Statistical Learning
Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning Zhenhuan Yang 1 Y unwen Lei 2 Puyu Wang 3 Tianbao Yang
Pairwise learning refers to learning tasks where the loss function depends on a pair of instances.
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.
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 (
Hessian Eigenspectra of More Realistic Nonlinear Models
The Hessian is ubiquitous in applied mathematics, statistics, and machine learning (ML).
Laplace Redux - Effortless Bayesian Deep Learning
Despite their successes, modern neural networks (NNs) still suffer from several shortcomings that limit their applicability in some settings.
Precise expressions for random projections: Low-rank approximation and randomized Newton
' means that while the surrogate expression is not exact, it approximates the true quantity up
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models
Modern machine learning models often contain more parameters than the number of training samples.