Technology
ASPiRe: AdaptiveSkillPriorsforReinforcementLearning
Transferring prior experience to new tasks is central to an agent's adaptability. In this work, we aim to accelerate online reinforcement learning by leveraging prior experience from large offline data.
Gradient-FreeMethodsforNonconvexNonsmooth StochasticCompositionalOptimization
Most of the existing work [2, 5, 6, 7, 8] for nonconvex SCO problem is based on the assumption that both functionsf()andg()are smooth.
Algorithmic Linearly Constrained Gaussian Processes
Neural Information Processing Systems http://nips.cc/
DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization
Neural Information Processing Systems http://nips.cc/
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
Jun Yang, Shengyang Sun, Daniel M. Roy
The developments of Rademacher complexity and PAC-Bayesian theory have been largely independent.
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher Reale, Rebecca Russell, Louis Kim, peter chin
Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections.
