Mitigating Bias in Calibration Error Estimation

google-research/google-research • • 15 Dec 2020

We find that binning-based estimators with bins of equal mass (number of instances) have lower bias than estimators with bins of equal width.

Similarity of Neural Network Representations Revisited

google-research/google-research • • ICML 2019 2019

We introduce a similarity index that measures the relationship between representational similarity matrices and does not suffer from this limitation.

Behavior Regularized Offline Reinforcement Learning

google-research/google-research • • 26 Nov 2019

In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment.

Continuous Control Offline RL +2

Can weight sharing outperform random architecture search? An investigation with TuNAS

google-research/google-research • • CVPR 2020

Efficient Neural Architecture Search methods based on weight sharing have shown good promise in democratizing Neural Architecture Search for computer vision models.

Image Classification Neural Architecture Search

On Making Stochastic Classifiers Deterministic

google-research/google-research • • NeurIPS 2019

Stochastic classifiers arise in a number of machine learning problems, and have become especially prominent of late, as they often result from constrained optimization problems, e. g. for fairness, churn, or custom losses.

Fairness

Likelihood Ratios for Out-of-Distribution Detection

google-research/google-research • • NeurIPS 2019

We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Practical and Consistent Estimation of f-Divergences

google-research/google-research • • NeurIPS 2019

The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning.

BIG-bench Machine Learning Mutual Information Estimation +1

Differentiable Ranking and Sorting using Optimal Transport

google-research/google-research • • NeurIPS 2019

From this observation, we propose extended rank and sort operators by considering optimal transport (OT) problems (the natural relaxation for assignments) where the auxiliary measure can be any weighted measure supported on $m$ increasing values, where $m \ne n$.

A Benchmark for Interpretability Methods in Deep Neural Networks

google-research/google-research • • NeurIPS 2019

We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks.

Feature Importance Image Classification

Optimizing Generalized Rate Metrics with Three Players

google-research/google-research • • NeurIPS 2019

We present a general framework for solving a large class of learning problems with non-linear functions of classification rates.

Fairness