Manish Raghavan

73 papers A* 16A 3Journal 43Unranked 9

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Impacts of Aggregation on Model Diversity and Consumer Utility. CoRR Kate Donahue, Manish Raghavan
2026	J	`jnl`	Strategic Candidacy in Generative AI Arenas. CoRR Chris Hays, Rachel Li, Bailey Flanigan, Manish Raghavan
2026	J	`jnl`	The Subjectivity of Monoculture. CoRR Nathanael Jo, Nikhil Garg, Manish Raghavan
2025	A*	`conf`	Double Machine Learning for Causal Inference under Shared-State Interference. ICML Chris Hays, Manish Raghavan
2025	J	`jnl`	Double Machine Learning for Causal Inference under Shared-State Interference. CoRR Chris Hays, Manish Raghavan
2025	J	`jnl`	Evaluating multiple models using labeled and unlabeled data. CoRR Divya Shanmugam, Shuvom Sadhuka, Manish Raghavan, John V. Guttag, Bonnie Berger, Emma Pierson
2025	—	`conf`	Evaluating the Impacts of Swapping on the US Decennial Census. CSLAW Maria Ballesteros, Cynthia Dwork, Gary King, Conlan Olson, Manish Raghavan
2025	J	`jnl`	Evaluating the Impacts of Swapping on the US Decennial Census. CoRR Maria Ballesteros, Cynthia Dwork, Gary King, Conlan Olson, Manish Raghavan
2025	J	`jnl`	Homogeneous Algorithms Can Reduce Competition in Personalized Pricing. CoRR Nathanael Jo, Kathleen Creel, Ashia Wilson, Manish Raghavan
2025	J	`jnl`	Statistical Guarantees in the Search for Less Discriminatory Algorithms. CoRR Chris Hays, Benjamin Laufer, Solon Barocas, Manish Raghavan
2025	J	`jnl`	The Impossibility of Inverse Permutation Learning in Transformer Models. CoRR Rohan Alur, Chris Hays, Manish Raghavan, Devavrat Shah
2025	—	`conf`	What Constitutes a Less Discriminatory Algorithm? CSLAW Benjamin Laufer, Manish Raghavan, Solon Barocas
2025	—	`conf`	When to Ask a Question: Understanding Communication Strategies in Generative AI Tools. UMAP (Adjunct Publication) Charlotte Park, Kate Donahue, Manish Raghavan
2024	J	`jnl`	Competition and Diversity in Generative AI. CoRR Manish Raghavan
2024	A*	`conf`	Content Moderation and the Formation of Online Communities: A Theoretical Framework. WWW Cynthia Dwork, Chris Hays, Jon M. Kleinberg, Manish Raghavan
2024	J	`jnl`	Distinguishing the Indistinguishable: Human Expertise in Algorithmic Prediction. CoRR Rohan Alur, Manish Raghavan, Devavrat Shah
2024	A*	`conf`	Equilibria, Efficiency, and Inequality in Network Formation for Hiring and Opportunity. EC Cynthia Dwork, Chris Hays, Jon M. Kleinberg, Manish Raghavan
2024	J	`jnl`	Equilibria, Efficiency, and Inequality in Network Formation for Hiring and Opportunity. CoRR Cynthia Dwork, Chris Hays, Jon M. Kleinberg, Manish Raghavan
2024	A*	`conf`	Human Expertise in Algorithmic Prediction. NeurIPS Rohan Alur, Manish Raghavan, Devavrat Shah
2024	J	`jnl`	Integrating Expert Judgment and Algorithmic Decision Making: An Indistinguishability Framework. CoRR Rohan Alur, Loren Laine, Darrick K. Li, Dennis L. Shung, Manish Raghavan, Devavrat Shah
2024	A*	`conf`	Reconciling the Accuracy-Diversity Trade-off in Recommendations. WWW Kenny Peng, Manish Raghavan, Emma Pierson, Jon M. Kleinberg, Nikhil Garg
2024	J	`jnl`	Synthetic Census Data Generation via Multidimensional Multiset Sum. CoRR Cynthia Dwork, Kristjan H. Greenewald, Manish Raghavan
2024	J	`jnl`	Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models. CoRR Vinith M. Suriyakumar, Rohan Alur, Ayush Sekhari, Manish Raghavan, Ashia C. Wilson
