arXiv research

The paper uses random matrix theory for multi-task regression, improving time series forecasting.

Paper estimates noise covariance in correlated multi-task linear models.

Unified analysis of multi-task functional linear regression with manifold and composite penalties.

Develops a scalable multi-task Gaussian process with neural embedding for improved performance.

New estimator tackles multi-task linear regression with outliers, avoiding eigenvalue lower bounds.

Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients. Multi-task learning (MTL) has been commonly utilized by…

The one-class kernel spectral regression (OC-KSR), the regression-based formulation of the kernel null-space approach has been found to be an effective Fisher criterion-based methodology for one-class classification (OCC), achieving state-of-the-art performance in one-class classification while providing relatively hig…

We study the stability properties of nonlinear multi-task regression in reproducing Hilbert spaces with operator-valued kernels. Such kernels, a.k.a. multi-task kernels, are appropriate for learning prob- lems with nonscalar outputs like multi-task learning and structured out- put prediction. We show that multi-task ke…

Study multi-task learning with low-rank representation in stochastic linear bandits.

MTL improves multi-dimensional regression in luminescence sensing.

A new framework integrates classification and regression tasks in multi-task learning.

Multi-task learning has shown to significantly enhance the performance of multiple related learning tasks in a variety of situations. We present the fused logistic regression, a sparse multi-task learning approach for binary classification. Specifically, we introduce sparsity inducing penalties over parameter differenc…

This work improves multi-task regression performance using approximations of full-conformal prediction.

We study the problem of estimating multiple linear regression equations for the purpose of both prediction and variable selection. Following recent work on multi-task learning Argyriou et al. [2008], we assume that the regression vectors share the same sparsity pattern. This means that the set of relevant predictor var…

A new multi-task learning estimator improves Gaussian graphical regression model fitting.

Extends neural diffusion processes for multi-task regression.

New method generates continuous Pareto sets for multi-task learning.

High dimensional regression benefits from sparsity promoting regularizations. Screening rules leverage the known sparsity of the solution by ignoring some variables in the optimization, hence speeding up solvers. When the procedure is proven not to discard features wrongly the rules are said to be \emph{safe}. In this …

Paper extends multi-task Gaussian Cox processes for heterogeneous tasks.

In this paper, we investigate a multivariate multi-response (MVMR) linear regression problem, which contains multiple linear regression models with differently distributed design matrices, and different regression and output vectors. The goal is to recover the support union of all regression vectors using $l_1/l_2$-reg…

Many real-world datasets are labeled with natural orders, i.e., ordinal labels. Ordinal regression is a method to predict ordinal labels that finds a wide range of applications in data-rich domains, such as natural, health and social sciences. Most existing ordinal regression approaches work well for independent and id…

Develops a new fairness learning approach for multi-task regression models.

We consider multi-task regression models where observations are assumed to be a linear combination of several latent node and weight functions, all drawn from Gaussian process (GP) priors that allow nonzero covariance between grouped latent functions. We show that when these grouped functions are conditionally independ…

This paper proposes MM-DAGs for analyzing traffic congestion, learning multiple DAGs jointly.

We demystify attention patterns in multi-head softmax models for linear data.

Many real-world large-scale regression problems can be formulated as Multi-task Learning (MTL) problems with a massive number of tasks, as in retail and transportation domains. However, existing MTL methods still fail to offer both the generalization performance and the scalability for such problems. Scaling up MTL met…

Sensors are an integral part of modern Internet of Things (IoT) applications. There is a critical need for the analysis of heterogeneous multivariate temporal data obtained from the individual sensors of these systems. In this paper we particularly focus on the problem of the scarce amount of training data available pe…

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

Proposes an MTL method with clustering to improve regression accuracy.

This study compares mtl architectures for renewable power generation forecasting.

New algorithm improves learning efficiency in multi-task contextual bandits.

The paper shows how multi-task learning in neural networks is similar to kernel regression and Hilbert spaces.

This paper addresses the meta-learning problem in sparse linear regression with infinite tasks. We assume that the learner can access several similar tasks. The goal of the learner is to transfer knowledge from the prior tasks to a similar but novel task. For p parameters, size of the support set k , and l samples per …

SMART transfers knowledge across related studies for multi-task learning.

Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous experience to rapidly learn new skills or adapt to new environments. Representation learning -- a key tool for performing meta-learning -- learns a data representation that can transfer knowledge across multiple tasks, which is es…

We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task regressio…

Develops a method for fairness in multi-task learning using Wasserstein barycenters.

New algorithms minimize regret in multi-task and lifelong linear bandits with shared representation.

Proposes a transductive matrix completion method with calibration for multi-task learning.

Paper presents novel online MTL methods using WRLS and OSLSSVR.

We sharply characterize the performance of different penalization schemes for the problem of selecting the relevant variables in the multi-task setting. Previous work focuses on the regression problem where conditions on the design matrix complicate the analysis. A clearer and simpler picture emerges by studying the No…

Proposes a novel MTL approach based on bias-variance analysis.

SOFARI improves inference on multi-task learning latent factors.

The typical multi-task learning methods for spatio-temporal data prediction involve low-rank tensor computation. However, such a method have relatively weak performance when the task number is small, and we cannot integrate it into non-linear models. In this paper, we propose a two-step suboptimal unitary method (SUM) …

New method learns shared structures in non-linear tasks.

Enhances ENet's prediction accuracy while maintaining uncertainty estimation.

Normative modeling has recently been proposed as an alternative for the case-control approach in modeling heterogeneity within clinical cohorts. Normative modeling is based on single-output Gaussian process regression that provides coherent estimates of uncertainty required by the method but does not consider spatial c…

New method for MTL with varying sparsity patterns across tasks.