Statistical Learning
Accelerated Primal-Dual Gradient Method for Smooth
Problem (1) has a large number of application, some of which we now briefly introduce.
Accelerated Primal-Dual Gradient Method for Smooth
Problem (1) has a large number of application, some of which we now briefly introduce.
On sensitivity of meta-learning to support data
Meta-learning algorithms are widely used for few-shot learning.
Multi-fidelity Monte Carlo: a pseudo-marginal approach
A key challenge in applying MCMC to scientific domains is computation: the target density of interest is often a function of expensive computations, such as a high-fidelity physical simulation, an intractable integral, or a slowly-converging iterative algorithm.
EnsembleSampling_Final
Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In this paper, we establish a regret bound that ensures desirable behavior when ensemble sampling is applied to the linear bandit problem.
