arXiv research

Study optimizes shared singular subspace estimation from noisy matrices.

Model improves covariance estimation from shared and distinct datasets.

It is often the case that, within an online recommender system, multiple users share a common account. Can such shared accounts be identified solely on the basis of the userprovided ratings? Once a shared account is identified, can the different users sharing it be identified as well? Whenever such user identification …

The paper proposes methods to find a shared active subspace for multivariate vector-valued functions.

This paper analyzes AJIVE for estimating shared subspace across multiple datasets, revealing its strengths and limitations.

This paper presents a novel approach to speaker subspace modelling based on Gaussian-Binary Restricted Boltzmann Machines (GRBM). The proposed model is based on the idea of shared factors as in the Probabilistic Linear Discriminant Analysis (PLDA). GRBM hidden layer is divided into speaker and channel factors, herein t…

Paper proposes CLAIR for efficient LLM fine-tuning across clients.

New method learns shared structures in non-linear tasks.

We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with sa…

The paper proposes a method to balance fairness and prediction accuracy by adjusting data representations.

Study on reliability of latent reuse in diffusion models under distribution shift.

Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data. This paper presents an approach to multi-view subspace clustering that learns a joint subspace…

A method for identifying joint and individual subspaces from multi-view data.

Estimates shared linear subspace from noisy data with multiple users.

Method captures shared information across many views robustly.

Dockless bike sharing systems need effective bike flow prediction models.

Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing parameters with other networks. In practice, however, MTL involves searching an enormous space of possible parameter sharing architectures to find (a) the layers or subspaces that benefit from sharing, (b) the appropriate amoun…

Multi-view subspace learning (MSL) aims to find a low-dimensional subspace of the data obtained from multiple views. Different from single view case, MSL should take both common and specific knowledge among different views into consideration. To enhance the robustness of model, the complexity, non-consistency and simil…

In this work we propose a method for reducing the dimensionality of tensor objects in a binary classification framework. The proposed Common Mode Patterns method takes into consideration the labels' information, and ensures that tensor objects that belong to different classes do not share common features after the redu…

In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of information available in multiple datasets. Multidatasets, on the other hand, yield…

Anchor PCA improves robustness in multi-domain PCA.

PAS method improves UDA by progressively refining subspaces for reliable pseudo-labels.

New model for multiplex networks learns shared structure.

Meta-learning bandits by reducing dimensionality with PCA.

GAME improves matrix completion by considering subgroup-specific latent structures.

In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combi- nat…

This paper presents a new multitask learning framework that learns a shared representation among the tasks, incorporating both task and feature clusters. The jointly-induced clusters yield a shared latent subspace where task relationships are learned more effectively and more generally than in state-of-the-art multitas…

Multi-view clustering is an important and fundamental problem. Many multi-view subspace clustering methods have been proposed, and most of them assume that all views share a same coefficient matrix. However, the underlying information of multi-view data are not fully exploited under this assumption, since the coefficie…

Method preserves correlations in synthetic data.

New algorithm catches moving subspaces in bandit problems.

Sparse subspace clustering (SSC) is one of the current state-of-the-art methods for partitioning data points into the union of subspaces, with strong theoretical guarantees. However, it is not practical for large data sets as it requires solving a LASSO problem for each data point, where the number of variables in each…

Enhances robustness of multi-view clustering via partition fusion.

Quaternion self-attention reduces computational cost and improves performance.

Adversarial examples are maliciously perturbed inputs designed to mislead machine learning (ML) models at test-time. They often transfer: the same adversarial example fools more than one model. In this work, we propose novel methods for estimating the previously unknown dimensionality of the space of adversarial inputs…

CoreFlow models matrix-valued distributions efficiently, preserving shared low-rank structure.

The hyperbolic manifold is a smooth manifold of negative constant curvature. While the hyperbolic manifold is well-studied in the literature, it has gained interest in the machine learning and natural language processing communities lately due to its usefulness in modeling continuous hierarchies. Tasks with hierarchica…

New bounds on learning shared representations improve model performance and efficiency.

The paper analyzes PLS-SVD in high-dimensional data integration, revealing its strengths and limitations.

LASER compresses recursive model activations by exploiting their low-dimensional structure.

Proposes a transfer learning method for PCA studies.

We will develop simple relations between the arc-lengths of a pair of geodesics that share common end-points. The two geodesics differ only by the requirement that one is constrained to lie in a subspace of the parent manifold. We will present two applications of our results. In the first example we explore the converg…

Improved neural network training in low-dimensional random bases.

Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts

Hierarchical beta process has found interesting applications in recent years. In this paper we present a modified hierarchical beta process prior with applications to hierarchical modeling of multiple data sources. The novel use of the prior over a hierarchical factor model allows factors to be shared across different …

Proposes HeteroJIVE for joint subspace estimation in multi-view data with statistical and structural heterogeneity.

This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called Gen-RKM. To enable joint multi-view generation, this mechanism uses a shared representation of data from various views. Furthermore, the mod…

Proposes joint LCA for multiview data to identify shared and view-specific components.

Proposes a method for multi-view clustering that integrates consistent and complementary graph regularizers.