Stage-based hyper-parameter optimization reduces GPU-hours and training time.
problem Efficiently executing hyper-parameter optimization for deep learning models.
method Stage-based execution strategy to remove redundant computations.
result Stage-based execution outperforms trial-based method by up to 6.60 times in GPU-hours and 4.13 times in training time.
Paper proposes a reinforcement learning framework for efficient hyper-parameter tuning of stochastic optimization algorithms.
problem Efficient tuning of hyper-parameters for stochastic optimization algorithms.
method Modeling hyper-parameter tuning as a Markov decision process and using policy gradient algorithms.
result The proposed framework significantly reduces the time required for hyper-parameter tuning compared to Bayesian optimization.
Hippo optimizes deep learning hyper-parameters by reducing redundant trials.
problem Redundant hyper-parameter trials in hyper-parameter optimization.
method Hippo breaks down hyper-parameter sequences into stages and executes them in a tree structure.
result Hippo reduces GPU-hours and training time significantly compared to existing methods.
This work improves Bayesian Optimization for setting DNN hyper-parameters.
problem Manual setting of DNN hyper-parameters is error-prone and computationally expensive.
method Combines Bayesian Optimization with tuning rules to reduce search space and improve accuracy.
result Improves efficiency and accuracy of hyper-parameter tuning for deep neural networks.
This paper proposes an efficient autoHPO method based on data-to-hyper-parameter mapping.
problem Manual hyper-parameter tuning is costly and dependent.
method The approach is based on mapping from data to hyper-parameters using a sophisticated network structure and effective construction algorithms.
result The proposed approach significantly outperforms state-of-the-art methods.
Evolutionary Strategies optimize hyper-parameters for off-policy learning.
problem Hyper-parameter sensitivity in off-policy learning.
method Application of Evolutionary Strategies for online hyper-parameter tuning.
result Our method outperforms state-of-the-art baselines.
Study examines the cost of tuning hyper-parameters in regression models.
problem Estimating optimal hyper-parameters for regression models.
method Established finite-sample oracle inequalities for hyper-parameter selection.
result Generalization error of selected models shrinks at nearly a parametric rate.
Two-tier approach optimizes RL hyper-parameters for better agent learning.
problem Optimizing hyper-parameters in reinforcement learning to improve agent performance.
method Two-step optimization: first categorical hyper-parameters, then solution-level hyper-parameters.
result Promising results in simulated control tasks, suggesting user-independent reinforcement learning applications.
Federated learning improves with adaptive hyper-parameters and representation matching.
problem Heterogeneous client data leads to divergent local models in federated learning.
method Representation matching and adaptive hyper-parameters.
result Significant performance and robustness improvements in federated learning.
This paper explores hyperparameter optimization for machine learning models.
problem Finding the best hyper-parameters for machine learning models.
method Introduces state-of-the-art optimization techniques and discusses their application.
result Comparison of different optimization methods on benchmark datasets.
A new method selects algorithms and optimizes their hyper-parameters efficiently.
problem Redundant hyper-parameter search space in AutoML.
method Cascaded algorithm selection and hyper-parameter optimization with ER-UCB bandit.
result ER-UCB strategy achieves optimal regret bound for algorithm selection.
KD technique improves QDNN performance with reduced hyper-parameters.
problem Restoring performance loss in QDNNs due to quantization.
method Applied KD with reduced hyper-parameters, including a new coefficient reduction technique.
result Achieved 92.7% test accuracy on CIFAR-10 and 67.0% on CIFAR-100 with 2-bit weights.
Study shows best hyper-parameters improve deep learning model's accuracy for IoT attack detection.
problem Improving accuracy of deep learning model for IoT attack detection.
method Examined three hyper-parameters' influence on model performance.
result Model's reported accuracy not achievable due to optimal hyper-parameters.
Generative approach speeds hyperparameter tuning for machine learning models.
problem Computational infeasibility of cross-validation and difficulty of fully Bayesian hyper-parameter learning.
method Combines optimization-based approximations and amortization techniques.
result Rapid evaluation of hyper-parameters over grids or ranges, supporting predictive tuning and uncertainty quantification.
The quality of an induced model by a learning algorithm is dependent on the quality of the training data and the hyper-parameters supplied to the learning algorithm. Prior work has shown that improving the quality of the training data (i.e., by removing low quality instances) or tuning the learning algorithm hyper-para…
A framework for auto-tuning hyper-parameters in contextual bandit algorithms.
problem Auto-tuning hyper-parameters in real-time for contextual bandit algorithms.
method Proposes a Syndicated Bandits framework to learn multiple hyper-parameters dynamically.
result Achieves optimal regret bounds under certain scenarios and handles multiple contextual bandit algorithms.
