Statistical Learning
Decentralized Accelerated Proximal Gradient Descent Haishan Y e 1 Ziang Zhou 1 Luo
In this paper, we study the decentralized composite optimization problem with a non-smooth regularization term.
X-CAL: Explicit Calibration for Survival Analysis Mark Goldstein
When a model's predicted number of events within any time interval is similar to the observed number, it is called well-calibrated . A survival model's calibration can be measured using, for instance, distributional calibration (
Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis
The high-level question in this work is: If we learn a task using a sufficiently deep NN, how can we uncover the underlying hierarchical organization of sub-functions in that task?
ParallelandEfficientHierarchicalk-Median Clustering
Inparticular,standardmetricformulations as hierarchical k-center,k-means, andk-median received a lot of attention and the problems have been studied extensively in different models of computation.