34 citations · 68 across the 17 of their papers we have counts for
17 papers
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
Characterizing Properties and Trade-offs of Centralized Delegation Mechanisms in Liquid Democracy
Brian Brubach, Audrey Ballarin, Heeba Nazeer
Liquid democracy is a form of transitive delegative democracy that has received a flurry of scholarly attention from the computer science community in recent years. In its simplest…
Fair Labeled Clustering
Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson +1
Numerous algorithms have been produced for the fundamental problem of clustering under many different notions of fairness. Perhaps the most common family of notions currently studi…
Implications of Distance over Redistricting Maps: Central and Outlier Maps
Seyed A. Esmaeili, Darshan Chakrabarti, Hayley Grape +1
In representative democracy, a redistricting map is chosen to partition an electorate into districts which each elects a representative. A valid redistricting map must satisfy a co…
Fair Clustering Under a Bounded Cost
Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan +1
Clustering is a fundamental unsupervised learning problem where a dataset is partitioned into clusters that consist of nearby points in a metric space. A recent variant, fair clust…
Improved Guarantees for Offline Stochastic Matching via New Ordered Contention Resolution Schemes
Brian Brubach, Nathaniel Grammel, Will Ma +2
Matching is one of the most fundamental and broadly applicable problems across many domains. In these diverse real-world applications, there is often a degree of uncertainty in the…
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
Michelle Bao, Angela Zhou, Samantha Zottola +5
Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS dataset, are commonly used in algorithmic fairness papers due to benchmarking practices of comparing alg…
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Brian Brubach, Darshan Chakrabarti, John P. Dickerson +2
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research. However, in a variety of situations…
Improved Approximation Algorithms for Stochastic-Matching Problems
Marek Adamczyk, Brian Brubach, Fabrizio Grandoni +3
We consider the Stochastic Matching problem, which is motivated by applications in kidney exchange and online dating. In this problem, we are given an undirected graph. Each edge i…
Stochastic Optimization and Learning for Two-Stage Supplier Problems
Brian Brubach, Nathaniel Grammel, David G. Harris +3
The main focus of this paper is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the for…
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Brian Brubach, Darshan Chakrabarti, John P. Dickerson +3
Clustering is a foundational problem in machine learning with numerous applications. As machine learning increases in ubiquity as a backend for automated systems, concerns about fa…
Probabilistic Fair Clustering
Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas +1
In clustering problems, a central decision-maker is given a complete metric graph over vertices and must provide a clustering of vertices that minimizes some objective function. In…
Online Matching Frameworks under Stochastic Rewards, Product Ranking, and Unknown Patience
Brian Brubach, Nathaniel Grammel, Will Ma +1
We study generalizations of online bipartite matching in which each arriving vertex (customer) views a ranked list of offline vertices (products) and matches to (purchases) the fir…
Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts
Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan +1
Online matching problems have garnered significant attention in recent years due to numerous applications in e-commerce, online advertisements, ride-sharing, etc. Many of them capt…
Algorithms to Approximate Column-Sparse Packing Problems
Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan +1
Column-sparse packing problems arise in several contexts in both deterministic and stochastic discrete optimization. We present two unifying ideas, (non-uniform) attenuation and mu…
Online Stochastic Matching: New Algorithms and Bounds
Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan +1
Online matching has received significant attention over the last 15 years due to its close connection to Internet advertising. As the seminal work of Karp, Vazirani, and Vazirani h…
Improved Online Square-into-Square Packing
Brian Brubach
In this paper, we show an improved bound and new algorithm for the online square-into-square packing problem. This two-dimensional packing problem involves packing an online sequen…