Statistical Learning
Boundary Matters: A Bi-Level Active Finetuning Method Han Lu
The pretraining-finetuning paradigm has gained widespread adoption in vision tasks and other fields. However, the finetuning phase still requires high-quality annotated samples.
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
Arindam Banerjee, Qilong Gu, Vidyashankar Sivakumar, Steven Z. Wu
Neural Information Processing Systems http://nips.cc/
AdaptingtoOnlineLabelShiftwith ProvableGuarantees YongBai1, Yu-JieZhang2,1, PengZhao1, MasashiSugiyama3,2, Zhi-HuaZhou1
Thestandard supervised learning paradigm workseffectivelywhentraining data shares the same distribution as the upcoming testing samples.
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization
Adithya M Devraj, Jianshu Chen
We exploit the richstructures ofthereformulated problem anddevelopastochastic primal-dual algorithm, SVRPDA-I, to solve the problem efficiently.
Optimizing Data Collection for Machine Learning
Optimizing Data Collection for Machine Learning