Statistical Learning
EfficientRisk-AverseReinforcementLearning
A risk measure often focuses on the worst returns out of the agent'sexperience.
AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling
Bichuan Guo, Yuxing Han, Jiangtao Wen
Neural Information Processing Systems http://nips.cc/
ScalableRule-Based RepresentationLearningfor InterpretableClassification
Ensemble methodsand fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability.
Certified Machine Unlearning via Noisy Stochastic Gradient Descent
"The right to be forgotten" ensured by laws for user data privacy becomes increasingly important.
Two
We show that the proposed algorithms converge to the (regularized) global optimal solution, andmoreover,theirratesofconvergence areofpolynomial orderinthe online setting and exponential order inthe finite sample setting, respectively.
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems
Stochastic Proximal Gradient (SPG) methods have been widely used for solving optimization problems with a simple (possibly non-smooth) regularizer in machine learning and statistics.
