#bayesianoptimization — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #bayesianoptimization, aggregated by home.social.
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🚀 Our latest benchmark shows hyperparameter tuning with Optuna hits 0.9617 validation accuracy in just 64.59 seconds! Using Bayesian optimization and the Tree‑structured Parzen Estimator, we ran 100 trials to squeeze out every percent. Dive into the details of the experiment and see how you can apply these tricks to your own models. #HyperparameterTuning #Optuna #BayesianOptimization #ModelOptimization
🔗 https://aidailypost.com/news/hyperparameter-tuning-reaches-09617-accuracy-6459-seconds
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Professor Jin Xu (East China Normal University) will give a talk on 17 December on Bayesian Optimization via Exact Penalty. 🎓📊
🕒 14:15–15:00
📍 TU Dortmund, M/E 21EPBO shows how complex equality and resource constraints can be solved more efficiently – relevant for research, students, and anyone using data-driven methods. 🔍✨
What interests you most about optimization?
#Statistics #BayesianOptimization #Research #UARuhr #TUDortmund #DataScience
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Professor Jin Xu (East China Normal University) will give a talk on 17 December on Bayesian Optimization via Exact Penalty. 🎓📊
🕒 14:15–15:00
📍 TU Dortmund, M/E 21EPBO shows how complex equality and resource constraints can be solved more efficiently – relevant for research, students, and anyone using data-driven methods. 🔍✨
What interests you most about optimization?
#Statistics #BayesianOptimization #Research #UARuhr #TUDortmund #DataScience
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Meta releases Ax 1.0 for automated machine learning optimization: Meta launches Ax 1.0, an open-source platform using Bayesian optimization to automate complex experimentation across AI development, infrastructure tuning, and hardware design. https://ppc.land/meta-releases-ax-1-0-for-automated-machine-learning-optimization/ #Meta #MachineLearning #ArtificialIntelligence #BayesianOptimization #OpenSource
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Meta releases Ax 1.0 for automated machine learning optimization: Meta launches Ax 1.0, an open-source platform using Bayesian optimization to automate complex experimentation across AI development, infrastructure tuning, and hardware design. https://ppc.land/meta-releases-ax-1-0-for-automated-machine-learning-optimization/ #Meta #MachineLearning #ArtificialIntelligence #BayesianOptimization #OpenSource
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Meta releases Ax 1.0 for automated machine learning optimization: Meta launches Ax 1.0, an open-source platform using Bayesian optimization to automate complex experimentation across AI development, infrastructure tuning, and hardware design. https://ppc.land/meta-releases-ax-1-0-for-automated-machine-learning-optimization/ #Meta #MachineLearning #ArtificialIntelligence #BayesianOptimization #OpenSource
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Meta releases Ax 1.0 for automated machine learning optimization: Meta launches Ax 1.0, an open-source platform using Bayesian optimization to automate complex experimentation across AI development, infrastructure tuning, and hardware design. https://ppc.land/meta-releases-ax-1-0-for-automated-machine-learning-optimization/ #Meta #MachineLearning #ArtificialIntelligence #BayesianOptimization #OpenSource
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Meta releases Ax 1.0 for automated machine learning optimization: Meta launches Ax 1.0, an open-source platform using Bayesian optimization to automate complex experimentation across AI development, infrastructure tuning, and hardware design. https://ppc.land/meta-releases-ax-1-0-for-automated-machine-learning-optimization/ #Meta #MachineLearning #ArtificialIntelligence #BayesianOptimization #OpenSource
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"Model-based Causal Bayesian Optimization"
https://arxiv.org/abs/2211.10257#BayesianOptimization #Bayes #Optimization #MachineLearning #CausalInference
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"Model-based Causal Bayesian Optimization"
https://arxiv.org/abs/2211.10257#BayesianOptimization #Bayes #Optimization #MachineLearning #CausalInference
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"Model-based Causal Bayesian Optimization"
https://arxiv.org/abs/2211.10257#BayesianOptimization #Bayes #Optimization #MachineLearning #CausalInference
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"Model-based Causal Bayesian Optimization"
https://arxiv.org/abs/2211.10257#BayesianOptimization #Bayes #Optimization #MachineLearning #CausalInference
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"Model-based Causal Bayesian Optimization"
https://arxiv.org/abs/2211.10257#BayesianOptimization #Bayes #Optimization #MachineLearning #CausalInference
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Meta-Learning Priors for Safe Bayesian Optimization
📜 https://arxiv.org/abs/2210.00762
❓ Task: Query-efficient Bayesian Optimization subject to safety constraints.
💡 Idea: Set prior kernel hyper-params based on stds and calibration frequencies observed on related data. Search by exploiting monotonicity to efficiently prune unsafe and safe but sub-optimal solutions.
📈 Result: >2x convergence speedup for tuning the controller of a high-speed wafer inspection robot.
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Meta-Learning Priors for Safe Bayesian Optimization
📜 https://arxiv.org/abs/2210.00762
❓ Task: Query-efficient Bayesian Optimization subject to safety constraints.
💡 Idea: Set prior kernel hyper-params based on stds and calibration frequencies observed on related data. Search by exploiting monotonicity to efficiently prune unsafe and safe but sub-optimal solutions.
📈 Result: >2x convergence speedup for tuning the controller of a high-speed wafer inspection robot.
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Meta-Learning Priors for Safe Bayesian Optimization
📜 https://arxiv.org/abs/2210.00762
❓ Task: Query-efficient Bayesian Optimization subject to safety constraints.
💡 Idea: Set prior kernel hyper-params based on stds and calibration frequencies observed on related data. Search by exploiting monotonicity to efficiently prune unsafe and safe but sub-optimal solutions.
📈 Result: >2x convergence speedup for tuning the controller of a high-speed wafer inspection robot.
-
Meta-Learning Priors for Safe Bayesian Optimization
📜 https://arxiv.org/abs/2210.00762
❓ Task: Query-efficient Bayesian Optimization subject to safety constraints.
💡 Idea: Set prior kernel hyper-params based on stds and calibration frequencies observed on related data. Search by exploiting monotonicity to efficiently prune unsafe and safe but sub-optimal solutions.
📈 Result: >2x convergence speedup for tuning the controller of a high-speed wafer inspection robot.
-
Meta-Learning Priors for Safe Bayesian Optimization
📜 https://arxiv.org/abs/2210.00762
❓ Task: Query-efficient Bayesian Optimization subject to safety constraints.
💡 Idea: Set prior kernel hyper-params based on stds and calibration frequencies observed on related data. Search by exploiting monotonicity to efficiently prune unsafe and safe but sub-optimal solutions.
📈 Result: >2x convergence speedup for tuning the controller of a high-speed wafer inspection robot.