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…
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48 results for “multi-task optimization”
MUMBO optimizes multiple tasks efficiently, even with low-cost related functions.
problem Efficiently optimizing multiple related functions with low-cost evaluations.
method Derives a novel multi-task version of entropy search.
result Robust performance with low computational overhead across various optimization challenges.
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecastingstat.ML
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.
Adaptive data fusion boosts efficiency in multi-task optimization.
problem Multi-task non-smooth optimization in various fields.
method Adaptive data fusion approach leveraging commonalities among objectives.
result Significant improvements in sample efficiency with sharp statistical guarantees.
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.
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.
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…
Multi-task learning is a powerful method for solving multiple correlated tasks simultaneously. However, it is often impossible to find one single solution to optimize all the tasks, since different tasks might conflict with each other. Recently, a novel method is proposed to find one single Pareto optimal solution with…
New method generates continuous Pareto sets for multi-task learning.
problem Challenges in finding optimal solutions for correlated multi-task learning problems.
method Efficiently generates locally continuous Pareto sets and fronts in multi-objective optimization problems.
result Demonstrates continuous analysis of Pareto optimal solutions in machine learning problems.
Study uses multi-task Bayesian optimization to speed up SVM hyperparameter tuning for nodules diagnosis.
problem Redundant and time-consuming hyperparameter tuning for SVM classifiers in medical imaging.
method Employed multi-task Bayesian optimization to accelerate hyperparameter search.
result Multi-task Bayesian optimization significantly accelerates hyperparameter search.
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…
New method finds optimal hyperparameters for multiple tasks and criteria.
problem Finding optimal hyperparameters for multiple tasks and criteria.
method Multi-Task Multi Criteria (MTMC) method that provides Pareto-optimal solutions.
result The method selects optimal hyperparameters based on given criteria significance coefficients.
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.
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.
TempLe learns transition templates for efficient multi-task RL.
problem Efficiently transferring knowledge across different RL tasks with varying state/action spaces.
method Generates transition dynamics templates to abstract similarities between tasks.
result Achieves significantly lower sample complexity than single-task or multi-task methods.
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…
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 algorithms optimize multiple tasks with shared similarities, reducing regret.
problem Optimizing multiple objectives with shared similarities in non-parametric Bayesian optimization.
method Developed two novel BO algorithms using multi-task kernels and random scalarizations.
result Derived worst-case regret bounds capturing inter-task similarities.
A new method for multi-task learning improves performance without weakening inductive bias.
problem Joint optimization of parameters for multiple tasks remains challenging.
method Maximum Roaming, a novel parameter partitioning method inspired by dropout.
result Maximum Roaming improves performance compared to recent multi-task learning formulations.
Proposes a simple framework to balance task difficulty in multi-task learning.
problem Varying difficulty levels among different tasks in multi-task learning.
method Introduces a Balanced Multi-Task Learning (BMTL) framework that transforms training losses to balance task difficulty.
result Empirical studies show state-of-the-art performance of the proposed BMTL framework.
New research shows Multi-Task Learning does not behave like Multi-Objective Optimization.
problem Applying MOO methods to MTL problems is not equivalent.
method Comparing MTL and MOO approaches on Multi-Fashion-MNIST datasets.
result A single model can perform as well as optimizing multiple objectives.
Multi-Task Averagingstat.ML
We present a multi-task learning approach to jointly estimate the means of multiple independent data sets. The proposed multi-task averaging (MTA) algorithm results in a convex combination of the single-task maximum likelihood estimates. We derive the optimal minimum risk estimator and the minimax estimator, and show t…
A TS algorithm improves performance in multi-task bandits with transfer.
problem Improving performance across multiple related bandit tasks.
method A TS-type algorithm for online multi-task learning with a novel concentration inequality.
result The TS-type algorithm achieves nearly-optimal performance guarantees.
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.
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 -bracketing number to quantify complexity and similarity.
result Multi-task reinforcement learning can improve sample efficiency in non-Markovian decision making processes.
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.
Online field experiments are the gold-standard way of evaluating changes to real-world interactive machine learning systems. Yet our ability to explore complex, multi-dimensional policy spaces - such as those found in recommendation and ranking problems - is often constrained by the limited number of experiments that c…
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.
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…
Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximizationmath.OC
Paper tackles multi-block min-max optimization with applications in deep AUC maximization.
problem Multi-block min-max bilevel optimization with non-convex strongly-concave upper level and strongly convex lower level.
method Single-loop randomized stochastic algorithm for constant number of blocks per iteration.
result Sample complexity of O(1/ε^4) for finding ε-stationary point, matching optimal complexity.
This paper simplifies MTGP derivations for Gaussian processes.
problem Understanding the derivations of Multi-task Gaussian Process formulations and their gradients.
method Friendly derivations of Multi-task Gaussian Process formulations and their gradients.
result Simplified derivations of Multi-task Gaussian Process formulations and gradients.
A distributed algorithm for online multi-task learning reduces communication and runtime costs.
problem Heavy communication and high runtime complexity in online multi-task learning.
method Adaptive primal-dual algorithm that synchronizes data across geographically distributed tasks.
result The proposed algorithm achieves optimal regret and is effective on real-world datasets.
Automated multi-task learning algorithm that optimizes network topology.
problem Over-sharing in multi-task learning leads to over-generalization and suboptimal performance.
method Tree-structured design space with gumbel-softmax sampling for differentiable network splitting.
result End-to-end trainable algorithm that optimizes network topology for multiple objectives across tasks.
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…
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 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…
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…
A new framework enables real-time task trade-off control.
problem Conflict between multiple related tasks in a fixed model capacity.
method Formulates MTL as a preference-conditioned multiobjective optimization problem; uses a hypernetwork-based neural network.
result A single model can handle different trade-off preferences among multiple tasks.
Federated learning poses new statistical and systems challenges in training machine learning models over distributed networks of devices. In this work, we show that multi-task learning is naturally suited to handle the statistical challenges of this setting, and propose a novel systems-aware optimization method, MOCHA,…
Algorithm learns which weights to share in deep multi-task learning.
problem Difficulty in deciding which weights to share between tasks in deep learning models.
method Combines natural evolution strategy and stochastic gradient descent to learn optimal weight sharing.
result Task-specific networks achieve lower test errors than existing methods on multi-task learning datasets.
Proposes a new method for clustering tasks in multi-task learning.
problem Improving performance of multiple related prediction tasks.
method Semisoft task clustering approach with a three-step algorithm.
result Validation of the proposed approach on synthetic and real-world datasets.
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.
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 algorithm optimizes privacy and utility in multi-task learning with skewed data.
problem Privacy constraints in multi-task learning with uneven data distribution.
method Adaptive reweighting of privacy budget allocation among tasks.
result Significant improvement in utility with state-of-the-art performance on benchmarks.
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.
Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Stochastic Approachcs.LG
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.
Bayesian Optimization improved for high-dimensional outputs.
problem Optimizing many correlated outcomes efficiently.
method Efficient multi-task Gaussian Process sampling.
result Substantial improvements in sample efficiency.