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.
A new overlapping space solves the configuration search problem for graph embeddings.
Overlapping clustering problem is an important learning issue in which clusters are not mutually exclusive and each object may belongs simultaneously to several clusters. This paper presents a kernel based method that produces overlapping clusters on a high feature space using mercer kernel techniques to improve separa…
Sliced inverse regression (SIR) is a pioneer tool for supervised dimension reduction. It identifies the effective dimension reduction space, the subspace of significant factors with intrinsic lower dimensionality. In this paper, we propose to refine the SIR algorithm through an overlapping slicing scheme. The new algor…
We study one extremal problem on the product of power of generalized inner radii of non-overlapping domains in .
In this paper we describe the homology and cohomology of some natural bimodules over the little discs operad, whose components are configurations of non--overlapping discs. At the end we briefly explain how this algebraic structure intervenes in the study of spaces of non--equal immersions.
We find flat band Hamiltonians and Ginsparg-Wilson relations for symmetry classes.
Learning with feature evolution studies the scenario where the features of the data streams can evolve, i.e., old features vanish and new features emerge. Its goal is to keep the model always performing well even when the features happen to evolve. To tackle this problem, canonical methods assume that the old features …
Improved off-policy evaluation for MDPs with weak distributional overlap.
Firms implementing digital advertising campaigns face a complex problem in determining the right match between their advertising creatives and target audiences. Typical solutions to the problem have leveraged non-experimental methods, or used "split-testing" strategies that have not explicitly addressed the complexitie…
Deconfounding scores improve causal effect estimation with weak overlap.
A new method speeds up overlapping group lasso computations.
In medicine, visualizing chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosomes often overlap and it is necessary to identify and distinguish between the overlapping chromosomes. A segmentation solution that is fast and automated will enable scaling of co…
We examine overlapping clustering schemes with functorial constraints, in the spirit of Carlsson--Memoli. This avoids issues arising from the chaining required by partition-based methods. Our principal result shows that any clustering functor is naturally constrained to refine single-linkage clusters and be refined by …
New method improves CATE estimation in low overlap regions.
Proposes a sensitivity framework to handle limited overlap in causal inference.
Producing overlapping schemes is a major issue in clustering. Recent proposed overlapping methods relies on the search of an optimal covering and are based on different metrics, such as Euclidean distance and I-Divergence, used to measure closeness between observations. In this paper, we propose the use of another meas…
The study simplifies assessing overlap in logistic regression models using empirical likelihood.
We present a new algorithm for community detection. The algorithm uses random walks to embed the graph in a space of measures, after which a modification of -means in that space is applied. The algorithm is therefore fast and easily parallelizable. We evaluate the algorithm on standard random graph benchmarks, inclu…
This work draws inspiration from three important sources of research on dissimilarity-based clustering and intertwines those three threads into a consistent principled functorial theory of clustering. Those three are the overlapping clustering of Jardine and Sibson, the functorial approach of Carlsson and Mémoli to par…
Study optimizes estimating linear functionals from observational data without strict overlap.
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because…
Community detection is a fundamental problem in network analysis which is made more challenging by overlaps between communities which often occur in practice. Here we propose a general, flexible, and interpretable generative model for overlapping communities, which can be thought of as a generalization of the degree-co…
RISA improves VFL by using imputed samples with low uncertainty.
As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been proposed in recent years that are capable of assigning each node to more than a single community. The performance of these algorithms tends to …
We present a principled approach for detecting overlapping temporal community structure in dynamic networks. Our method is based on the following framework: find the overlapping temporal community structure that maximizes a quality function associated with each snapshot of the network subject to a temporal smoothness c…
New mechanism detects overlap density for weak-to-strong generalization.
We calculate eigenvector overlaps between intersecting time periods of covariance matrices.
Community detection is a task of fundamental importance in social network analysis that can be used in a variety of knowledge-based domains. While there exist many works on community detection based on connectivity structures, they suffer from either considering the overlapping or non-overlapping communities. In this w…
Unified approach for fair classification with overlapping groups.
Overlap-Local-SGD improves distributed SGD by overlapping communication and computation.
New LT-O-learners improve HLTE estimation with low overlap.
ION-C solves overlapping network integration problems efficiently.
Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate the effect of intervention changes on that subgroup without further assumptions. When overlap does not hold globally, characterizing local reg…
Study on Langevin dynamics for recovering planted signals in spiked matrix models.
New method uses neural networks to improve free energy estimation.
Proposes a meta-algorithm for classification with overlapping classes in high-energy physics.
New method detects overlapping communities in weighted graphs without pure nodes assumption.
New algorithms achieve small prediction regret for learning from overlapping groups.
K-fold Cross Validation is commonly used to evaluate classifiers and tune their hyperparameters. However, it assumes that data points are Independent and Identically Distributed (i.i.d.) so that samples used in the training and test sets can be selected randomly and uniformly. In Human Activity Recognition datasets, we…
Study on overlaps of singular vectors in Gaussian matrix submatrices.
Deconfounding scores improve causal effect estimation with weak overlap.
Semidefinite programs have recently been developed for the problem of community detection, which may be viewed as a special case of the stochastic blockmodel. Here, we develop a semidefinite program that can be tailored to other instances of the blockmodel, such as non-assortative networks and overlapping communities. …
New GNN model detects overlapping communities better than existing methods.
The paper tackles the problem of identifying same-cluster elements in overlapping clusters with minimum queries.
This paper presents a novel spectral algorithm with additive clustering designed to identify overlapping communities in networks. The algorithm is based on geometric properties of the spectrum of the expected adjacency matrix in a random graph model that we call stochastic blockmodel with overlap (SBMO). An adaptive ve…
New metrics assess class overlap and imbalance in datasets.
Deep learning methods improve overlapping speaker separation across languages and noise.
CnGAN generates synthetic user preferences for non-overlapped users in cross-network recommender systems.