Optimization
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.
Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras
Neural Information Processing Systems http://nips.cc/
Pareto Multi-Task Learning
Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, Sam Kwong
Theproposed algorithm first formulates a multi-task learning problem as a multiobjective optimization problem, and then decomposes the multiobjective optimization problem into a set of constrained subproblems with different trade-off preferences.
Maximum Causal Tsallis Entropy Imitation Learning
Kyungjae Lee, Sungjoon Choi, Songhwai Oh
Neural Information Processing Systems http://nips.cc/
Riemannian stochastic optimization methods avoid strict saddle points Anonymous Author(s) Affiliation Address email
Riemannian setting is a highly challenging affair.
