Disentangled representations naturally emerge in multi-task learning.
problem Finding adaptable representations for multiple tasks.
method Empirical study of neural networks trained on automatically generated supervised tasks.
result Disentanglement naturally occurs during multi-task learning.
Develops a method to learn metrics across multiple domains using heterogeneous transfer learning.
problem Limited labeled data in target domain and heterogeneous data across multiple domains.
method HMTML framework that learns metrics and transformations across multiple domains, maximizing high-order covariance in a common subspace.
result Effective feature transformations and metrics learned across multiple domains, validated by extensive experiments.
Rugby-Bot predicts multiple metrics from a single source using fine-grain data.
problem Complexity of sporting events requires multiple metrics for accurate analysis.
method Multi-task learning with fine-grain spatial data and wide-and-deep learning.
result Predictions are consistent and can be in distribution form.
MBML improves multi-task RL by inferring task identity from state-action pairs.
problem Multi-task batch reinforcement learning with unseen tasks.
method MBML uses triplet loss and relabeling to robustify task inference.
result Significantly faster convergence on unseen tasks compared to random initialization.
This paper investigates multi-task reinforcement learning in non-Markovian decision making, showing benefits in sample efficiency.
problem Investigating multi-task reinforcement learning in non-Markovian decision making processes.
method Developed a joint model class for tasks and used the η-bracketing number to quantify complexity and similarity. result Multi-task reinforcement learning can improve sample efficiency in non-Markovian decision making processes.
Paper proposes multi-task learning for multi-modal video Q&A.
problem Expensive to create large-scale datasets for multi-modal video Q&A.
method Composed of three networks: video Q&A, temporal retrieval, and modality alignment.
result State-of-the-art results on TVQA dataset.
Proposes a new distance metric for multi-marginal optimal transport.
problem Computational scalability in multi-marginal optimal transport.
method Random one-dimensional projections to construct sliced multi-marginal Wasserstein distance.
result Sliced multi-marginal Wasserstein distance is a metric with dimension-free sample complexity.
DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data. They ignore the semantically similar and dissimilar constraints bet…
Improves few-shot learning using multi-task representation learning theory.
problem Few-shot learning with limited data.
method Multi-task representation learning theory and spectral-based regularization.
result Improved performance of meta-learning methods through new spectral regularization.
Graph representation learning improves with domain knowledge.
problem Efficiently learning graph representations from scarce labels.
method Multi-task knowledge distillation combining graph metrics.
result Improves prediction performance, especially with limited training data.
Multi-task learning (MTL) improves prediction performance in different contexts by learning models jointly on multiple different, but related tasks. Network data, which are a priori data with a rich relational structure, provide an important context for applying MTL. In particular, the explicit relational structure imp…
Deep learning models predict postoperative complications more accurately than random forests.
problem Predicting postoperative complications to inform patient care decisions.
method Multi-task deep neural networks integrating intraoperative physiological data.
result Deep learning models improved prediction accuracy and provided interpretable risk factors.
Develops a new fairness learning approach for multi-task regression models.
problem Fairness in multi-task regression models with biased datasets.
method Uses rank-based non-parametric independence test (Mann Whitney U statistic) and reformulates as non-convex optimization problem.
result Outperforms state-of-the-art methods on fairness metrics.
New landmark states improve transfer learning in multi-task RL.
problem Improving sample complexity and regret in new RL tasks.
method Topological landmark covering, landmark value functions, action pruning.
result Theoretical bounds on Q values at state-action pairs.
Gradient surgery improves multi-task learning efficiency.
problem Challenges in sharing structure across multiple tasks.
method Gradient projection onto normal plane of conflicting gradients.
result Substantial gains in efficiency and performance.
Small parameterized towers improve multi-task learning efficiency and generalization.
problem Balancing Pareto efficiency and generalization in multi-task learning.
method Under-parameterized self-auxiliaries for multi-task models.
result Small parameterized towers enhance Pareto efficiency in various multi-task applications.
