3 research outputs found
OPT-GAN: Black-Box Global Optimization via Generative Adversarial Nets
Black-box optimization (BBO) algorithms are concerned with finding the best
solutions for problems with missing analytical details. Most classical methods
for such problems are based on strong and fixed a priori assumptions, such as
Gaussianity. However, the complex real-world problems, especially when the
global optimum is desired, could be very far from the a priori assumptions
because of their diversities, causing unexpected obstacles to these methods. In
this study, we propose a generative adversarial net-based broad-spectrum global
optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with
strategies to balance exploration-exploitation trade-off. It has potential to
better adapt to the regularity and structure of diversified landscapes than
other methods with fixed prior, e.g. Gaussian assumption or separability.
Experiments conducted on BBO benchmarking problems and several other benchmarks
with diversified landscapes exhibit that OPT-GAN outperforms other traditional
and neural net-based BBO algorithms.Comment: M. Lu and S. Ning contribute equally. Submitted to IEEE transactions
on Neural Networks and Learning System
Benchmarking GNN-CMA-ES on the BBOB noiseless testbed
International audiencePopular machine learning estimators involve regularization parameters that can be challenging to tune, and standard strategies rely on grid search for this task. In this paper, we revisit the techniques of approximating the regularization path up to predefined tolerance in a unified framework and show that its complexity is for uniformly convex loss of order and for Generalized Self-Concordant functions. This framework encompasses least-squares but also logistic regression, a case that as far as we know was not handled as precisely in previous works. We leverage our technique to provide refined bounds on the validation error as well as a practical algorithm for hyperparameter tuning. The latter has global convergence guarantee when targeting a prescribed accuracy on the validation set. Last but not least, our approach helps relieving the practitioner from the (often neglected) task of selecting a stopping criterion when optimizing over the training set: our method automatically calibrates this criterion based on the targeted accuracy on the validation set