Papers Hyperparameter Optimization
“Hyperparameter Optimization” 태그가 달린 논문 1,012편 · 필터 해제
A Single-Loop Algorithm for Decentralized Bilevel Optimization
Bilevel optimization has gained significant attention in recent years due to its broad applications in machine learning. This paper focuses on bilevel optimization in decentralized networks and proposes a novel single-lo…
Bilevel OptimizationHyperparameter OptimizationAutoML for Large Capacity Modeling of Meta's Ranking Systems
Web-scale ranking systems at Meta serving billions of users is complex. Improving ranking models is essential but engineering heavy. Automated Machine Learning (AutoML) can release engineers from labor intensive work of …
AutoMLHyperparameter OptimizationNeural Architecture SearchImpact of HPO on AutoML Forecasting Ensembles
A forecasting ensemble consisting of a diverse range of estimators for both local and global univariate forecasting, in particular MQ-CNN,DeepAR, Prophet, NPTS, ARIMA and ETS, can be used to make forecasts for a variety …
AutoMLAvgBayesian OptimisationEnsemble Learning+1TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications
We introduce TabRepo, a new dataset of tabular model evaluations and predictions. TabRepo contains the predictions and metrics of 1310 models evaluated on 200 classification and regression datasets. We illustrate the ben…
AutoMLHyperparameter OptimizationTransfer LearningSaturn: Efficient Multi-Large-Model Deep Learning
In this paper, we propose Saturn, a new data system to improve the efficiency of multi-large-model training (e.g., during model selection/hyperparameter optimization). We first identify three key interconnected systems c…
Deep LearningHyperparameter OptimizationmodelModel Selection+1Predicting Ground Reaction Force from Inertial Sensors
The study of ground reaction forces (GRF) is used to characterize the mechanical loading experienced by individuals in movements such as running, which is clinically applicable to identify athletes at risk for stress-rel…
Hyperparameter OptimizationregressionHodge-Compositional Edge Gaussian Processes
We propose principled Gaussian processes (GPs) for modeling functions defined over the edge set of a simplicial 2-complex, a structure similar to a graph in which edges may form triangular faces. This approach is intende…
Gaussian ProcessesHyperparameter OptimizationLarge-Scale Gaussian Processes via Alternating Projection
Training and inference in Gaussian processes (GPs) require solving linear systems with $n\times n$ kernel matrices. To address the prohibitive $\mathcal{O}(n^3)$ time complexity, recent work has employed fast iterative m…
Gaussian ProcessesHyperparameter OptimizationHyperparameter Optimization for Multi-Objective Reinforcement Learning
Reinforcement learning (RL) has emerged as a powerful approach for tackling complex problems. The recent introduction of multi-objective reinforcement learning (MORL) has further expanded the scope of RL by enabling agen…
Hyperparameter OptimizationMulti-Objective Reinforcement Learningreinforcement-learningReinforcement Learning+1Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting
Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Shor…
BenchmarkingHyperparameter OptimizationProduction ForecastingQuantum Machine Learning+3Hyperparameter optimization of hp-greedy reduced basis for gravitational wave surrogates
In a previous work we introduced, in the context of gravitational wave science, an initial study on an automated domain-decomposition approach for reduced basis through hp-greedy refinement. The approach constructs local…
Bayesian OptimizationHyperparameter Optimizationparameter estimationScrap Your Schedules with PopDescent
In contemporary machine learning workloads, numerous hyper-parameter search algorithms are frequently utilized to efficiently discover high-performing hyper-parameter values, such as learning and regularization rates. As…
Computational EfficiencyHyperparameter OptimizationFairer and More Accurate Tabular Models Through NAS
Making models algorithmically fairer in tabular data has been long studied, with techniques typically oriented towards fixes which usually take a neural model with an undesirable outcome and make changes to how the data …
FairnessHyperparameter OptimizationNeural Architecture SearchA Hyperparameter Study for Quantum Kernel Methods
Quantum kernel methods are a promising method in quantum machine learning thanks to the guarantees connected to them. Their accessibility for analytic considerations also opens up the possibility of prescreening datasets…
Hyperparameter OptimizationQuantum Machine LearningMachine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines
This work endeavors to juxtapose the efficacy of machine learning algorithms within classical and quantum computational paradigms. Particularly, by emphasizing on Support Vector Machines (SVM), we scrutinize the classifi…
Hyperparameter OptimizationQuantum Machine LearningTarget Variable Engineering
How does the formulation of a target variable affect performance within the ML pipeline? The experiments in this study examine numeric targets that have been binarized by comparing against a threshold. We compare the pre…
Hyperparameter OptimizationModel SelectionregressionImproving Fast Minimum-Norm Attacks with Hyperparameter Optimization
Evaluating the adversarial robustness of machine learning models using gradient-based attacks is challenging. In this work, we show that hyperparameter optimization can improve fast minimum-norm attacks by automating the…
Adversarial RobustnessHyperparameter OptimizationAuto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
Classical machine learning models, such as linear models and tree-based models, are widely used in industry. These models are sensitive to data distribution, thus feature preprocessing, which transforms features from one…
AutoMLHyperparameter OptimizationNeural Architecture SearchFedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent
The theoretical landscape of federated learning (FL) undergoes rapid evolution, but its practical application encounters a series of intricate challenges, and hyperparameter optimization is one of these critical challeng…
Federated LearningHyperparameter OptimizationDeterministic Langevin Unconstrained Optimization with Normalizing Flows
We introduce a global, gradient-free surrogate optimization strategy for expensive black-box functions inspired by the Fokker-Planck and Langevin equations. These can be written as an optimization problem where the objec…
Active LearningBayesian OptimizationHyperparameter Optimization