
Individually Fair Gradient Boosting
We consider the task of enforcing individual fairness in gradient boosti...
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Statistical inference for individual fairness
As we rely on machine learning (ML) models to make more consequential de...
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Individually Fair Ranking
We develop an algorithm to train individually fair learningtorank (LTR...
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OutlierRobust Optimal Transport
Optimal transport (OT) provides a way of measuring distances between dis...
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Matrix Completion Methods for the Total Electron Content Video Reconstruction
The total electron content (TEC) maps can be used to estimate the signal...
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There is no tradeoff: enforcing fairness can improve accuracy
One of the main barriers to the broader adoption of algorithmic fairness...
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SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness
In this paper, we cast fair machine learning as invariant machine learni...
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Two Simple Ways to Learn Individual Fairness Metrics from Data
Individual fairness is an intuitive definition of algorithmic fairness t...
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Minimax optimal approaches to the label shift problem
We study minimax rates of convergence in the label shift problem. In add...
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Auditing ML Models for Individual Bias and Unfairness
We consider the task of auditing ML models for individual bias/unfairnes...
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Federated Learning with Matched Averaging
Federated learning allows edge devices to collaboratively learn a shared...
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CommunicationEfficient Integrative Regression in HighDimensions
We consider the task of metaanalysis in highdimensional settings in wh...
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Learning fair predictors with Sensitive Subspace Robustness
We consider an approach to training machine learning systems that are fa...
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Dirichlet Simplex Nest and Geometric Inference
We propose Dirichlet Simplex Nest, a class of probabilistic models suita...
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Uniform bounds for invariant subspace perturbations
For a fixed matrix A and perturbation E we develop purely deterministic ...
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Precision Matrix Estimation with Noisy and Missing Data
Estimating conditional dependence graphs and precision matrices are some...
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Statistical Convergence of the EM Algorithm on Gaussian Mixture Models
We study the convergence behavior of the Expectation Maximization (EM) a...
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Debiasing representations by removing unwanted variation due to protected attributes
We propose a regressionbased approach to removing implicit biases in re...
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Information Theoretic Interpretation of Deep learning
We interpret part of the experimental results of ShwartzZiv and Tishby ...
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An inexact subsampled proximal Newtontype method for largescale machine learning
We propose a fast proximal Newtontype algorithm for minimizing regulari...
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On conditional parity as a notion of nondiscrimination in machine learning
We identify conditional parity as a general notion of nondiscrimination...
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Communicationefficient sparse regression: a oneshot approach
We devise a oneshot approach to distributed sparse regression in the hi...
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Exact postselection inference, with application to the lasso
We develop a general approach to valid inference after model selection. ...
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Learning Mixtures of Linear Classifiers
We consider a discriminative learning (regression) problem, whereby the ...
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On model selection consistency of regularized Mestimators
Regularized Mestimators are used in diverse areas of science and engine...
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Yuekai Sun
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