Reliable and effective multi-task learning is a prerequisite for the development of robotic agents that can quickly learn to accomplish related, everyday tasks. However, in the reinforcement learning domain, multi-task learning has not exhibited the same level of success as in other domains, such as computer vision. In…
Develops a new method to improve performance in multi-objective learning problems.
problem Gradient bias in multi-objective learning leading to degraded performance.
method Stochastic Multi-objective gradient Correction (MoCo) method that guarantees convergence without increasing batch size.
result Demonstrates effectiveness of MoCo method in simulations on multi-task learning.
Multi-task learning uses auxiliary data or knowledge from relevant tasks to facilitate the learning in a new task. Multi-task optimization applies multi-task learning to optimization to study how to effectively and efficiently tackle multiple optimization problems simultaneously. Evolutionary multi-tasking, or multi-fa…
This study proposes a deep learning framework using ResNeXt for efficient financial data mining.
problem Complex financial data with high dimensionality, nonlinearity, and task correlations.
method Introduces ResNeXt into multi-task learning framework for efficient feature extraction and task collaboration.
result Significantly improved performance in classification and regression tasks on S&P 500 data.
Automatically finds efficient multi-task models with less data.
problem Training separate models requires more data, parameters, and time.
method Compact search space for multi-task architectures, feature distillation for quick evaluation.
result Automatically identifies multi-task architectures that balance resource requirements and performance.
Survey on multi-task learning for deep neural networks.
problem Simultaneous learning of multiple tasks by a shared model.
method Partitioning deep MTL techniques into architectures, optimization methods, and task relationship learning.
result Improved data efficiency and reduced overfitting through shared representations.
Wide neural networks can benefit from multi-task learning in their infinite-width limit.
problem The generalization behavior of wide neural networks in multi-task learning settings.
method Optimizing wide ReLU neural networks with L2-regularization promotes multi-task learning in the infinite-width limit.
result An exact quantitative characterization of multi-task learning in the infinite-width limit of wide ReLU neural networks.
New approach for multi-task reinforcement learning without task interference.
problem Efficient knowledge sharing between tasks in reinforcement learning.
method Attention-based multi-task deep reinforcement learning.
result Achieves positive knowledge transfer and avoids negative transfer.
Deep multi-task learning benefits from low intrinsic dimensionality, leading to better generalization.
problem Improving generalization in deep multi-task learning with high-dimensional models.
method Parametrizing multi-task networks in a low-dimensional space using random expansions and weight compression.
result First non-vacuous generalization bounds for deep multi-task networks are derived.
Near-optimal rates for multi-task learning with shared representations.
problem Approximation and statistical complexity of learning multiple operators.
method Multiple Neural Operators (MNO) architecture and comparison with DeepONet.
result Near-optimal upper and lower bounds for approximation and generalization.
VIRTUAL improves federated multi-task learning for non-convex models.
problem Real-world federated datasets show statistical heterogeneity.
method VIRTUAL treats federated network as a star-shaped Bayesian network and uses variational inference.
result VIRTUAL outperforms state-of-the-art for federated learning on real-world datasets.
Improves shared encoder representations for better multi-task learning performance.
problem Improving quality of shared encoder representations in multi-task learning.
method Dummy Gradient norm Regularization (DGR) to decrease gradient norm of dummy task-specific predictors.
result DGR improves multi-task prediction performances and superior performance compared to existing methods.
A new framework integrates classification and regression tasks in multi-task learning.
problem Jointly solving classification and regression tasks in multi-task scenarios.
method Two-Stage Learning-to-Defer (L2D) framework with a unified deferral mechanism.
result Unified deferral mechanism ensures convergence to the Bayes-optimal rejector.
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…
SON-GOKU uses graph coloring to improve multi-task learning by partitioning tasks into compatible groups.
problem Gradient interference between conflicting multi-task learning objectives slows convergence and model performance.
method SON-GOKU computes gradient interference, constructs an interference graph, and applies greedy graph-coloring to partition tasks.
result SON-GOKU consistently outperforms baselines and state-of-the-art multi-task optimizers on six datasets.