2024	J	`jnl`	What Should We Do when Our Ideas of Fairness Conflict? Commun. ACM Manish Raghavan
2023	A*	`conf`	Auditing for Human Expertise. NeurIPS Rohan Alur, Loren Laine, Darrick K. Li, Manish Raghavan, Devavrat Shah, Dennis L. Shung
2023	J	`jnl`	Auditing for Human Expertise. CoRR Rohan Alur, Loren Laine, Darrick K. Li, Manish Raghavan, Devavrat Shah, Dennis L. Shung
2023	J	`jnl`	Content Moderation and the Formation of Online Communities: A Theoretical Framework. CoRR Cynthia Dwork, Chris Hays, Jon M. Kleinberg, Manish Raghavan
2023	J	`jnl`	Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits. SIAM J. Comput. Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
2023	J	`jnl`	Reconciling the accuracy-diversity trade-off in recommendations. CoRR Kenny Peng, Manish Raghavan, Emma Pierson, Jon M. Kleinberg, Nikhil Garg
2023	A*	`conf`	Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection. WWW Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, Philipp Zimmer
2023	J	`jnl`	Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection. CoRR Chris Hays, Zachary Schutzman, Manish Raghavan, Erin Walk, Philipp Zimmer
2023	—	`book`	The Societal Impacts of Algorithmic Decision-Making Manish Raghavan
2022	—	`conf`	Model Multiplicity: Opportunities, Concerns, and Solutions. FAccT Emily Black, Manish Raghavan, Solon Barocas
2022	A*	`conf`	The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization. EC Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan
2022	J	`jnl`	The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization. CoRR Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan
2022	J	`jnl`	The Right to be an Exception to a Data-Driven Rule. CoRR Sarah H. Cen, Manish Raghavan
2021	J	`jnl`	Algorithmic Monoculture and Social Welfare. CoRR Jon M. Kleinberg, Manish Raghavan
2021	J	`jnl`	Algorithmic monoculture and social welfare. Proc. Natl. Acad. Sci. USA Jon M. Kleinberg, Manish Raghavan
2021	—	`conf`	Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness. FAccT Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis
2021	J	`jnl`	Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems. CoRR Chloé Bakalar, Renata Barreto, Stevie Bergman, Miranda Bogen, Bobbie Chern, Sam Corbett-Davies, Melissa Hall, Isabel Kloumann, Michelle S. Lam, Joaquin Quiñonero Candela, Manish Raghavan, Joshua Simons, Jonathan Tannen, Edmund Tong, Kate Vredenburgh, Jiejing Zhao
2021	J	`jnl`	Stochastic Model for Sunk Cost Bias. CoRR Jon M. Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar
2021	A	`conf`	Stochastic model for sunk cost bias. UAI Jon M. Kleinberg, Sigal Oren, Manish Raghavan, Nadav Sklar
2021	—	`—`	The Societal Impacts of Algorithmic Decision-Making. Manish Raghavan
2020	J	`jnl`	Algorithmic classification and strategic effort. SIGecom Exch. Jon M. Kleinberg, Manish Raghavan
2020	J	`jnl`	Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness. CoRR Jessie Finocchiaro, Roland Maio, Faidra Monachou, Gourab K. Patro, Manish Raghavan, Ana-Andreea Stoica, Stratis Tsirtsis
2020	A*	`conf`	Designing Evaluation Rules That Are Robust to Strategic Behavior. AAAI Jon M. Kleinberg, Manish Raghavan
2020	J	`jnl`	Greedy Algorithm almost Dominates in Smoothed Contextual Bandits. CoRR Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
