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Books and Theses
- 2020
[b1]Brian Brubach:
Markets, Elections, and Microbes: Data-driven Algorithms from Theory to Practice. University of Maryland, College Park, MD, USA, 2020
Journal Articles
- 2020
- [j3]Brian Brubach, Karthik Abinav Sankararaman
, Aravind Srinivasan, Pan Xu:
Attenuate Locally, Win Globally: Attenuation-Based Frameworks for Online Stochastic Matching with Timeouts. Algorithmica 82(1): 64-87 (2020) - [j2]Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Online Stochastic Matching: New Algorithms and Bounds. Algorithmica 82(10): 2737-2783 (2020) - [j1]Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Algorithms to Approximate Column-sparse Packing Problems. ACM Trans. Algorithms 16(1): 10:1-10:32 (2020)
Conference and Workshop Papers
- 2024
- [c20]Seyed A. Esmaeili, Darshan Chakrabarti, Hayley Grape, Brian Brubach:
Implications of Distance over Redistricting Maps: Central and Outlier Maps. AAAI 2024: 9679-9687 - 2022
- [c19]Brian Brubach, Audrey Ballarin, Heeba Nazeer:
Characterizing Properties and Trade-offs of Centralized Delegation Mechanisms in Liquid Democracy. FAccT 2022: 1629-1638 - [c18]Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson, Brian Brubach:
Fair Labeled Clustering. KDD 2022: 327-335 - 2021
- [c17]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas:
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints. AAAI 2021: 6822-6830 - [c16]Nathaniel Grammel, Brian Brubach, Will Ma, Aravind Srinivasan:
Follow Your Star: New Frameworks for Online Stochastic Matching with Known and Unknown Patience. AISTATS 2021: 2872-2880 - [c15]Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti:
Approximating Two-Stage Stochastic Supplier Problems. APPROX-RANDOM 2021: 23:1-23:22 - [c14]Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, Suresh Venkatasubramanian:
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. NeurIPS Datasets and Benchmarks 2021 - [c13]Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan, John Dickerson:
Fair Clustering Under a Bounded Cost. NeurIPS 2021: 14345-14357 - [c12]Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan:
Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes. NeurIPS 2021: 27184-27195 - 2020
- [c11]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas:
A Pairwise Fair and Community-preserving Approach to k-Center Clustering. ICML 2020: 1178-1189 - [c10]Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John Dickerson:
Probabilistic Fair Clustering. NeurIPS 2020 - [c9]Brian Brubach, Aravind Srinivasan, Shawn Zhao:
Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives. EC 2020: 815-833 - 2018
- [c8]Brian Brubach:
Fast Matching-based Approximations for Maximum Duo-Preservation String Mapping and its Weighted Variant. CPM 2018: 5:1-5:14 - [c7]Brian Brubach, Jay Ghurye:
A Succinct Four Russians Speedup for Edit Distance Computation and One-against-many Banded Alignment. CPM 2018: 13:1-13:12 - [c6]Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Algorithms to Approximate Column-Sparse Packing Problems. SODA 2018: 311-330 - 2017
- [c5]Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts. AAMAS 2017: 1223-1231 - [c4]Brian Brubach, Jay Ghurye, Mihai Pop, Aravind Srinivasan:
Better Greedy Sequence Clustering with Fast Banded Alignment. WABI 2017: 3:1-3:13 - 2016
- [c3]Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
New Algorithms, Better Bounds, and a Novel Model for Online Stochastic Matching. ESA 2016: 24:1-24:16 - [c2]Brian Brubach:
Further Improvement in Approximating the Maximum Duo-Preservation String Mapping Problem. WABI 2016: 52-64 - 2014
- [c1]Brian Brubach:
Improved Bound for Online Square-into-Square Packing. WAOA 2014: 47-58
Informal and Other Publications
- 2022
- [i16]Seyed A. Esmaeili, Hayley Grape, Brian Brubach:
Centralized Fairness for Redistricting. CoRR abs/2203.00872 (2022) - [i15]Seyed A. Esmaeili, Sharmila Duppala, John P. Dickerson, Brian Brubach:
Fair Labeled Clustering. CoRR abs/2205.14358 (2022) - [i14]Brian Brubach, Audrey Ballarin, Heeba Nazeer:
Characterizing Properties and Trade-offs of Centralized Delegation Mechanisms in Liquid Democracy. CoRR abs/2206.05339 (2022) - 2021
- [i13]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas:
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints. CoRR abs/2103.02013 (2021) - [i12]Michelle Bao, Angela Zhou, Samantha Zottola, Brian Brubach, Sarah Desmarais, Aaron Horowitz, Kristian Lum, Suresh Venkatasubramanian:
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks. CoRR abs/2106.05498 (2021) - [i11]Brian Brubach, Nathaniel Grammel, Will Ma, Aravind Srinivasan:
Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes. CoRR abs/2106.06892 (2021) - [i10]Seyed A. Esmaeili, Brian Brubach, Aravind Srinivasan, John P. Dickerson:
Fair Clustering Under a Bounded Cost. CoRR abs/2106.07239 (2021) - 2020
- [i9]Seyed A. Esmaeili, Brian Brubach, Leonidas Tsepenekas, John P. Dickerson:
Probabilistic Fair Clustering. CoRR abs/2006.10916 (2020) - [i8]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas:
A Pairwise Fair and Community-preserving Approach to k-Center Clustering. CoRR abs/2007.07384 (2020) - [i7]Brian Brubach, Nathaniel Grammel, David G. Harris, Aravind Srinivasan, Leonidas Tsepenekas, Anil Vullikanti:
Approximation Algorithms for Radius-Based, Two-Stage Stochastic Clustering Problems with Budget Constraints. CoRR abs/2008.03325 (2020) - [i6]Marek Adamczyk, Brian Brubach, Fabrizio Grandoni, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Improved Approximation Algorithms for Stochastic-Matching Problems. CoRR abs/2010.08142 (2020) - 2019
- [i5]Brian Brubach, Nathaniel Grammel, Aravind Srinivasan:
Vertex-weighted Online Stochastic Matching with Patience Constraints. CoRR abs/1907.03963 (2019) - 2018
- [i4]Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Attenuate Locally, Win Globally: An Attenuation-based Framework for Online Stochastic Matching with Timeouts. CoRR abs/1804.08062 (2018) - 2017
- [i3]Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
Algorithms to Approximate Column-Sparse Packing Problems. CoRR abs/1711.02724 (2017) - 2016
- [i2]Brian Brubach, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu:
New Algorithms, Better Bounds, and a Novel Model for Online Stochastic Matching. CoRR abs/1606.06395 (2016) - 2014
- [i1]Brian Brubach:
Improved Online Square-into-Square Packing. CoRR abs/1401.5583 (2014)
Coauthor Index
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