b'Yuekai Sun'

research

∙ 07/05/2023

Conditional independence testing under model misspecification

Conditional independence (CI) testing is fundamental and challenging in ...

0 Felipe Maia Polo, et al. ∙

research

∙ 05/01/2023

ISAAC Newton: Input-based Approximate Curvature for Newton's Method

We present ISAAC (Input-baSed ApproximAte Curvature), a novel method tha...

0 Felix Petersen, et al. ∙

research

∙ 04/25/2023

On Uniform Consistency of Spectral Embeddings

In this paper, we study the convergence of the spectral embeddings obtai...

0 Ruofei Zhao, et al. ∙

research

∙ 02/20/2023

Simple Disentanglement of Style and Content in Visual Representations

Learning visual representations with interpretable features, i.e., disen...

0 Lilian Ngweta, et al. ∙

research

∙ 01/15/2023

Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees

We consider the task of training machine learning models with data-depen...

0 Songkai Xue, et al. ∙

research

∙ 06/07/2022

How does overparametrization affect performance on minority groups?

The benefits of overparameterization for the overall performance of mode...

0 Subha Maity, et al. ∙

research

∙ 05/26/2022

Understanding new tasks through the lens of training data via exponential tilting

Deploying machine learning models to new tasks is a major challenge desp...

0 Subha Maity, et al. ∙

research

∙ 05/26/2022

Predictor-corrector algorithms for stochastic optimization under gradual distribution shift

Time-varying stochastic optimization problems frequently arise in machin...

0 Subha Maity, et al. ∙

research

∙ 05/01/2022

Domain Adaptation meets Individual Fairness. And they get along

Many instances of algorithmic bias are caused by distributional shifts. ...

0 Debarghya Mukherjee, et al. ∙

research

∙ 04/13/2022

Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms

Sampling biases in training data are a major source of algorithmic biase...

0 Laura Niss, et al. ∙

research

∙ 11/21/2021

A linear adjustment based approach to posterior drift in transfer learning

We present a new model and methods for the posterior drift problem where...

0 Subha Maity, et al. ∙

research

∙ 10/26/2021

On sensitivity of meta-learning to support data

Meta-learning algorithms are widely used for few-shot learning. For exam...

10 Mayank Agarwal, et al. ∙

research

∙ 10/26/2021

Post-processing for Individual Fairness

Post-processing in algorithmic fairness is a versatile approach for corr...

0 Felix Petersen, et al. ∙

research

∙ 03/31/2021

Individually Fair Gradient Boosting

We consider the task of enforcing individual fairness in gradient boosti...

0 Alexander Vargo, et al. ∙

research

∙ 03/30/2021

Statistical inference for individual fairness

As we rely on machine learning (ML) models to make more consequential de...

0 Subha Maity, et al. ∙

research

∙ 03/19/2021

Individually Fair Ranking

We develop an algorithm to train individually fair learning-to-rank (LTR...

0 Amanda Bower, et al. ∙

research

∙ 12/14/2020

Outlier-Robust Optimal Transport

Optimal transport (OT) provides a way of measuring distances between dis...

0 Debarghya Mukherjee, et al. ∙

research

∙ 12/03/2020

Matrix Completion Methods for the Total Electron Content Video Reconstruction

The total electron content (TEC) maps can be used to estimate the signal...

0 Hu Sun, et al. ∙

research

∙ 11/06/2020

There is no trade-off: enforcing fairness can improve accuracy

One of the main barriers to the broader adoption of algorithmic fairness...

0 Subha Maity, et al. ∙

research

∙ 06/25/2020

SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness

In this paper, we cast fair machine learning as invariant machine learni...

0 Mikhail Yurochkin, et al. ∙

research

∙ 06/19/2020

Two Simple Ways to Learn Individual Fairness Metrics from Data

Individual fairness is an intuitive definition of algorithmic fairness t...

0 Debarghya Mukherjee, et al. ∙

research

∙ 03/23/2020

Minimax optimal approaches to the label shift problem

We study minimax rates of convergence in the label shift problem. In add...

0 Subha Maity, et al. ∙

research

∙ 03/11/2020

Auditing ML Models for Individual Bias and Unfairness

We consider the task of auditing ML models for individual bias/unfairnes...

0 Songkai Xue, et al. ∙

research

∙ 02/15/2020

Federated Learning with Matched Averaging

Federated learning allows edge devices to collaboratively learn a shared...

0 Hongyi Wang, et al. ∙

research

∙ 12/26/2019

Communication-Efficient Integrative Regression in High-Dimensions

We consider the task of meta-analysis in high-dimensional settings in wh...

0 Subha Maity, et al. ∙

research

∙ 06/28/2019

Learning fair predictors with Sensitive Subspace Robustness

We consider an approach to training machine learning systems that are fa...

5 Mikhail Yurochkin, et al. ∙

research

∙ 05/27/2019

Dirichlet Simplex Nest and Geometric Inference

We propose Dirichlet Simplex Nest, a class of probabilistic models suita...

0 Mikhail Yurochkin, et al. ∙

research

∙ 05/20/2019

Uniform bounds for invariant subspace perturbations

For a fixed matrix A and perturbation E we develop purely deterministic ...

0 Anil Damle, et al. ∙

research

∙ 04/07/2019

Precision Matrix Estimation with Noisy and Missing Data

Estimating conditional dependence graphs and precision matrices are some...

0 Roger Fan, et al. ∙

research

∙ 10/09/2018

Statistical Convergence of the EM Algorithm on Gaussian Mixture Models

We study the convergence behavior of the Expectation Maximization (EM) a...

0 Ruofei Zhao, et al. ∙

research

∙ 07/02/2018

Debiasing representations by removing unwanted variation due to protected attributes

We propose a regression-based approach to removing implicit biases in re...

0 Amanda Bower, et al. ∙

research

∙ 03/21/2018

Information Theoretic Interpretation of Deep learning

We interpret part of the experimental results of Shwartz-Ziv and Tishby ...

0 Tianchen Zhao, et al. ∙

research

∙ 08/28/2017

An inexact subsampled proximal Newton-type method for large-scale machine learning

We propose a fast proximal Newton-type algorithm for minimizing regulari...

0 Xuanqing Liu, et al. ∙

research

∙ 06/26/2017

On conditional parity as a notion of non-discrimination in machine learning

We identify conditional parity as a general notion of non-discrimination...

0 Ya'acov Ritov, et al. ∙

research

∙ 03/14/2015

Communication-efficient sparse regression: a one-shot approach

We devise a one-shot approach to distributed sparse regression in the hi...

0 Jason D. Lee, et al. ∙

research

∙ 11/25/2013

Exact post-selection inference, with application to the lasso

We develop a general approach to valid inference after model selection. ...

0 Jason D. Lee, et al. ∙

research

∙ 11/11/2013

Learning Mixtures of Linear Classifiers

We consider a discriminative learning (regression) problem, whereby the ...

0 Yuekai Sun, et al. ∙

research

∙ 05/31/2013

On model selection consistency of regularized M-estimators

Regularized M-estimators are used in diverse areas of science and engine...

0 Jason D. Lee, et al. ∙

Yuekai Sun

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