194 citations · 998 across the 36 of their papers we have counts for
42 papers
Fair Active Learning in Low-Data Regimes
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern +2
In critical machine learning applications, ensuring fairness is essential to avoid perpetuating social inequities. In this work, we address the challenges of reducing bias and impr…
Scalable Membership Inference Attacks via Quantile Regression
Martin Bertran, Shuai Tang, Michael Kearns +3
Membership inference attacks are designed to determine, using black box access to trained models, whether a particular example was used in training or not. Membership inference can…
Doubly Constrained Fair Clustering
John Dickerson, Seyed A. Esmaeili, Jamie Morgenstern +1
The remarkable attention which fair clustering has received in the last few years has resulted in a significant number of different notions of fairness. Despite the fact that these…
Multicalibrated Regression for Downstream Fairness
Ira Globus-Harris, Varun Gupta, Christopher Jung +3
We show how to take a regression function that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier sati…
Individual Preference Stability for Clustering
Saba Ahmadi, Pranjal Awasthi, Samir Khuller +4
In this paper, we propose a natural notion of individual preference (IP) stability for clustering, which asks that every data point, on average, is closer to the points in its own…
Active Learning with Safety Constraints
Romain Camilleri, Andrew Wagenmaker, Jamie Morgenstern +2
Active learning methods have shown great promise in reducing the number of samples necessary for learning. As automated learning systems are adopted into real-time, real-world deci…
Emergent specialization from participation dynamics and multi-learner retraining
Sarah Dean, Mihaela Curmei, Lillian J. Ratliff +2
Numerous online services are data-driven: the behavior of users affects the system's parameters, and the system's parameters affect the users' experience of the service, which in t…
Preference Dynamics Under Personalized Recommendations
Sarah Dean, Jamie Morgenstern
Many projects (both practical and academic) have designed algorithms to match users to content they will enjoy under the assumption that user's preferences and opinions do not chan…
Optimal Spend Rate Estimation and Pacing for Ad Campaigns with Budgets
Bhuvesh Kumar, Jamie Morgenstern, Okke Schrijvers
Online ad platforms offer budget management tools for advertisers that aim to maximize the number of conversions given a budget constraint. As the volume of impressions, conversion…
Distributionally Robust Data Join
Pranjal Awasthi, Christopher Jung, Jamie Morgenstern
Suppose we are given two datasets: a labeled dataset and unlabeled dataset which also has additional auxiliary features not present in the first dataset. What is the most principle…
Auctions and Peer Prediction for Academic Peer Review
Siddarth Srinivasan, Jamie Morgenstern
Peer reviewed publications are considered the gold standard in certifying and disseminating ideas that a research community considers valuable. However, we identify two major drawb…
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information
Pranjal Awasthi, Alex Beutel, Matthaeus Kleindessner +2
Training and evaluation of fair classifiers is a challenging problem. This is partly due to the fact that most fairness metrics of interest depend on both the sensitive attribute i…
Competition Alleviates Present Bias in Task Completion
Aditya Saraf, Anna R. Karlin, Jamie Morgenstern
We build upon recent work [Kleinberg and Oren, 2014, Kleinberg et al., 2016, 2017] that considers present biased agents, who place more weight on costs they must incur now than cos…
A Notion of Individual Fairness for Clustering
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
A common distinction in fair machine learning, in particular in fair classification, is between group fairness and individual fairness. In the context of clustering, group fairness…
Active Sampling for Min-Max Fairness
Jacob Abernethy, Pranjal Awasthi, Matthäus Kleindessner +3
We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimiza…
Diversity and Inclusion Metrics in Subset Selection
Margaret Mitchell, Dylan Baker, Nyalleng Moorosi +5
The ethical concept of fairness has recently been applied in machine learning (ML) settings to describe a wide range of constraints and objectives. When considering the relevance o…
Equalized odds postprocessing under imperfect group information
Pranjal Awasthi, Matthäus Kleindessner, Jamie Morgenstern
Most approaches aiming to ensure a model's fairness with respect to a protected attribute (such as gender or race) assume to know the true value of the attribute for every data poi…
Network Formation under Random Attack and Probabilistic Spread
Yu Chen, Shahin Jabbari, Michael Kearns +2
We study a network formation game where agents receive benefits by forming connections to other agents but also incur both direct and indirect costs from the formed connections. Sp…
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
Ángel Alexander Cabrera, Will Epperson, Fred Hohman +3
The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems m…
Predictive Inequity in Object Detection
Benjamin Wilson, Judy Hoffman, Jamie Morgenstern
In this work, we investigate whether state-of-the-art object detection systems have equitable predictive performance on pedestrians with different skin tones. This work is motivate…
Multi-Criteria Dimensionality Reduction with Applications to Fairness
Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh +2
Dimensionality reduction is a classical technique widely used for data analysis. One foundational instantiation is Principal Component Analysis (PCA), which minimizes the average r…
Guarantees for Spectral Clustering with Fairness Constraints
Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi +1
Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion pro…
Fair k-Center Clustering for Data Summarization
Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern
In data summarization we want to choose prototypes in order to summarize a data set. We study a setting where the data set comprises several demographic groups and we are restr…
The Price of Fair PCA: One Extra Dimension
Samira Samadi, Uthaipon Tantipongpipat, Jamie Morgenstern +2
We investigate whether the standard dimensionality reduction technique of PCA inadvertently produces data representations with different fidelity for two different populations. We…