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ORCIDManish Raghavan
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2020 – today
- 2024
[j5]Manish Raghavan:
What Should We Do when Our Ideas of Fairness Conflict? Commun. ACM 67(1): 88-97 (2024)- [c23]Cynthia Dwork
, Chris Hays, Jon M. Kleinberg, Manish Raghavan:
Equilibria, Efficiency, and Inequality in Network Formation for Hiring and Opportunity. EC 2024: 347-371 - [c22]Cynthia Dwork, Chris Hays, Jon M. Kleinberg, Manish Raghavan:
Content Moderation and the Formation of Online Communities: A Theoretical Framework. WWW 2024: 1307-1317 - [c21]Kenny Peng, Manish Raghavan, Emma Pierson, Jon M. Kleinberg, Nikhil Garg:
Reconciling the Accuracy-Diversity Trade-off in Recommendations. WWW 2024: 1318-1329 - [i28]Rohan Alur, Manish Raghavan, Devavrat Shah:
Distinguishing the Indistinguishable: Human Expertise in Algorithmic Prediction. CoRR abs/2402.00793 (2024) - [i27]Cynthia Dwork, Chris Hays, Jon M. Kleinberg, Manish Raghavan:
Equilibria, Efficiency, and Inequality in Network Formation for Hiring and Opportunity. CoRR abs/2402.13841 (2024) - [i26]Cynthia Dwork, Kristjan H. Greenewald, Manish Raghavan:
Synthetic Census Data Generation via Multidimensional Multiset Sum. CoRR abs/2404.10095 (2024) - [i25]Vinith M. Suriyakumar, Rohan Alur, Ayush Sekhari, Manish Raghavan, Ashia C. Wilson:
Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models. CoRR abs/2410.08074 (2024) - [i24]Rohan Alur, Loren Laine, Darrick K. Li, Dennis L. Shung, Manish Raghavan, Devavrat Shah:
Integrating Expert Judgment and Algorithmic Decision Making: An Indistinguishability Framework. CoRR abs/2410.08783 (2024) - 2023
- [b2]Manish Raghavan:
The Societal Impacts of Algorithmic Decision-Making. ACM Books 53, ACM 2023, ISBN 979-8-4007-0861-9 - [j4]Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu:
Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits. SIAM J. Comput. 52(2): 487-524 (2023) - [c20]Rohan Alur, Loren Laine, Darrick K. Li, Manish Raghavan, Devavrat Shah, Dennis L. Shung:
Auditing for Human Expertise. NeurIPS 2023 - [c19]Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, Philipp Zimmer:
Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection. WWW 2023: 3660-3669 - [i23]Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, Philipp Zimmer:
Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection. CoRR abs/2301.07015 (2023) - [i22]Rohan Alur, Loren Laine, Darrick K. Li, Manish Raghavan, Devavrat Shah, Dennis L. Shung:
Auditing for Human Expertise. CoRR abs/2306.01646 (2023) - [i21]Kenny Peng, Manish Raghavan, Emma Pierson, Jon M. Kleinberg, Nikhil Garg:
Reconciling the accuracy-diversity trade-off in recommendations. CoRR abs/2307.15142 (2023) - [i20]Cynthia Dwork, Chris Hays, Jon M. Kleinberg, Manish Raghavan:
Content Moderation and the Formation of Online Communities: A Theoretical Framework. CoRR abs/2310.10573 (2023) - 2022
- [c18]Emily Black, Manish Raghavan, Solon Barocas:
Model Multiplicity: Opportunities, Concerns, and Solutions. FAccT 2022: 850-863 - [c17]Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan:
The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization. EC 2022: 29 - [i19]Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan:
The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization. CoRR abs/2202.11776 (2022) - [i18]Sarah H. Cen, Manish Raghavan:
The Right to be an Exception to a Data-Driven Rule. CoRR abs/2212.13995 (2022) - 2021
- [b1]Manish Raghavan:
The Societal Impacts of Algorithmic Decision-Making. Cornell University, USA, 2021 - [j3]Jon M. Kleinberg, Manish Raghavan:
Algorithmic monoculture and social welfare. Proc. Natl. Acad. Sci. USA 118(22): e2018340118 (2021) - [c16]Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis:
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness. FAccT 2021: 489-503 - [c15]Jon M. Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar:
Stochastic model for sunk cost bias. UAI 2021: 1279-1288 - [i17]Jon M. Kleinberg, Manish Raghavan:
Algorithmic Monoculture and Social Welfare. CoRR abs/2101.05853 (2021) - [i16]Chloé Bakalar, Renata Barreto, Stevie Bergman, Miranda Bogen, Bobbie Chern, Sam Corbett-Davies, Melissa Hall, Isabel Kloumann, Michelle Lam, Joaquin Quiñonero Candela, Manish Raghavan, Joshua Simons, Jonathan Tannen, Edmund Tong, Kate Vredenburgh, Jiejing Zhao:
