arXiv research

Exclusive row biclustering for gene expression data.

TabPFN-3 scales tabular prediction models to large datasets and improves performance and speed.

A novel MCMC method clusters data faster and more accurately.

Asymmetry PRISM outperforms CPU and GPU solvers for institutional rebalancing.

Paper proposes an adaptive modeling approach for row-type dependent predictive analysis in banking.

Research quantifies financial exclusion risks in UK, focusing on cash infrastructure and socio-economic factors.

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…

Double autoencoder $Ae^2I$ improves missing value imputation in recommender systems.

The paper shows how particle movement on a manifold's grid approximates Brownian motion and heat diffusion.

We construct an explicit topological model (similar to the topological Springer fibers appearing in work of Khovanov and Russell) for every two-row Springer fiber associated with the even orthogonal group and prove that the respective topological model is homeomorphic to its corresponding Springer fiber. This confirms …

A new method for learning row and column structures with missing data.

Introduces joint exclusivity (JE), a new form of negative dependence.

Random sampling has become a critical tool in solving massive matrix problems. For linear regression, a small, manageable set of data rows can be randomly selected to approximate a tall, skinny data matrix, improving processing time significantly. For theoretical performance guarantees, each row must be sampled with pr…

Extends conformal prediction to contrastive learning for better coverage of positive samples.

Proposes SROF for row-wise fusion in federated learning for multivariate responses.

We consider analysis of relational data (a matrix), in which the rows correspond to subjects (e.g., people) and the columns correspond to attributes. The elements of the matrix may be a mix of real and categorical. Each subject and attribute is characterized by a latent binary feature vector, and an inferred matrix map…

Our work is focused on the joint sparsity recovery problem where the common sparsity pattern is corrupted by Poisson noise. We formulate the confidence-constrained optimization problem in both least squares (LS) and maximum likelihood (ML) frameworks and study the conditions for perfect reconstruction of the original r…

Improves unsupervised feature learning with an exclusivity concept.

We consider the problem of performing matrix completion with side information on row-by-row and column-by-column similarities. We build upon recent proposals for matrix estimation with smoothness constraints with respect to row and column graphs. We present a novel iterative procedure for directly minimizing an informa…

Model predicts firms likely to be added to investment exclusion lists.

Exclusive Group Lasso improves feature selection in correlated biological data.

In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize bo…

The paper calculates colored Jones polynomials for specific link configurations.

Exclusive Lasso improves survival prediction in cancer datasets.

In standard clustering problems, data points are represented by vectors, and by stacking them together, one forms a data matrix with row or column cluster structure. In this paper, we consider a class of binary matrices, arising in many applications, which exhibit both row and column cluster structure, and our goal is …

The paper develops inference methods for high-dimensional multi-task regression with row-sparse coefficients.

We propose and study a row-and-column affine measurement scheme for low-rank matrix recovery. Each measurement is a linear combination of elements in one row or one column of a matrix $X$. This setting arises naturally in applications from different domains. However, current algorithms developed for standard matrix rec…

The problem of biclustering consists of the simultaneous clustering of rows and columns of a matrix such that each of the submatrices induced by a pair of row and column clusters is as uniform as possible. In this paper we approximate the optimal biclustering by applying one-way clustering algorithms independently on t…

New algorithm improves matrix estimation with one-sided covariates.

We propose stochastic rank-$1$ bandits, a class of online learning problems where at each step a learning agent chooses a pair of row and column arms, and receives the product of their values as a reward. The main challenge of the problem is that the individual values of the row and column are unobserved. We assume tha…

We consider the problem of large-scale inference on the row or column variables of data in the form of a matrix. Often this data is transposable, meaning that both the row variables and column variables are of potential interest. An example of this scenario is detecting significant genes in microarrays when the samples…

New method selects variables in groups with few nonzeros, improving support recovery.

The paper proposes a model to learn disentangled representations using mutual information.

LCRSR recovers latent row space for multi-view clustering.

The paper proposes a new model to analyze directed networks and accurately estimate community memberships.

Linear regression models depend directly on the design matrix and its properties. Techniques that efficiently estimate model coefficients by partitioning rows of the design matrix are increasingly popular for large-scale problems because they fit well with modern parallel computing architectures. We propose a simple me…

Study one-sided matrix completion with two observations per row.

Develops a non-parametric Dirichlet process method for probabilistic biclustering.

Optimized sampling scheme for compressed sensing combining randomness and determinism.

ETM identifies field-specific keywords in text classification.

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…

AVAE improves semi-supervised learning by generating exclusive latent codes.

Spaces of polynomials are shown to be Euclidean balls.

New method estimates heterogeneous treatment effects with improved guarantees.

We propose a general framework for reduced-rank modeling of matrix-valued data. By applying a generalized nuclear norm penalty we can directly model low-dimensional latent variables associated with rows and columns. Our framework flexibly incorporates row and column features, smoothing kernels, and other sources of sid…

A new method preserves useful information in data rows with outlying cells.

New algorithm samples matrix rows proportional to their ℓ_p norm in a turnstile data stream.

New study analyzes security of neural network data reconstruction attacks.