New method learns multiple reward functions for complex tasks.
problem Learning reward functions for tasks with multiple ways of solving.
method Combines maximum entropy approach with Dirichlet process clustering.
result Method accurately learns reward functions for complex tasks.
Research explores the trade-off between multi-task learning and multitasking in deep neural networks.
problem Trade-off between multi-task learning and multitasking in deep neural networks.
method Meta-learning algorithm to manage the trade-off between shared and separated representations.
result Agent successfully optimizes training strategy based on environment.
Multi-task learning is a learning paradigm which seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this paper, we propose a regularization formulation for learning the relationships between tasks in multi-task learning. This formulation can be viewed as a n…
Bayesian approach improves network lasso for multi-task learning.
problem Improving the determination of relational coefficients in network lasso.
method Proposes a Bayesian approach to solve multi-task learning problems using network lasso.
result Objective determination of relational coefficients through Bayesian estimation.
The paper uses random matrix theory for multi-task regression, improving time series forecasting.
problem Improving time series forecasting using multi-task regression.
method Applying random matrix theory to multi-task regression problems, deriving closed-form solutions for optimization.
result Provides a robust foundation for hyperparameter optimization in multi-task regression scenarios.
Multi-task learning aims to learn multiple tasks jointly by exploiting their relatedness to improve the generalization performance for each task. Traditionally, to perform multi-task learning, one needs to centralize data from all the tasks to a single machine. However, in many real-world applications, data of differen…
Develops a method for fairness in multi-task learning using Wasserstein barycenters.
problem Extending fairness to multi-task learning with shared representations.
method Definition of Strong Demographic Parity extended to multi-task learning using multi-marginal Wasserstein barycenters. Closed form solution for optimal fair predictor.
result Empirical results show practical value of post-processing methodology in promoting fair decision-making.
Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated a…
Flexible multi-task learning framework using summary statistics.
problem Data-sharing constraints in healthcare settings.
method Proposes a flexible multi-task learning framework utilizing summary statistics and adaptive parameter selection.
result Systematic non-asymptotic analysis and simulations demonstrate the method's performance.
Study improves GMM learning performance through multi-task and transfer learning.
problem Improving GMM learning performance through similar task structures.
method Proposes a multi-task GMM learning procedure based on EM algorithm, robust to outliers.
result Achieves minimax optimal rate of convergence for parameter estimation and mis-clustering.
PoDiRe learns long-term rewards in multi-task recommendations.
problem Long-term rewards in multiple recommendation tasks.
method Policy Distilled Reinforcement Learning (PoDiRe) combining deep reinforcement learning and knowledge distillation.
result PoDiRe outperforms state-of-the-art methods in real-world data.
Proposes a low-rank deep CNN for multi-task learning.
problem Multi-task learning with deep neural networks.
method Low-rank deep network with nuclear norm and sparsity penalties.
result Improves performance on multiple tasks compared to standard models.
This paper reviews dMTL and methods for selecting auxiliary tasks.
problem Improving model performance for multiple tasks.
method Review of dMTL approaches and methods for selecting auxiliary tasks.
result Methods for selecting auxiliary tasks can improve dMTL performance.
We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training data. This flexible framework allows us to naturally derive formulations for global, multi-task and local metric learning. The resulting algor…
This paper proposes a method to reveal task relationships in multi-task learning models using sparse graphs.
problem Understanding the underlying task relationships in multi-task learning models.
method Proposes a bilevel formulation of multi-task learning that induces sparse graphs.
result The method improves interpretability of multi-task learning models without sacrificing generalization performance.
A new method for multi-task learning by allocating parameters.
problem Sharing parameters between unrelated tasks can hurt performance.
method Learned binary variables to allocate components to tasks, encouraging sharing between related tasks.
result Achieves a 17% relative reduction of the error rate on Omniglot benchmark.
Pareto MTL finds optimal solutions for multiple tasks with different trade-offs.
problem Finding a single optimal solution for multiple conflicting tasks.
method Formulate multi-task learning as multiobjective optimization, decompose into subproblems, solve in parallel.
result Generates well-representative Pareto optimal solutions for different trade-offs.