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Found 5 papers, 4 papers with code
Generalizing Gaussian Smoothing for Random Search
Gaussian smoothing (GS) is a derivative-free optimization (DFO) algorithm that estimates the gradient of an objective using perturbations of the current parameters sampled from a standard normal distribution.
Modeling and Optimization Trade-off in Meta-learning
By searching for shared inductive biases across tasks, meta-learning promises to accelerate learning on novel tasks, but with the cost of solving a complex bilevel optimization problem.
Assessing Generalization in Deep Reinforcement Learning
Our aim is to catalyze community-wide progress on generalization in deep RL.
Out-of-Distribution Generalization
reinforcement-learning
+1
Confidence Intervals for Algorithmic Leveraging in Linear Regression
The age of big data has produced data sets that are computationally expensive to analyze and store.
Efficient moment calculations for variance components in large unbalanced crossed random effects models
Large crossed data sets, described by generalized linear mixed models, have become increasingly common and provide challenges for statistical analysis.
Methodology Computation
