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Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
google-research/google-research
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NeurIPS 2020
We call our technique Protagonist Antagonist Induced Regret Environment Design (PAIRED).
Distribution-Free Predictive Inference For Regression
In the spirit of reproducibility, all of our empirical results can also be easily (re)generated using this package.
Generalized Random Forests
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations.
ICDAR2017 Competition on Reading Chinese Text in the Wild (RCTW-17)
This report introduces RCTW, a new competition that focuses on Chinese text reading.
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
The resulting geo-FNO model has both the computation efficiency of FFT and the flexibility of handling arbitrary geometries.
Kernels for Vector-Valued Functions: a Review
Kernel methods are among the most popular techniques in machine learning.
High-Dimensional Metrics in R
The package High-dimensional Metrics (\Rpackage{hdm}) is an evolving collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models.
Double/Debiased Machine Learning for Treatment and Causal Parameters
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
Differentiable Compositional Kernel Learning for Gaussian Processes
The NKN architecture is based on the composition rules for kernels, so that each unit of the network corresponds to a valid kernel.
Testing Conditional Independence in Supervised Learning Algorithms
We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a reduced feature set.
