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ORCIDNikhil Garg 0001
Person information
- affiliation: Cornell Tech, NY, USA
Other persons with the same name
- Nikhil Garg — disambiguation page
- Nikhil Garg 0002
![[0000-0002-9210-2826]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-9210-2826")
— University of Sherbrooke, QC, Canada (and 1 more) - Nikhil Garg 0003 — University of Geneva, Switzerland
- Nikhil Garg 0005 — Ecole Polytechnique Fédérale de Lausanne, Switzerland
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2020 – today
- 2024
[j6]Zhi Liu, Uma Bhandaram, Nikhil Garg:
Quantifying spatial under-reporting disparities in resident crowdsourcing. Nat. Comput. Sci. 4(1): 57-65 (2024)- [c25]Gabriel Agostini, Emma Pierson, Nikhil Garg:
A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing. AAAI 2024: 21888-21896 - [c24]Zhi Liu, Sarah Rankin, Nikhil Garg:
Identifying and Addressing Disparities in Public Libraries with Bayesian Latent Variable Modeling. AAAI 2024: 22258-22265 - [c23]Sidhika Balachandar, Nikhil Garg, Emma Pierson:
Domain constraints improve risk prediction when outcome data is missing. ICLR 2024 - [c22]Rajiv Movva, Sidhika Balachandar, Kenny Peng, Gabriel Agostini, Nikhil Garg, Emma Pierson:
Topics, Authors, and Institutions in Large Language Model Research: Trends from 17K arXiv Papers. NAACL-HLT 2024: 1223-1243 - [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]Kenny Peng, Nikhil Garg:
Wisdom and Foolishness of Noisy Matching Markets. CoRR abs/2402.16771 (2024) - 2023
- [c20]Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak, John V. Guttag, Nikhil Garg, Emma Pierson:
Coarse race data conceals disparities in clinical risk score performance. MLHC 2023: 443-472 - [c19]Meena Jagadeesan, Nikhil Garg, Jacob Steinhardt:
Supply-Side Equilibria in Recommender Systems. NeurIPS 2023 - [c18]Rana Shahout, Yehonatan Peisakhovsky, Sasha Stoikov, Nikhil Garg:
Interface Design to Mitigate Inflation in Recommender Systems. RecSys 2023: 897-903 - [i27]Rajiv Movva, Divya Shanmugam, Kaihua Hou, Priya Pathak, John V. Guttag, Nikhil Garg, Emma Pierson:
Coarse race data conceals disparities in clinical risk score performance. CoRR abs/2304.09270 (2023) - [i26]Smitha Milli, Emma Pierson, Nikhil Garg:
Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems. CoRR abs/2305.17428 (2023) - [i25]Rajiv Movva, Sidhika Balachandar, Kenny Peng, Gabriel Agostini, Nikhil Garg, Emma Pierson:
Large language models shape and are shaped by society: A survey of arXiv publication patterns. CoRR abs/2307.10700 (2023) - [i24]Rana Shahout, Yehonatan Peisakhovsky, Sasha Stoikov, Nikhil Garg:
Interface Design to Mitigate Inflation in Recommender Systems. CoRR abs/2307.12424 (2023) - [i23]Kenny Peng, Manish Raghavan, Emma Pierson, Jon M. Kleinberg, Nikhil Garg:
Reconciling the accuracy-diversity trade-off in recommendations. CoRR abs/2307.15142 (2023) - [i22]Sidhika Balachandar, Nikhil Garg, Emma Pierson:
Domain constraints improve risk prediction when outcome data is missing. CoRR abs/2312.03878 (2023) - [i21]Kenny Peng, Nikhil Garg:
Monoculture in Matching Markets. CoRR abs/2312.09841 (2023) - [i20]Gabriel Agostini, Emma Pierson, Nikhil Garg:
A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing. CoRR abs/2312.11754 (2023) - 2022
- [j5]Nikhil Garg, Hamid Nazerzadeh:
Driver Surge Pricing. Manag. Sci. 68(5): 3219-3235 (2022) - [c17]Lydia T. Liu, Nikhil Garg, Christian Borgs:
Strategic ranking. AISTATS 2022: 2489-2518 - [c16]J. D. Zamfirescu-Pereira, Jerry Chen, Emily Wen, Allison Koenecke, Nikhil Garg, Emma Pierson:
Trucks Don't Mean Trump: Diagnosing Human Error in Image Analysis. FAccT 2022: 799-813 - [c15]Gourab K. Patro, Lorenzo Porcaro, Laura Mitchell, Qiuyue Zhang, Meike Zehlike, Nikhil Garg:
Fair ranking: a critical review, challenges, and future directions. FAccT 2022: 1929-1942 - [c14]Nikhil Garg, Wes Gurnee, David Rothschild, David B. Shmoys:
Combatting Gerrymandering with Social Choice: The Design of Multi-member Districts. EC 2022: 560-561 - [c13]Zhi Liu, Nikhil Garg:
Equity in Resident Crowdsourcing: Measuring Under-reporting without Ground Truth Data. EC 2022: 1016-1017 - [i19]Gourab K. Patro, Lorenzo Porcaro, Laura Mitchell, Qiuyue Zhang, Meike Zehlike, Nikhil Garg:
Fair ranking: a critical review, challenges, and future directions. CoRR abs/2201.12662 (2022) - [i18]Zhi Liu, Nikhil Garg:
Equity in Resident Crowdsourcing: Measuring Under-reporting without Ground Truth Data. CoRR abs/2204.08620 (2022) - [i17]J. D. Zamfirescu-Pereira, Jerry Chen, Emily Wen, Allison Koenecke, Nikhil Garg, Emma Pierson:
Trucks Don't Mean Trump: Diagnosing Human Error in Image Analysis. CoRR abs/2205.07333 (2022) - [i16]Meena Jagadeesan, Nikhil Garg, Jacob Steinhardt:
Supply-Side Equilibria in Recommender Systems. CoRR abs/2206.13489 (2022) - 2021
- [j4]Nikhil Garg, Ramesh Johari:
Designing Informative Rating Systems: Evidence from an Online Labor Market. Manuf. Serv. Oper. Manag. 23(3): 589-605 (2021) - [j3]Nikhil Garg, Ashish Goel, Benjamin Plaut:
Markets for public decision-making. Soc. Choice Welf. 56(4): 755-801 (2021) - [c12]Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg:
The Stereotyping Problem in Collaboratively Filtered Recommender Systems. EAAMO 2021: 6:1-6:10 - [c11]Zhi Liu, Nikhil Garg:
Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps. EAAMO 2021: 11:1-11:13 - [c10]Nikhil Garg, Hannah Li, Faidra Monachou:
Standardized Tests and Affirmative Action: The Role of Bias and Variance. FAccT 2021: 261 - [i15]Wenshuo Guo, Karl Krauth, Michael I. Jordan, Nikhil Garg:
The Stereotyping Problem in Collaboratively Filtered Recommender Systems. CoRR abs/2106.12622 (2021) - [i14]Nikhil Garg, Wes Gurnee, David Rothschild, David B. Shmoys:
Combatting Gerrymandering with Social Choice: the Design of Multi-member Districts. CoRR abs/2107.07083 (2021) - [i13]Zhi Liu, Nikhil Garg:
Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps. CoRR abs/2107.08922 (2021) - [i12]Lydia T. Liu, Nikhil Garg, Christian Borgs:
Strategic Ranking. CoRR abs/2109.08240 (2021) - 2020
- [c9]William Cai, Johann Gaebler, Nikhil Garg, Sharad Goel:
Fair Allocation through Selective Information Acquisition. AIES 2020: 22-28 - [c8]Nikhil Garg, Ramesh Johari:
Designing Informative Rating Systems: Evidence from an Online Labor Market. EC 2020: 71 - [c7]Nikhil Garg, Hamid Nazerzadeh:
Driver Surge Pricing. EC 2020: 501 - [i11]Nikhil Garg, Hannah Li, Faidra Monachou:
Standardized Tests and Affirmative Action: The Role of Bias and Variance. CoRR abs/2010.04396 (2020)
2010 – 2019
- 2019
- [j2]Nikhil Garg, Vijay Kamble, Ashish Goel, David Marn, Kamesh Munagala:
Iterative Local Voting for Collective Decision-making in Continuous Spaces. J. Artif. Intell. Res. 64: 315-355 (2019) - [c6]Nikhil Garg, Ramesh Johari:
Designing Optimal Binary Rating Systems. AISTATS 2019: 1930-1939 - [c5]Nikhil Garg, Lodewijk Gelauff, Sukolsak Sakshuwong, Ashish Goel:
Who Is in Your Top Three? Optimizing Learning in Elections with Many Candidates. HCOMP 2019: 22-31 - [c4]Dorottya Demszky, Nikhil Garg, Rob Voigt, James Zou, Jesse Shapiro, Matthew Gentzkow, Dan Jurafsky:
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings. NAACL-HLT (1) 2019: 2970-3005 - [i10]Dorottya Demszky, Nikhil Garg, Rob Voigt, James Zou, Matthew Gentzkow, Jesse Shapiro, Dan Jurafsky:
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings. CoRR abs/1904.01596 (2019) - [i9]Nikhil Garg, Hamid Nazerzadeh:
Driver Surge Pricing. CoRR abs/1905.07544 (2019) - [i8]Nikhil Garg, Lodewijk Gelauff, Sukolsak Sakshuwong, Ashish Goel:
Who is in Your Top Three? Optimizing Learning in Elections with Many Candidates. CoRR abs/1906.08160 (2019) - [i7]William Cai, Johann Gaebler, Nikhil Garg, Sharad Goel:
Fair Allocation through Selective Information Acquisition. CoRR abs/1911.02715 (2019) - 2018
- [j1]Nikhil Garg, Londa Schiebinger, Dan Jurafsky, James Zou:
Word embeddings quantify 100 years of gender and ethnic stereotypes. Proc. Natl. Acad. Sci. USA 115(16): E3635-E3644 (2018) - [c3]Nikhil Garg, Ashish Goel, Benjamin Plaut:
Markets for Public Decision-Making. WINE 2018: 445 - [i6]Nikhil Garg, Ramesh Johari:
Designing Optimal Binary Rating Systems. CoRR abs/1806.06908 (2018) - [i5]Nikhil Garg, Ashish Goel, Benjamin Plaut:
Markets for Public Decision-making. CoRR abs/1807.10836 (2018) - [i4]Nikhil Garg, Ramesh Johari:
Designing Informative Rating Systems for Online Platforms: Evidence from Two Experiments. CoRR abs/1810.13028 (2018) - 2017
- [c2]Nikhil Garg, Vijay Kamble, Ashish Goel, David Marn, Kamesh Munagala:
Collaborative Optimization for Collective Decision-making in Continuous Spaces. WWW 2017: 617-626 - [i3]Nikhil Garg, Vijay Kamble, Ashish Goel, David Marn, Kamesh Munagala:
Collaborative Optimization for Collective Decision-making in Continuous Spaces. CoRR abs/1702.07984 (2017) - [i2]Nikhil Garg, Londa Schiebinger, Dan Jurafsky, James Zou:
Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes. CoRR abs/1711.08412 (2017) - 2015
- [c1]Nikhil Garg, Sarabjot Singh, Jeffrey G. Andrews:
Impact of Dual Slope Path Loss on User Association in HetNets. GLOBECOM Workshops 2015: 1-6 - [i1]Nikhil Garg, Sarabjot Singh, Jeffrey G. Andrews:
Impact of Dual Slope Path Loss on User Association in HetNets. CoRR abs/1510.07295 (2015)
Coauthor Index
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