BCV method helps estimate clusters and hyper-parameters in large data sets.
problem Determining the number of clusters in large-scale data.
method Bi-cross validation (BCV) for spectral clustering.
result BCV directly applies to spectral clustering for estimating clusters and hyper-parameters.
New estimators outperform maximum likelihood without hyper-parameter estimation.
problem Improving system identification performance without hyper-parameter estimation.
method Developed generalized Bayes and closed-form biased estimators using excess MSE.
result New estimators have comparable performance to empirical-Bayes-based regularized estimator.
Paper proposes efficient GP hyper-parameter optimization methods.
problem Efficient hyper-parameter optimization for Gaussian process regression.
method Cross-validation and ADMM for O(n2) complexity. result Proposed methods outperform traditional ML-based routines.
Paper presents an efficient method for selecting machine learning algorithms and hyper-parameters.
problem Efficient selection of machine learning algorithms and hyper-parameters is challenging for large datasets.
method Progressive sampling-based Bayesian optimization
result Significantly reduces search time, classification error rate, and error rate variability.
Bayesian optimisation has gained great popularity as a tool for optimising the parameters of machine learning algorithms and models. Somewhat ironically, setting up the hyper-parameters of Bayesian optimisation methods is notoriously hard. While reasonable practical solutions have been advanced, they can often fail to …
ABPS improves RL training efficiency by sharing policies and evolving hyper-params.
problem Data inefficiency in training deep RL models for real-world applications.
method ABPS: adaptive behavior policy sharing; ABPS-PBT: hybridizing ABPS with PBT for evolving hyper-params.
result ABPS achieves superior performance and reduced variance compared to conventional hyper-parameter tuning.
XGBoost model improves business risk classification with feature selection and Bayesian hyper-parameter optimization.
problem Improving business risk classification models using advanced machine learning techniques.
method XGBoost, feature selection (FS), Bayesian hyper-parameter optimization (TPE and RS), 10-fold cross-validation.
result Bayesian TPE optimization outperforms random search and achieves higher accuracy and AUC, recall, and F1 score.
Optimizes SGD for text classification using hyper-parameter tuning.
problem Improving performance of SGD in text classification.
method Fine-tuning hyper-parameters using Grid-Search approach.
result Optimized SGD classification leads to better accuracy and execution time.
Bayesian optimisation tackles stochastic MPC hyper-parameter tuning.
problem Fine-tuning hyper-parameters in stochastic MPC models.
method Heteroscedastic Bayesian optimisation framework.
result Framework effectively tunes hyper-parameters in control problems.
Budgeted hyper-parameter tuning algorithm improves performance.
problem Optimizing hyper-parameters with resource constraints.
method Sequential decision making, Bayesian model, action-value function.
result Superior performance across various budgets.
Paper proposes CNN-LSTM model for multi-speaker speech separation.
problem Multi-speaker source separation using deep learning.
method Parallel CNN-LSTM architecture with Bayesian hyperparameter optimization.
result Parallel CNN-LSTM model outperforms LSTM-only and CNN-only models.
Paper proposes a new algorithm for efficient hyper-parameter optimization.
problem Efficient hyper-parameter tuning for machine learning models.
method Information geometric optimization with stochastic natural gradient for discrete search domains.
result The proposed algorithm achieves faster optimization than existing methods without manual tuning.
New method improves anomaly detection in acoustic signals.
problem Poor anomaly detection performance in existing acoustic signal-based unsupervised methods.
method Deep autoencoding Gaussian mixture model with hyper-parameter optimization.
result Significantly improved anomaly detection performance compared to previous methods.
DLOPT simplifies deep learning hyper-parameter optimization.
problem Finding optimal network configurations in deep learning.
method Introduces a novel library for hyper-parameter optimization.
result Demonstrates effectiveness through use examples.
This paper benchmarks and optimizes GBDT algorithms using GPU acceleration.
problem Maximizing predictive power of GBDT models through efficient hyper-parameter tuning.
method Evaluation of GPU acceleration in XGBoost, LightGBM, and Catboost; comparison of hyper-parameter optimization.
result GPU acceleration significantly speeds up hyper-parameter optimization and generalization performance.
