350 papers with code • 0 benchmarks • 11 datasets

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Greatest papers with code

Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals

tensorflow/tensorflow 11 Sep 2018

This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem.


Fairness in Streaming Submodular Maximization: Algorithms and Hardness

google-research/google-research NeurIPS 2020

Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data.


Fairness without Demographics through Adversarially Reweighted Learning

google-research/google-research NeurIPS 2020

Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns.


Fair Correlation Clustering

google-research/google-research 6 Feb 2020

We define a fairlet decomposition with cost similar to the $k$-median cost and this allows us to obtain approximation algorithms for a wide range of fairness constraints.

Combinatorial Optimization Fairness

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.


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.


What Do Compressed Deep Neural Networks Forget?

google-research/google-research 13 Nov 2019

However, this measure of performance conceals significant differences in how different classes and images are impacted by model compression techniques.

Fairness Interpretability Techniques for Deep Learning +4

Pairwise Fairness for Ranking and Regression

google-research/google-research 12 Jun 2019

We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity.

Fairness General Classification

Fair Bayesian Optimization

awslabs/autogluon 9 Jun 2020

Moreover, our method can be used in synergy with such specialized fairness techniques to tune their hyperparameters.


Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

jacobgil/pytorch-grad-cam 3 Oct 2019

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.

Adversarial Attack Decision Making +1