks for multi-task learning in one shot.
problem Efficiently grouping and training multiple tasks in machine learning.
method Formulates Multi-Task Grouping as a differentiable pruning problem, training all groups simultaneously.
result Significantly improves training efficiency and mitigates objective bias.
Continuing the quest for exclusive Racah matrices, which are needed for evaluation of colored arborescent-knot polynomials in Chern-Simons theory, we suggest to extract them from a new kind of a double-evolution -- that of the antiparallel double-braids, which is a simple two-parametric family of two-bridge knots, gene…
Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial Networks (GANs) have recently shown promising performance in semi-supervised classificatio…
The paper proves limitations on actions of a specific group on spheres.
problem Prohibiting effective actions of a specific group on spheres with odd fixed points.
method Analyzing the group structure and applying representation theory.
result Proves that cannot act effectively with odd number of fixed points on low-dimensional spheres.
It is well known that a random vector with given marginal distributions is comonotonic if and only if it has the largest sum with respect to the convex order [ Kaas, Dhaene, Vyncke, Goovaerts, Denuit (2002), A simple geometric proof that comonotonic risks have the convex-largest sum, ASTIN Bulletin 32, 71-80. Cheung (2…
Mapping spaces of supermanifolds are usually thought as exclusively in functorial terms (i.e. trough the Grothendieck functor of points). In this work we provide a geometric description of such mapping spaces in terms of infinite-dimensional super-vector bundles.
PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communitiescs.LG
PieClam autoencodes graphs into communities, improving graph anomaly detection.
problem Graph anomaly detection and universal graph autoencoding.
method Probabilistic graph model with overlapping inclusive and exclusive communities.
result PieClam is a universal autoencoder that uniformly approximates any graph.
Study cooperative bandit learning with imperfect communication, achieving near-optimal performance.
problem Real-world distributed decision-making with imperfect communication.
method Proposed decentralized algorithms for three communication scenarios: stochastic networks, random delays, and adversarially corrupted rewards.
result Achieved competitive performance and near-optimal guarantees on group regret.
We describe the inclusive Racah matrices for the first non-(anti)symmetric rectangular representation R=[2,2] for quantum groups U_q(sl_N). Most of them have sizes 2, 3, and 4 and are fully described by the eigenvalue hypothesis. Of two 6x6 matrices, one is also described in this way, but the other one corresponds to t…