In many signal processing problems, it may be fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyper-parameters characterizing the probability distribution of the frame coefficients. This problem is difficult since in general the …
Optimal Word2Vec hyper-parameters improve NLP tasks.
problem Finding the best Word2Vec hyper-parameters for NLP tasks.
method Empirical evaluation of various hyper-parameter combinations on NLP tasks.
result The best hyper-parameters vary by task, and high analogy scores don't always correlate with performance.
Meta-Semi learns to optimize SSL with minimal hyper-parameter tuning.
problem Limited labeled data in SSL makes it impractical to tune many hyper-parameters.
method Meta-learning approach that dynamically reweights unlabeled data loss.
result Meta-Semi achieves competitive performance on various SSL tasks.
This paper reviews hyper-parameter optimization methods for deep learning.
problem Designing and training deep neural networks is challenging and unpredictable.
method Reviews major optimization algorithms and services for hyper-parameter tuning.
result Comprehensive comparison of optimization algorithms and services.
P3O merges on-policy and off-policy updates without extra hyper-parameters.
problem Combining on-policy and off-policy RL algorithms to reduce sample complexity.
method Interleaves off-policy updates with on-policy updates using effective sample size.
result P3O reduces sample complexity of state-of-the-art algorithms.
GeneCAI optimizes DNN compression hyper-parameters for mobile devices.
problem Efficient deployment of complex DNNs on resource-limited devices.
method GeneCAI uses genetic algorithm to learn optimal hyper-parameters.
result GeneCAI finds models with better accuracy-complexity trade-off.
BOFFIN TTS optimizes hyper-parameters for new speaker adaptation.
problem Fine-tuning a pre-trained TTS model for a new speaker with limited data.
method Bayesian optimization to efficiently find optimal hyper-parameters.
result Average 30% improvement in speaker similarity over standard techniques.
Although deep learning has produced dazzling successes for applications of image, speech, and video processing in the past few years, most trainings are with suboptimal hyper-parameters, requiring unnecessarily long training times. Setting the hyper-parameters remains a black art that requires years of experience to ac…
Predicts neural network accuracy with minimal training, speeding up hyper-parameter tuning.
problem Expensive full training of neural networks during hyper-parameter tuning.
method Train network multiple times, store initial and final accuracies, use curve fitting and SVM for prediction.
result Predicted accuracies match known optimal settings with low computational cost.
A new Randomized-Hyperopt method improves XGBoost hyperparameter tuning.
problem Improving the performance of XGBoost through hyperparameter optimization.
method Proposes Randomized-Hyperopt for XGBoost hyperparameter tuning.
result Randomized-Hyperopt outperforms other methods in terms of accuracy and execution time.
Improves risk control in predictions using semi-supervised calibration.
problem Noisy hyper-parameter tuning from limited labeled data.
method Semi-supervised calibration using unlabeled data to tune hyper-parameters rigorously.
result Improves prediction accuracy without sacrificing statistical validity.
This paper introduces an efficient method for optimizing deep learning hyperparameters.
problem The high dependency of deep learning algorithms on hyper-parameters.
method Orthogonal Array Tuning Method (OATM) for deep learning hyper-parameter tuning.
result The proposed OATM method significantly saves tuning time compared to state-of-the-art methods.
Efficiently models learning curves using Gaussian processes with latent Kronecker structure.
problem Joint modeling of machine learning model performance across hyper-parameters and training progress.
method Imposes latent Kronecker structure to leverage efficient product kernels and handle missing values.
result Matches the performance of a Transformer on a learning curve prediction task.
Study shows how network width affects SGD hyper-parameters and generalization.
problem Understanding how network width impacts SGD hyper-parameters and generalization.
method Generated model families by increasing network width, performed hyper-parameter search.
result Wider networks achieve higher test accuracy and optimal normalized noise scale.
New sampling method improves search efficiency in machine learning.
problem Efficiently sampling effective solutions from large search spaces.
method Developed a parameterized family of coverage-based designs and algorithms for effective synthesis.
result Consistently outperforms existing exploratory sampling methods in sample mining and hyper-parameter optimization.
A hybrid RL-Bayesian search configures machine learning pipelines efficiently.
problem Optimizing hyper-parameters in a hierarchical conditional space.
method Combines Reinforcement Learning and Bayesian Optimization.
result Outperforms state-of-the-art methods in pipeline optimization.
Tree-search algorithm optimizes noisy black-box functions with multi-fidelity queries.
problem Optimizing noisy functions with expensive evaluations and low-cost approximations.
method Tree-like hierarchical partitions and multi-fidelity bandit tree-search algorithm.
result Simple regret bounds for the proposed algorithm.