🚀 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
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
Myself and Yoshua Bengio are hiring a postdoctoral researcher @Mila_Quebec
! For this call, we are prioritizing candidates with experience in reinforcement learning, scientific discovery, or high-impact applications of ML. Apply here (https://docs.google.com/forms/d/e/1FAIpQLScqXiMClkgDBvrIZyxdtx60Pcbj3JzZeC-LFg3yiUOZlvgyLw/viewform?usp=sf_link)
We are looking for someone with skills in the areas of
#Reinforcementlearning
#ML4science
#Bayesianoptimization
#Foundationalmodels for #decisionmaking
#RealworldML
"Model-based Causal Bayesian Optimization"
https://arxiv.org/abs/2211.10257
#BayesianOptimization #Bayes #Optimization #MachineLearning #CausalInference
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.
