GSR optimizes tasks in scientific workflows, improving performance across diverse applications.
problem Uncertainty in task selection and evaluation in scientific workflow optimization.
method Generate-Select-Refine (GSR) framework that alternates between task generation and optimization.
result GSR outperforms existing LLM-based optimizers in various scientific applications.
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…
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.
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…
A new meta-learning BO approach that bypasses surrogate models and directly learns task utility.
problem Scalability issues and sensitivity to task similarity in existing meta-learning BO methods.
method Directly learns the utility of queries across tasks, models task uncertainty, and includes an auxiliary model for robust adaptation.
result Demonstrates strong anytime performance and outperforms state-of-the-art methods in various benchmarks.
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.
Learned optimizers trained on many tasks improve model training efficiency and generalization.
problem Training models efficiently and effectively with minimal user intervention.
method Developed a neural network-based hierarchical optimizer trained on thousands of tasks.
result The learned optimizers generalize better to unseen tasks and exhibit unique behaviors.
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…
DUET optimizes LLM training data mixtures using feedback from unseen tasks.
problem Optimizing LLM performance on unseen tasks without prior data knowledge.
method Interleaves influence function with Bayesian optimization for data selection.
result DUET converges to optimal training data mixture for unseen tasks.
Optimal task order improves continual learning performance.
problem Challenges in neural networks learning multiple tasks in sequence.
method Linear teacher-student model with latent factors, derived analytical expression.
result Two principles for optimal task order: least representative first and dissimilar adjacent tasks.
HyperSTAR optimizes deep network hyperparameters for specific tasks.
problem Finding optimal hyperparameters for deep neural networks is computationally expensive and task-agnostic approaches are inefficient.
method HyperSTAR learns a dataset representation and a performance predictor from raw images, ranking and recommending hyperparameters conditioned on a joint dataset-hyperparameter space.
result HyperSTAR reduces HPO time by 50% on large-scale image classification datasets.
M-L2O adapts fast to new tasks by self-adapting during test-time.
problem L2O optimizers struggle with out-of-distribution tasks.
method Meta-training an L2O optimizer to adapt quickly to new tasks.
result M-L2O converges significantly faster than vanilla L2O with only 5 steps of adaptation.
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.
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.
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.
Meta-BO method clusters and learns from prior tasks to optimize heterogeneous functions.
problem Optimizing multiple functions with historical data and scalability issues.
method Clustering-based meta-learning, surrogate prototypes, adaptive weighting policies.
result Scalable and robust meta-BO method improves convergence to global optimum.
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.
Paper studies how few pretraining tasks are needed for a linear model to solve new tasks.
problem How many pretraining tasks are needed for a linear model to solve new tasks?
method Pretrained a linear attention model for linear regression with a Gaussian prior.
result Effective pretraining requires a small number of independent tasks, and the model closely matches Bayes optimal.
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.
Study task-guided exploration in linear dynamical systems, improving sample complexity.
problem Efficiently learning about an environment to complete a specific task.
method Proposed a computationally efficient experiment-design based exploration algorithm.
result Optimally explores the environment, collecting precise information needed to complete the task.
We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly…
AdMRL improves meta-reinforcement learning by minimizing worst-case sub-optimality gap.
problem Meta-reinforcement learning's sensitivity to task distribution shift.
method Model-based adversarial approach with minimax objective and alternating optimization.
result Efficacy in worst-case performance, generalization to out-of-distribution tasks, and sample efficiency.
Novel hyperparameter optimization for target tasks under covariate shift.
problem Hyperparameter optimization under multi-source covariate shift.
method Construct variance reduced estimator to unbiasedly approximate target objective; propose no-regret hyperparameter optimization procedure.
result Proposed framework broadens applications of automated hyperparameter optimization.
System learns optimizer hyperparameters to generalize across tasks.
problem Lack of generalization in learning optimizers for neural networks.
method Generalization-first approach, learning optimizer hyperparameters.
result System outperforms Adam on various tasks, including unseen modalities.
