Improved Bound for Online Square-into-Square Packing. - Brian Brubach (WAOA, 2014)

Improved Online Square-into-Square Packing. - Brian Brubach (CoRR, 2014)

New Algorithms, Better Bounds, and a Novel Model for Online Stochastic Matching. - Brian Brubach; Karthik Abinav Sankararaman; Aravind Srinivasan; Pan Xu 001 (ESA, 2016)

Further Improvement in Approximating the Maximum Duo-Preservation String Mapping Problem. - Brian Brubach (WABI, 2016)

Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts. - Brian Brubach; Karthik Abinav Sankararaman; Aravind Srinivasan; Pan Xu 001 (AAMAS, 2017)

Better Greedy Sequence Clustering with Fast Banded Alignment. - Brian Brubach; Jay Ghurye; Mihai Pop; Aravind Srinivasan (WABI, 2017)

Fast Matching-based Approximations for Maximum Duo-Preservation String Mapping and its Weighted Variant. - Brian Brubach (CPM, 2018)

A Succinct Four Russians Speedup for Edit Distance Computation and One-against-many Banded Alignment. - Brian Brubach; Jay Ghurye (CPM, 2018)

Vertex-weighted Online Stochastic Matching with Patience Constraints. - Brian Brubach; Nathaniel Grammel; Aravind Srinivasan (CoRR, 2019)

A Pairwise Fair and Community-preserving Approach to k-Center Clustering. - Brian Brubach; Darshan Chakrabarti; John P. Dickerson; Samir Khuller; Aravind Srinivasan; Leonidas Tsepenekas (ICML, 2020)

Probabilistic Fair Clustering. - Seyed A. Esmaeili; Brian Brubach; Leonidas Tsepenekas; John Dickerson 001 (NeurIPS, 2020)

Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives. - Brian Brubach; Aravind Srinivasan; Shawn Zhao (EC, 2020)

Attenuate Locally, Win Globally: Attenuation-Based Frameworks for Online Stochastic Matching with Timeouts. - Brian Brubach; Karthik Abinav Sankararaman; Aravind Srinivasan; Pan Xu 001 (Algorithmica, 2020)

Online Stochastic Matching: New Algorithms and Bounds. - Brian Brubach; Karthik Abinav Sankararaman; Aravind Srinivasan; Pan Xu 001 (Algorithmica, 2020)

Approximation Algorithms for Radius-Based, Two-Stage Stochastic Clustering Problems with Budget Constraints. - Brian Brubach; Nathaniel Grammel; David G. Harris 001; Aravind Srinivasan; Leonidas Tsepenekas; Anil Vullikanti (CoRR, 2020)

Improved Approximation Algorithms for Stochastic-Matching Problems. - Marek Adamczyk; Brian Brubach; Fabrizio Grandoni 001; Karthik Abinav Sankararaman; Aravind Srinivasan; Pan Xu 001 (CoRR, 2020)

Algorithms to Approximate Column-sparse Packing Problems. - Brian Brubach; Karthik Abinav Sankararaman; Aravind Srinivasan; Pan Xu 001 (ACM Trans. Algorithms, 2020)

Markets, Elections, and Microbes: Data-driven Algorithms from Theory to Practice. - Brian Brubach (2020)

Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints. - Brian Brubach; Darshan Chakrabarti; John P. Dickerson; Aravind Srinivasan; Leonidas Tsepenekas (AAAI, 2021)

Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience. - Nathaniel Grammel; Brian Brubach; Will Ma; Aravind Srinivasan (AISTATS, 2021)

Approximating Two-Stage Stochastic Supplier Problems. - Brian Brubach; Nathaniel Grammel; David G. Harris 001; Aravind Srinivasan; Leonidas Tsepenekas; Anil Vullikanti (APPROX-RANDOM, 2021)

It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. - Michelle Bao; Angela Zhou; Samantha Zottola; Brian Brubach; Sarah Desmarais; Aaron Horowitz; Kristian Lum; Suresh Venkatasubramanian (NeurIPS Datasets and Benchmarks, 2021)

Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes. - Brian Brubach; Nathaniel Grammel; Will Ma; Aravind Srinivasan (NeurIPS, 2021)

Fair Clustering Under a Bounded Cost. - Seyed A. Esmaeili; Brian Brubach; Aravind Srinivasan; John Dickerson 001 (NeurIPS, 2021)

Characterizing Properties and Trade-offs of Centralized Delegation Mechanisms in Liquid Democracy. - Brian Brubach; Audrey Ballarin; Heeba Nazeer (FAccT, 2022)

Fair Labeled Clustering. - Seyed A. Esmaeili; Sharmila Duppala; John P. Dickerson; Brian Brubach (KDD, 2022)

Centralized Fairness for Redistricting. - Seyed A. Esmaeili; Hayley Grape; Brian Brubach (CoRR, 2022)

Implications of Distance over Redistricting Maps: Central and Outlier Maps. - Seyed A. Esmaeili; Darshan Chakrabarti; Hayley Grape; Brian Brubach (AAAI, 2024)

Online Matching Frameworks Under Stochastic Rewards, Product Ranking, and Unknown Patience. - Brian Brubach; Nathaniel Grammel; Will Ma; Aravind Srinivasan (Oper. Res., 2025)