Optimization
BILCO: AnEfficientAlgorithmforJointAlignmentof TimeSeries
We develop the strategy of linear component operations thatintegrates dynamic programming techniqueandthepush-relabel approach.
A Appendix: Proofs and Algorithms A.1 Proofs of results in Section 4 Proof of Proposition 4.1. Plug B
(Bertsekas, 1999). Algorithm 1. Furthermore, we call ˆ f (), X We can show that | f () ˆ f () |, 8 2 [, ] . Besides, computing the upper bound claimed in Proposition 4.2 requires finding The second equality is from the fact that the objective function is affine w.r.t. Finally, we verify the rest two components. Finally, we verify the rest two components. This finishes the proof of our claim.
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin
Neural Information Processing Systems http://nips.cc/
Constrained Cross-Entropy Method for Safe Reinforcement Learning
Neural Information Processing Systems http://nips.cc/
Distributional Policy Optimization: An Alternative Approach for Continuous Control
Chen Tessler, Guy Tennenholtz, Shie Mannor
Wesuggestanoveldistributional framework, able to represent arbitrary distribution functions over the continuous actionspace.
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cedric Archambeau, Rodolphe Jenatton
Neural Information Processing Systems http://nips.cc/
Scalable Global Optimization via Local Bayesian Optimization
David Eriksson, Michael Pearce, Jacob Gardner, Ryan D. Turner, Matthias Poloczek
To overcome these challenges, we adopt alocal strategy for BO.