Statistical Learning
Regularized Gradient Descent Ascentfor Two-Player Zero-Sum Markov Games
Under >0, ( )is 2 SB, and ( )isunique 2 SA.
LearningImbalancedDatasetswith Label-Distribution-AwareMarginLoss
First, we propose a theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound.
Generalizing to Unseen Domains via Adversarial Data Augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
Neural Information Processing Systems http://nips.cc/
SolvingInterpretableKernelDimensionReduction
Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable ofcapturing nonlinear relationships.
dececdcbf0ea0162234a8fb4ab051415-Supplemental-Conference.pdf
Thus,ฮณ(ฯ) (0,1] for ฯ (0,1], which meets the algorithm design requirement. Algorithm 2 actually performs the gradient descent scheme on the function หfti(x) = Eu B[fti(x+ฯตu)] restricted to the convex set(1 ฮถ)K.
