'Empirical Design in Reinforcement Learning', by Andrew Patterson, Samuel Neumann, Martha White, Adam White.

http://jmlr.org/papers/v25/23-0183.html

#reinforcement #experiments #hyperparameters

#CausalML update - I am now fitting my first #CausalForest on real data!

Does anyone have advice on the most important #hyperparameters (After the # of trees & tree depth.)

I'm working on large imbalanced data sets and a large number of treatment variables, so it's not like anything you see in the economics literature. 🤔 #ML #AI #causal

'On the Hyperparameters in Stochastic Gradient Descent with Momentum', by Bin Shi.

http://jmlr.org/papers/v25/22-1189.html

#sgd #hyperparameters #stochastic

'Pre-trained Gaussian Processes for Bayesian Optimization', by Zi Wang et al.

http://jmlr.org/papers/v25/23-0269.html

#priors #prior #hyperparameters

'An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters', by Julie Keisler, El-Ghazali Talbi, Sandra Claudel, Gilles Cabriel.

http://jmlr.org/papers/v25/23-0166.html

#forecasting #algorithmic #hyperparameters

'Low-rank Variational Bayes correction to the Laplace method', by Janet van Niekerk, Haavard Rue.

http://jmlr.org/papers/v25/21-1405.html

#variational #hyperparameters #approximations

'Beyond the Golden Ratio for Variational Inequality Algorithms', by Ahmet Alacaoglu, Axel Böhm, Yura Malitsky.

http://jmlr.org/papers/v24/22-1488.html

#ascent #constrained #hyperparameters