A new optimizer, Grad-Avg, converges faster than SGD and improves classification task performance.
problem Optimizing error functions in regression and classification tasks.
method Grad-Avg optimizer based on gradient averaging, with mathematical convergence proof and parameter scaling for classification.
result Grad-Avg converges faster than other optimizers for classification tasks on benchmark datasets.
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.
Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters. Appropriately scheduling the optimization of a task objective or a set of parameters is usually crucial to the quality of convergence. In this paper, we present AutoLoss,…
Paper tackles robust knowledge transfer in parallel RL tasks.
problem Transfer knowledge from low-tier to high-tier tasks in parallel RL without shared dynamics or reward functions.
method Identifies Optimal Value Dominance condition and proposes online learning algorithms for both tasks.
result Achieves constant regret on partial states and near-optimal regret when tasks are dissimilar.
TaskSet dataset speeds up hyperparameter optimization.
problem Optimizing hyperparameters for various machine learning tasks.
method Meta-learning an ordered list of hyperparameters from a large dataset.
result Meta-learning achieves large speedups in sample efficiency.
Meta-learning for discrete tasks using submodular optimization.
problem Improving performance on new tasks with limited data.
method Discrete submodular optimization for personalized initial solutions.
result Significant reduction in computational cost for new tasks.
Paper integrates predictive and prescriptive tasks using bilevel optimization.
problem Combining prediction and decision-making in machine learning.
method Bilevel optimization for integrating predictive and prescriptive tasks.
result Improved decision-making through direct integration of predictions.
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.
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.
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.
Efficient streaming algorithms for robust statistics with near-optimal memory.
problem High-dimensional robust statistics tasks in streaming model.
method First efficient streaming algorithms with near-optimal memory requirements.
result Near-optimal error guarantees and space complexity nearly-linear in the dimension for robust mean estimation.
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.
Meta-learning balances task-specific modeling and optimization complexity.
problem Balancing accurate task-specific modeling with ease of optimization in meta-learning.
method Theoretical and empirical analysis of trade-off between modeling and optimization in meta-learning.
result Explicit bounds on modeling and optimization errors for non-convex and linear regression problems.
LIBO optimizes repeated bandit tasks without prior knowledge or regret.
problem Optimizing repeated bandit tasks without prior knowledge or regret.
method LIBO sequentially meta-learns a kernel to adapt to the environment and solve tasks with the latest estimate.
result LIBO achieves sublinear lifelong regret, converging to oracle performance as more tasks are solved.
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.
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.
MAML outperforms NAL in diverse task landscapes.
problem Understanding when and how MAML outperforms NAL in various task landscapes.
method Analytical and numerical studies in a linear regression setting with a mixture of easy and hard tasks.
result MAML gains over NAL when there is task hardness discrepancy and optimal solutions of hard tasks are closely packed.
This paper tackles multilingual speech processing by optimizing conflicting objectives hierarchically.
problem Training models for multilingual, multi-task speech processing is hampered by conflicting objectives.
method Investigates three multi-objective MSP formulations and introduces a lightweight layer-selection mechanism.
result A bi-level recipe outperforms standard flat optimization in state-of-the-art MSP models.
CausalCOMRL improves RL task representations by integrating causal relationships, enhancing generalizability.
problem Spurious correlations in context-based offline meta-reinforcement learning.
method CausalCOMRL integrates causal representation learning to uncover and incorporate causal relationships among task components.
result CausalCOMRL achieves better performance on meta-reinforcement learning benchmarks.
ScaML-GP efficiently learns from few meta-tasks using Gaussian processes.
problem Exploiting historical data for quick task solving in low-data regimes.
method Modular Gaussian process model with a carefully designed multi-task kernel.
result ScaML-GP learns efficiently with few and many meta-tasks.
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.
In this paper, we present a new approach to Transfer Learning (TL) in Reinforcement Learning (RL) for cross-domain tasks. Many of the available techniques approach the transfer architecture as a method of speeding up the target task learning. We propose to adapt and reuse the mapped source task optimal-policy directly …
We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical…
Improved stability and generalization for blackbox learned optimizers.
problem Stability and generalization issues in blackbox learned optimizers.
method Investigation using dynamical systems, modifications to optimizer architecture and meta-training procedure.
result Improved stability and generalization of learned optimizers.