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems. CoRR abs/2103.06172 (2021) - [i15]Jon M. Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar:
Stochastic Model for Sunk Cost Bias. CoRR abs/2106.11003 (2021) - 2020
- [j2]Jon M. Kleinberg, Manish Raghavan:
Algorithmic classification and strategic effort. SIGecom Exch. 18(2): 45-52 (2020) - [j1]Jon M. Kleinberg, Manish Raghavan:
How Do Classifiers Induce Agents to Invest Effort Strategically? ACM Trans. Economics and Comput. 8(4): 19:1-19:23 (2020) - [c14]Jon M. Kleinberg, Manish Raghavan:
Designing Evaluation Rules That Are Robust to Strategic Behavior. AAAI 2020: 13673-13676 - [c13]Solon Barocas, Andrew D. Selbst, Manish Raghavan:
The hidden assumptions behind counterfactual explanations and principal reasons. FAT* 2020: 80-89 - [c12]Rediet Abebe, Solon Barocas, Jon M. Kleinberg, Karen Levy, Manish Raghavan, David G. Robinson:
Roles for computing in social change. FAT* 2020: 252-260 - [c11]Manish Raghavan, Solon Barocas, Jon M. Kleinberg, Karen Levy:
Mitigating bias in algorithmic hiring: evaluating claims and practices. FAT* 2020: 469-481 - [i14]Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu:
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits. CoRR abs/2005.10624 (2020) - [i13]Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis:
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness. CoRR abs/2010.05434 (2020)
2010 – 2019
- 2019
- [c10]Jon M. Kleinberg, Manish Raghavan:
How Do Classifiers Induce Agents to Invest Effort Strategically? EC 2019: 825-844 - [c9]Manish Purohit, Sreenivas Gollapudi, Manish Raghavan:
Hiring Under Uncertainty. ICML 2019: 5181-5189 - [i12]Manish Raghavan, Manish Purohit, Sreenivas Gollapudi:
Hiring Under Uncertainty. CoRR abs/1905.02709 (2019) - [i11]Manish Raghavan, Solon Barocas, Jon M. Kleinberg, Karen Levy:
Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices. CoRR abs/1906.09208 (2019) - [i10]Rediet Abebe, Solon Barocas, Jon M. Kleinberg, Karen Levy, Manish Raghavan, David G. Robinson:
Roles for Computing in Social Change. CoRR abs/1912.04883 (2019) - [i9]Solon Barocas, Andrew D. Selbst, Manish Raghavan:
The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons. CoRR abs/1912.04930 (2019) - 2018
- [c8]Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu:
The Externalities of Exploration and How Data Diversity Helps Exploitation. COLT 2018: 1724-1738 - [c7]Jon M. Kleinberg, Manish Raghavan:
Selection Problems in the Presence of Implicit Bias. ITCS 2018: 33:1-33:17 - [c6]Manish Raghavan, Ashton Anderson, Jon M. Kleinberg:
Mapping the Invocation Structure of Online Political Interaction. WWW 2018: 629-638 - [i8]Jon M. Kleinberg, Manish Raghavan:
Selection Problems in the Presence of Implicit Bias. CoRR abs/1801.03533 (2018) - [i7]Manish Raghavan, Ashton Anderson, Jon M. Kleinberg:
Mapping the Invocation Structure of Online Political Interaction. CoRR abs/1802.09597 (2018) - [i6]Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu:
The Externalities of Exploration and How Data Diversity Helps Exploitation. CoRR abs/1806.00543 (2018) - [i5]Jon M. Kleinberg, Manish Raghavan:
How Do Classifiers Induce Agents To Invest Effort Strategically? CoRR abs/1807.05307 (2018) - 2017
- [c5]Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan:
Inherent Trade-Offs in the Fair Determination of Risk Scores. ITCS 2017: 43:1-43:23 - [c4]Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger:
On Fairness and Calibration. NIPS 2017: 5680-5689 - [c3]Jon M. Kleinberg, Sigal Oren, Manish Raghavan:
Planning with Multiple Biases. EC 2017: 567-584 - [i4]Jon M. Kleinberg, Sigal Oren, Manish Raghavan:
Planning with Multiple Biases. CoRR abs/1706.01062 (2017) - [i3]Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger:
On Fairness and Calibration. CoRR abs/1709.02012 (2017) - 2016
- [c2]Jon M. Kleinberg, Sigal Oren, Manish Raghavan:
Planning Problems for Sophisticated Agents with Present Bias. EC 2016: 343-360 - [i2]Jon M. Kleinberg, Sigal Oren, Manish Raghavan:
Planning Problems for Sophisticated Agents with Present Bias. CoRR abs/1603.08177 (2016) - [i1]Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan:
Inherent Trade-Offs in the Fair Determination of Risk Scores. CoRR abs/1609.05807 (2016) - 2014
- [c1]Nilesh N. Dalvi, Marian Olteanu, Manish Raghavan, Philip Bohannon:
Deduplicating a places database. WWW 2014: 409-418
Coauthor Index
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