2020	J	`jnl`	How Do Classifiers Induce Agents to Invest Effort Strategically? ACM Trans. Economics and Comput. Jon M. Kleinberg, Manish Raghavan
2020	—	`conf`	Mitigating bias in algorithmic hiring: evaluating claims and practices. FAT* Manish Raghavan, Solon Barocas, Jon M. Kleinberg, Karen Levy
2020	—	`conf`	Roles for computing in social change. FAT* Rediet Abebe, Solon Barocas, Jon M. Kleinberg, Karen Levy, Manish Raghavan, David G. Robinson
2020	—	`conf`	The hidden assumptions behind counterfactual explanations and principal reasons. FAT* Solon Barocas, Andrew D. Selbst, Manish Raghavan
2019	A*	`conf`	Hiring Under Uncertainty. ICML Manish Purohit, Sreenivas Gollapudi, Manish Raghavan
2019	J	`jnl`	Hiring Under Uncertainty. CoRR Manish Raghavan, Manish Purohit, Sreenivas Gollapudi
2019	A*	`conf`	How Do Classifiers Induce Agents to Invest Effort Strategically? EC Jon M. Kleinberg, Manish Raghavan
2019	J	`jnl`	Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices. CoRR Manish Raghavan, Solon Barocas, Jon M. Kleinberg, Karen Levy
2019	J	`jnl`	Roles for Computing in Social Change. CoRR Rediet Abebe, Solon Barocas, Jon M. Kleinberg, Karen Levy, Manish Raghavan, David G. Robinson
2019	J	`jnl`	The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons. CoRR Solon Barocas, Andrew D. Selbst, Manish Raghavan
2018	J	`jnl`	How Do Classifiers Induce Agents To Invest Effort Strategically? CoRR Jon M. Kleinberg, Manish Raghavan
2018	A*	`conf`	Mapping the Invocation Structure of Online Political Interaction. WWW Manish Raghavan, Ashton Anderson, Jon M. Kleinberg
2018	J	`jnl`	Mapping the Invocation Structure of Online Political Interaction. CoRR Manish Raghavan, Ashton Anderson, Jon M. Kleinberg
2018	A	`conf`	Selection Problems in the Presence of Implicit Bias. ITCS Jon M. Kleinberg, Manish Raghavan
2018	J	`jnl`	Selection Problems in the Presence of Implicit Bias. CoRR Jon M. Kleinberg, Manish Raghavan
2018	A*	`conf`	The Externalities of Exploration and How Data Diversity Helps Exploitation. COLT Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
2018	J	`jnl`	The Externalities of Exploration and How Data Diversity Helps Exploitation. CoRR Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
2017	A	`conf`	Inherent Trade-Offs in the Fair Determination of Risk Scores. ITCS Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan
2017	—	`conf`	On Fairness and Calibration. NIPS Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger
2017	J	`jnl`	On Fairness and Calibration. CoRR Geoff Pleiss, Manish Raghavan, Felix Wu, Jon M. Kleinberg, Kilian Q. Weinberger
2017	A*	`conf`	Planning with Multiple Biases. EC Jon M. Kleinberg, Sigal Oren, Manish Raghavan
2017	J	`jnl`	Planning with Multiple Biases. CoRR Jon M. Kleinberg, Sigal Oren, Manish Raghavan
2016	J	`jnl`	Inherent Trade-Offs in the Fair Determination of Risk Scores. CoRR Jon M. Kleinberg, Sendhil Mullainathan, Manish Raghavan
2016	A*	`conf`	Planning Problems for Sophisticated Agents with Present Bias. EC Jon M. Kleinberg, Sigal Oren, Manish Raghavan
2016	J	`jnl`	Planning Problems for Sophisticated Agents with Present Bias. CoRR Jon M. Kleinberg, Sigal Oren, Manish Raghavan
2014	A*	`conf`	Deduplicating a places database. WWW Nilesh N. Dalvi, Marian Olteanu, Manish Raghavan, Philip Bohannon