Hyperparameter Optimization
277 papers with code • 1 benchmarks • 3 datasets
Hyperparameter Optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Whether the algorithm is suitable for the data directly depends on hyperparameters, which directly influence overfitting or underfitting. Each model requires different assumptions, weights or training speeds for different types of data under the conditions of a given loss function.
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Use these libraries to find Hyperparameter Optimization models and implementationsLatest papers with no code
Universal Link Predictor By In-Context Learning on Graphs
In this work, we introduce the Universal Link Predictor (UniLP), a novel model that combines the generalizability of heuristic approaches with the pattern learning capabilities of parametric models.
Poisson Process for Bayesian Optimization
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian process (GP).
Glocal Hypergradient Estimation with Koopman Operator
Through numerical experiments of hyperparameter optimization, including optimization of optimizers, we demonstrate the effectiveness of the glocal hypergradient estimation.
Breaking MLPerf Training: A Case Study on Optimizing BERT
Speeding up the large-scale distributed training is challenging in that it requires improving various components of training including load balancing, communication, optimizers, etc.
Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble
This paper explores meta-learners for stacking ensemble in HPO, free of hyperparameter tuning, able to reduce the effects of multicollinearity and considering the ensemble learning process generalization power.
Large Language Model Agent for Hyper-Parameter Optimization
Hyperparameter optimization is critical in modern machine learning, requiring expert knowledge, numerous trials, and high computational and human resources.
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research
One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments.
A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization
To capture the conditional dependence between branching and nested parameters, a unified Bayesian optimization framework is proposed.
Adaptive Regret for Bandits Made Possible: Two Queries Suffice
In this paper, we give query and regret optimal bandit algorithms under the strict notion of strongly adaptive regret, which measures the maximum regret over any contiguous interval $I$.
Hypercomplex neural network in time series forecasting of stock data
The goal of this paper is to test three classes of neural network (NN) architectures based on four-dimensional (4D) hypercomplex algebras for time series prediction.