Automated hyperparameter tuning aspires to facilitate the application of machine learning for non-experts. In the literature, different optimization approaches are applied for that purpose. This paper investigates the performance of Differential Evolution for tuning hyperparameters of supervised learning algorithms for…
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18 results for “SMAC”
Managers of US National Forests must decide what policy to apply for dealing with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber companies, home owners, and wildlife biologists) have often led to spirited political debates and even violent eco-terrorism. One way to transform these conflicts into…
SMAC method optimizes tree-boosting hyperparameters best.
problem Optimizing hyperparameters for tree-boosting to improve model accuracy.
method Compared and evaluated various hyperparameter optimization methods.
result SMAC method outperforms other methods for hyperparameter tuning.
In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent learning, in which teams of agents must learn to coordinate their behaviour while conditioning only…
QTRAN++ improves MARL performance in complex environments.
problem Poor empirical performance of QTRAN in complex environments.
method Stabilizing training objective, removing role separation, and introducing a multi-head mixing network.
result QTRAN++ achieves state-of-the-art performance in the Starcraft Multi-Agent Challenge (SMAC).
Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approachcs.CV
A registration-free framework monitors shape and color in 4D point clouds.
problem Monitoring shape and color changes in complex parts without registration.
method Laplace-Beltrami operator spectral properties for geometric and color feature capture; combined monitoring scheme for shape and color anomalies.
result Effective detection of shape deformations and color anomalies without registration or mesh reconstruction.
QR-MIX models joint state-action values as a distribution to handle randomness in MARL.
problem Randomness in rewards and observations leads to randomness in long-term returns in MARL.
method QR-MIX uses quantile regression and combines it with QMIX and IQN to model joint state-action values as a distribution.
result QR-MIX outperforms QMIX in the StarCraft Multi-Agent Challenge (SMAC) environment.
QMIX combines per-agent values to create decentralised policies.
problem Training decentralised policies from centralised learning.
method QMIX uses a mixing network to estimate joint action-values as a monotonic combination of per-agent values.
result QMIX significantly outperforms existing methods on the StarCraft Multi-Agent Challenge (SMAC).
Automl is the key technology for machine learning problem. Current state of art hyperparameter optimization methods are based on traditional black-box optimization methods like SMBO (SMAC, TPE). The objective function of black-box optimization is non-smooth, or time-consuming to evaluate, or in some way noisy. Recent y…
This study improves hyperparameter optimization for categorical and non-normal data.
problem Bayesian hyperparameter optimization struggles with categorical hyperparameters and non-normal data.
method Integrates conformalized quantile regression to address estimation weaknesses and provides robust calibration guarantees.
result Quantile surrogate architectures and acquisition functions yield superior performance compared to existing methods.
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model. It is designed for both single- and multi…
Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communication constraints during execution and computational tractability in training. In this paper, we analyse value-based methods that are known to have superior performance in com…
Improved Bayesian optimization for conditional parameter spaces.
problem Efficient global optimization of expensive-to-evaluate functions in conditional parameter spaces.
method Additive tree-structured covariance function for conditional parameter optimization.
result Significantly improved sample-efficiency and wider applicability compared to existing methods.
POCAII optimizes hyperparameters with a new approach, showing superior performance.
problem Hyperparameter optimization with limited resources.
method Explicitly separates search and evaluation phases, focusing on exploration and exploitation.
result POCAII outperforms state-of-the-art HPO algorithms in low-budget scenarios.
Novel covariance function improves Bayesian optimization efficiency.
problem Efficient global optimization of expensive black-box functions.
method Additive tree-structured covariance function and parallel optimization algorithm.
result Significantly outperforms state-of-the-art methods in conditional parameter optimization.
A new Bayesian optimization method using Poisson process for better noise robustness.
problem Estimating relative rankings of candidates in noisy environments.
method Poisson process-based ranking surrogate model and tailored acquisition functions.
result PoPBO framework shows lower computation costs and better robustness to noise compared to GP-BO.
Efficient Hyperparameter Optimization of Deep Learning Algorithms Using Deterministic RBF Surrogatescs.AI
Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has been proposed for optimizing hyperparameters of various machine learning algorithms. Those methods adopt probabilistic surrogate models like G…
Improved exploration in RL with latent state marginalization.
problem Complexity of deep probabilistic models limits their practical use in reinforcement learning.
method Adopting latent variable policies within the MaxEnt framework, with low-cost marginalization of latent states.
result Effective marginalization leads to better exploration and more robust training.