Optimization
Universal Boosting Variational Inference
Neural Information Processing Systems http://nips.cc/
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine
Neural Information Processing Systems http://nips.cc/
iMAML algorithm perform better than MAML
We thank the reviewers for the thoughtful feedback! Reviewer #1: Thank you for the thoughtful questions! We do not require convexity of L anywhere. Furthermore, regularity conditions are often needed for analysis but not to run the algorithm. Similarly, iMAML shows promising empirical results.
Coresets for Near-Convex Functions
Empirical risk minimization (ERM) may be used to generalize the result from train to test data.
Bayesian Optimization with Exponential Convergence
Kenji Kawaguchi, Leslie Pack Kaelbling, Tomás Lozano-Pérez
This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and without the δ -cover sampling. Most Bayesian optimization methods require auxiliary optimization: an additional non-convex global optimization problem, which can be time-consuming and hard to implement in practice. Also, the existing Bayesian optimization method with exponential convergence [ 1] requires access to the δ -cover sampling, which was considered to be impractical [ 1, 2]. Our approach eliminates both requirements and achieves an exponential convergence rate.
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Aaron Klein, Zhenwen Dai, Frank Hutter, Neil Lawrence, Javier Gonzalez
Neural Information Processing Systems http://nips.cc/