Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
Restless Bandit Problem with Rewards Generated by a Linear Gaussian Dynamical System
Decision-making under uncertainty is a fundamental problem encountered frequently and can be formulated as a stochastic multi-armed bandit problem. In the problem, the learner interacts with an environment by choosing an…
Decision Making Under UncertaintyHyperparameter OptimizationHyperparameter Importance Analysis for Multi-Objective AutoML
Hyperparameter optimization plays a pivotal role in enhancing the predictive performance and generalization capabilities of ML models. However, in many applications, we do not only care about predictive performance but a…
AutoMLHyperparameter OptimizationAutomating Code Adaptation for MLOps -- A Benchmarking Study on LLMs
This paper explores the possibilities of the current generation of Large Language Models for incorporating Machine Learning Operations (MLOps) functionalities into ML training code bases. We evaluate the performance of O…
BenchmarkingHyperparameter OptimizationModel OptimizationTranslationAequitas Flow: Streamlining Fair ML Experimentation
Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps that exist in other fair ML packages. In ad…
BenchmarkingFairnessHyperparameter OptimizationDeep Learning and genetic algorithms for cosmological Bayesian inference speed-up
In this paper, we present a novel approach to accelerate the Bayesian inference process, focusing specifically on the nested sampling algorithms. Bayesian inference plays a crucial role in cosmological parameter estimati…
Bayesian InferenceHyperparameter Optimizationparameter estimationIn-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
With the increasing computational costs associated with deep learning, automated hyperparameter optimization methods, strongly relying on black-box Bayesian optimization (BO), face limitations. Freeze-thaw BO offers a pr…
Bayesian OptimizationHyperparameter OptimizationIn-Context LearningTransductive Spiking Graph Neural Networks for Loihi
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural netwo…
Bayesian OptimizationGraph ClassificationHyperparameter OptimizationHyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It
There has been a growing interest in off-policy evaluation in the literature such as recommender systems and personalized medicine. We have so far seen significant progress in developing estimators aimed at accurately es…
counterfactualDecision MakingHyperparameter OptimizationOff-policy evaluation+1Self-adaptive PSRO: Towards an Automatic Population-based Game Solver
Policy-Space Response Oracles (PSRO) as a general algorithmic framework has achieved state-of-the-art performance in learning equilibrium policies of two-player zero-sum games. However, the hand-crafted hyperparameter va…
Hyperparameter OptimizationGeneralized Population-Based Training for Hyperparameter Optimization in Reinforcement Learning
Hyperparameter optimization plays a key role in the machine learning domain. Its significance is especially pronounced in reinforcement learning (RL), where agents continuously interact with and adapt to their environmen…
Computational EfficiencyHyperparameter Optimizationreinforcement-learningReinforcement Learning+1Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage the advanced search algorithms for multiobjective optimization in DeepHyper, a scalable …
Efficient ExplorationHyperparameter OptimizationMultiobjective OptimizationHyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
SecureBoost is a tree-boosting algorithm that leverages homomorphic encryption (HE) to protect data privacy in vertical federated learning. SecureBoost and its variants have been widely adopted in fields such as finance …
Bayesian OptimizationFederated LearningHyperparameter OptimizationInference Attack+1Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks
This paper investigates how various randomization techniques impact Deep Neural Networks (DNNs). Randomization, like weight noise and dropout, aids in reducing overfitting and enhancing generalization, but their interact…
Data AugmentationHyperparameter OptimizationThe Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization
To reach high performance with deep learning, hyperparameter optimization (HPO) is essential. This process is usually time-consuming due to costly evaluations of neural networks. Early discarding techniques limit the res…
Hyperparameter OptimizationTowards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks
Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose environmental and resource challenges. Deep shift neural networks (D…
AutoMLHyperparameter OptimizationIntelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers
Pretraining language models on large text corpora is a common practice in natural language processing. Fine-tuning of these models is then performed to achieve the best results on a variety of tasks. In this paper, we in…
Hyperparameter OptimizationSimple Hack for Transformers against Heavy Long-Text Classification on a Time- and Memory-Limited GPU Service
Many NLP researchers rely on free computational services, such as Google Colab, to fine-tune their Transformer models, causing a limitation for hyperparameter optimization (HPO) in long-text classification due to the met…
ArticlesGPUHyperparameter Optimizationtext-classification+1Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates
We study the problem of efficiently computing the derivative of the fixed-point of a parametric nondifferentiable contraction map. This problem has wide applications in machine learning, including hyperparameter optimiza…
Data PoisoningHyperparameter OptimizationMeta-LearningLarge Language Models to Generate System-Level Test Programs Targeting Non-functional Properties
System-Level Test (SLT) has been a part of the test flow for integrated circuits for over a decade and still gains importance. However, no systematic approaches exist for test program generation, especially targeting non…
C++ codeHyperparameter OptimizationBreast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability
Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection an…
Cancer ClassificationHyperparameter Optimization