arXiv research

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

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

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

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

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) …

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

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

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…

Paper introduces a new model to handle multi-task learning across different input domains.

New algorithm speeds up cluster-based compressive sensing tasks.

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.

This work improves trace norm regularization for multi-task learning with limited data.

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…

MT-HAL learns features and task associations for multiple tasks with a shared sparse structure.

We present a multi-task learning formulation for Deep Gaussian processes (DGPs), through non-linear mixtures of latent processes. The latent space is composed of private processes that capture within-task information and shared processes that capture across-task dependencies. We propose two different methods for segmen…

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…

Graph networks struggle with multi-task learning due to varying property loss surface curvatures.

In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination o…

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

The paper provides fast, sample-efficient algorithms for learning common features in multi-task linear regression.

We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGCPs, whose log intensities are given by linear combinations of latent functions drawn from Gaussian process priors. The combination coefficients are als…

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

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…

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

Small parameterized towers improve multi-task learning efficiency and generalization.

Multi-task learning holds the promise of less data, parameters, and time than training of separate models. We propose a method to automatically search over multi-task architectures while taking resource constraints into consideration. We propose a search space that compactly represents different parameter sharing strat…

New method improves hyperparameter tuning efficiency across similar tasks.

Broker uses multi-task dynamic pricing to learn competitive prices in credit markets.

Proposes a new method for clustering tasks in multi-task learning.

Estimates multiple related causal graphs with shared causal order.

SON-GOKU uses graph coloring to improve multi-task learning by partitioning tasks into compatible groups.

New image restoration method using localized patches and external databases.

MTL improves multi-dimensional regression in luminescence sensing.

The paper uses distance correlation for brain connectivity and a novel multi-task learning model for age prediction.

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

Bayesian approach improves network lasso for multi-task learning.

MixTS uses a mixture prior to analyze Thompson Sampling in multi-task learning.

This paper investigates multi-task reinforcement learning in non-Markovian decision making, showing benefits in sample efficiency.

Influential node detection is a central research topic in social network analysis. Many existing methods rely on the assumption that the network structure is completely known \textit{a priori}. However, in many applications, network structure is unavailable to explain the underlying information diffusion phenomenon. To…

While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge. Multi-task learning has emerged as a promising approach for sharing structure across multiple tasks…

This paper reviews dMTL and methods for selecting auxiliary tasks.

Rewriting history improves RL algorithms for solving multiple tasks.

Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. Many of the existing exploration frameworks such as $E^3$, $R_{max}$, Thompson sampling assume a single stationary MDP and are not suitable for system identification in the multi-task setting. We present a nove…

This paper proposes a method to reveal task relationships in multi-task learning models using sparse graphs.

This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compa…

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 …

A significant challenge to make learning techniques more suitable for general purpose use is to move beyond i) complete supervision, ii) low dimensional data, iii) a single task and single view per instance. Solving these challenges allows working with "Big Data" problems that are typically high dimensional with multip…