Jamie Morgenstern | DeepAI

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Jie Zhang
215 publications
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160 publications
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90 publications
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66 publications
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60 publications
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55 publications
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50 publications
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41 publications
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∙ 07/07/2023

Scalable Membership Inference Attacks via Quantile Regression

Membership inference attacks are designed to determine, using black box ...

0 Martin Bertran, et al. ∙

∙ 05/31/2023

Doubly Constrained Fair Clustering

The remarkable attention which fair clustering has received in the last ...

0 John Dickerson, et al. ∙

∙ 09/15/2022

Multicalibrated Regression for Downstream Fairness

We show how to take a regression function f̂ that is appropriately “mult...

6 Ira Globus-Harris, et al. ∙

∙ 07/07/2022

Individual Preference Stability for Clustering

In this paper, we propose a natural notion of individual preference (IP)...

6 Saba Ahmadi, et al. ∙

∙ 06/22/2022

Active Learning with Safety Constraints

Active learning methods have shown great promise in reducing the number ...

0 Romain Camilleri, et al. ∙

∙ 06/06/2022

Multi-learner risk reduction under endogenous participation dynamics

Prediction systems face exogenous and endogenous distribution shift – th...

0 Sarah Dean, et al. ∙

∙ 05/25/2022

Preference Dynamics Under Personalized Recommendations

Many projects (both practical and academic) have designed algorithms to ...

0 Sarah Dean, et al. ∙

∙ 02/11/2022

Distributionally Robust Data Join

Suppose we are given two datasets: a labeled dataset and unlabeled datas...

0 Pranjal Awasthi, et al. ∙

∙ 02/04/2022

Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets

Online ad platforms offer budget management tools for advertisers that a...

0 Bhuvesh Kumar, et al. ∙

∙ 08/27/2021

Auctions and Prediction Markets for Scientific Peer Review

Peer reviewed publications are considered the gold standard in certifyin...

0 Siddarth Srinivasan, et al. ∙

∙ 02/16/2021

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Training and evaluation of fair classifiers is a challenging problem. Th...

16 Pranjal Awasthi, et al. ∙

∙ 09/29/2020

Competition Alleviates Present Bias in Task Completion

We build upon recent work [Kleinberg and Oren, 2014, Kleinberg et al., 2...

0 Aditya Saraf, et al. ∙

∙ 06/11/2020

Adaptive Sampling to Reduce Disparate Performance

Existing methods for reducing disparate performance of a classifier acro...

1 Jacob Abernethy, et al. ∙

∙ 06/08/2020

A Notion of Individual Fairness for Clustering

A common distinction in fair machine learning, in particular in fair cla...

0 Matthäus Kleindessner, et al. ∙

∙ 02/09/2020

Diversity and Inclusion Metrics in Subset Selection

The ethical concept of fairness has recently been applied in machine lea...

23 Margaret Mitchell, et al. ∙

∙ 06/07/2019

Effectiveness of Equalized Odds for Fair Classification under Imperfect Group Information

Most approaches for ensuring or improving a model's fairness with respec...

0 Pranjal Awasthi, et al. ∙

∙ 06/01/2019

Network Formation under Random Attack and Probabilistic Spread

We study a network formation game where agents receive benefits by formi...

0 Yu Chen, et al. ∙

∙ 04/10/2019

FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning

The growing capability and accessibility of machine learning has led to ...

0 Ángel Alexander Cabrera, et al. ∙

∙ 02/28/2019

Fair Dimensionality Reduction and Iterative Rounding for SDPs

We model "fair" dimensionality reduction as an optimization problem. A c...

0 Jamie Morgenstern, et al. ∙

∙ 02/21/2019

Predictive Inequity in Object Detection

In this work, we investigate whether state-of-the-art object detection s...

0 Benjamin Wilson, et al. ∙

∙ 01/24/2019

Guarantees for Spectral Clustering with Fairness Constraints

Given the widespread popularity of spectral clustering (SC) for partitio...

0 Matthäus Kleindessner, et al. ∙

∙ 01/24/2019

Fair k-Center Clustering for Data Summarization

In data summarization we want to choose k prototypes in order to summari...

0 Matthäus Kleindessner, et al. ∙

∙ 10/31/2018

The Price of Fair PCA: One Extra Dimension

We investigate whether the standard dimensionality reduction technique o...

0 Samira Samadi, et al. ∙

∙ 03/23/2018

Datasheets for Datasets

Currently there is no standard way to identify how a dataset was created...

0 Timnit Gebru, et al. ∙

∙ 01/10/2018

A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem

Bandit learning is characterized by the tension between long-term explor...

0 Sampath Kannan, et al. ∙

∙ 06/07/2017

A Convex Framework for Fair Regression

We introduce a flexible family of fairness regularizers for (linear and ...

0 Richard Berk, et al. ∙

∙ 05/23/2016

Fairness in Learning: Classic and Contextual Bandits

We introduce the study of fairness in multi-armed bandit problems. Our f...

0 Matthew Joseph, et al. ∙

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