Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Fast global convergence of natural policy gradient methods with entropy regularization
Natural policy gradient (NPG) methods are among the most widely used policy optimization
algorithms in contemporary reinforcement learning. This class of methods is often applied in …
algorithms in contemporary reinforcement learning. This class of methods is often applied in …
Neural policy gradient methods: Global optimality and rates of convergence
Distributed learning in the nonconvex world: From batch data to streaming and beyond
Distributed learning has become a critical enabler of the massively connected world that
many people envision. This article discusses four key elements of scalable distributed …
many people envision. This article discusses four key elements of scalable distributed …
On the bias-variance-cost tradeoff of stochastic optimization
We consider stochastic optimization when one only has access to biased stochastic oracles
of the objective, and obtaining stochastic gradients with low biases comes at high costs. This …
of the objective, and obtaining stochastic gradients with low biases comes at high costs. This …
Biased stochastic first-order methods for conditional stochastic optimization and applications in meta learning
Conditional stochastic optimization covers a variety of applications ranging from invariant
learning and causal inference to meta-learning. However, constructing unbiased gradient …
learning and causal inference to meta-learning. However, constructing unbiased gradient …
Non-asymptotic convergence analysis of two time-scale (natural) actor-critic algorithms
As an important type of reinforcement learning algorithms, actor-critic (AC) and natural actor-
critic (NAC) algorithms are often executed in two ways for finding optimal policies. In the first …
critic (NAC) algorithms are often executed in two ways for finding optimal policies. In the first …
Multi-agent performative prediction with greedy deployment and consensus seeking agents
We consider a scenario where multiple agents are learning a common decision vector from
data which can be influenced by the agents' decisions. This leads to the problem of multi …
data which can be influenced by the agents' decisions. This leads to the problem of multi …