111 citations · 147 across the 6 of their papers we have counts for
6 papers
Generalizing Gaussian Smoothing for Random Search
Katelyn Gao, Ozan Sener
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 s…
Modeling and Optimization Trade-off in Meta-learning
Katelyn Gao, Ozan Sener
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 pro…
Assessing Generalization in Deep Reinforcement Learning
Charles Packer, Katelyn Gao, Jernej Kos +3
Deep reinforcement learning (RL) has achieved breakthrough results on many tasks, but agents often fail to generalize beyond the environment they were trained in. As a result, deep…
Estimation and Inference for Very Large Linear Mixed Effects Models
K. Gao, A. B. Owen
Linear mixed models with large imbalanced crossed random effects structures pose severe computational problems for maximum likelihood estimation and for Bayesian analysis. The cost…
Confidence Intervals for Algorithmic Leveraging in Linear Regression
Katelyn Gao
The age of big data has produced data sets that are computationally expensive to analyze and store. Algorithmic leveraging proposes that we sample observations from the original da…
Efficient moment calculations for variance components in large unbalanced crossed random effects models
Katelyn Gao, Art B. Owen
Large crossed data sets, described by generalized linear mixed models, have become increasingly common and provide challenges for statistical analysis. At very large sizes it becom…