Fetching the paper…
Reading the bibliography…
Multicalibration is a notion of fairness for predictors that requires them to provide calibrated predictions across a large set of protected groups.
Correlation properties of cyclic sequences
Robert C Titsworth · 1962
Earlier work this paper cites.
Admissible probability measurement procedures
Emir H. Shuford, Arthur Albert, and H. Edward Massengill · 1966
Earlier work this paper cites.
Elicitation of personal probabilities and expectations
Leonard J. Savage · 1971
Earlier work this paper cites.
A general method for comparing probability assessors
Mark J. Schervish · 1989
Earlier work this paper cites.
Testing juntas
Eldar Fischer, Guy Kindler, Dana Ron, Shmuel Safra, and Alex Samorodnitsky · 2002
Earlier work this paper cites.
Learning functions of k relevant variables
Elchanan Mossel, Ryan O’Donnell, and Rocco A. Servedio · 2003
Earlier work this paper cites.
Deterministic calibration and Nash equilibrium
Sham M. Kakade and Dean P. Foster · 2004
Earlier work this paper cites.
Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
Daniel A. Spielman and Shang-Hua Teng · 2004
Earlier work this paper cites.
Loss functions for binary class probability estimation and classification: Structure and applications
Andreas Buja, Werner Stuetzle, and Yi Shen · 2005
Earlier work this paper cites.
Agnostically learning decision trees
Parikshit Gopalan, Adam Tauman Kalai, and Adam R. Klivans · 2008
Earlier work this paper cites.
Composite binary losses
Mark D. Reid and Robert C. Williamson · 2010
Earlier work this paper cites.
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, and Gal Yona · 2011
Earlier work this paper cites.
Analysis of boolean functions
Ryan O’Donnell · 2014
Earlier work this paper cites.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2017
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Earlier work this paper cites.
Multiclass classification, information, divergence and surrogate risk
John Duchi, Khashayar Khosravi, and Feng Ruan · 2018
Cited alongside, same era.
Multicalibration: Calibration for the (computationally-identifiable) masses
Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
Cited alongside, same era.
Fairness and Machine Learning: Limitations and Opportunities
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
Cited alongside, same era.
The implicit fairness criterion of unconstrained learning
Lydia T Liu, Max Simchowitz, and Moritz Hardt · 2019
Cited alongside, same era.
Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan and J Zico Kolter · 2019
The calibration generalization gap
Annabelle Carrell, Neil Mallinar, James Lucas, and Preetum Nakkiran · 2022
Later among the works it cites.
Beyond bernoulli: Generating random outcomes that cannot be distinguished from nature
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, and Gal Yona · 2022
Later among the works it cites.
Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, and Udi Wieder · 2022
Later among the works it cites.
Low-degree multicalibration
Parikshit Gopalan, Michael P. Kim, Mihir Singhal, and Shengjia Zhao · 2022
Later among the works it cites.
Multicalibrated partitions for importance weights
Parikshit Gopalan, Omer Reingold, Vatsal Sharan, and Udi Wieder · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning with Fenchel-Young losses
Mathieu Blondel, André F. T. Martins, and Vlad Niculae · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, …, and Dario Amodei · 2020
Cited alongside, same era.
Calibration of pre-trained transformers
Shrey Desai and Greg Durrett · 2020
Cited alongside, same era.
Augmix: A simple method to improve robustness and uncertainty under data shift
Dan Hendrycks*, Norman Mu*, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
Cited alongside, same era.
Moment multicalibration for uncertainty estimation
Christopher Jung, Changhwa Lee, Mallesh M Pai, Aaron Roth, and Rakesh Vohra · 2020
Cited alongside, same era.
Soft calibration objectives for neural networks
Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael C Mozer, and Becca Roelofs · 2021
Cited alongside, same era.
Universal adaptability: Target-independent inference that competes with propensity scoring
Michael P Kim, Christoph Kern, Shafi Goldwasser, Frauke Kreuter, and Omer Reingold · 2022
Later among the works it cites.
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al · 2023
Closest in time.
A unifying theory of distance from calibration
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu, and Preetum Nakkiran · 2023
Closest in time.
Happymap : A generalized multicalibration method
Zhun Deng, Cynthia Dwork, and Linjun Zhang · 2023
Closest in time.
Oracle efficient online multicalibration and omniprediction
Sumegha Garg, Christopher Jung, Omer Reingold, and Aaron Roth · 2023
Closest in time.
Multicalibration as boosting for regression
Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, and Jessica Sorrell · 2023
Closest in time.
Loss Minimization Through the Lens Of Outcome Indistinguishability
Parikshit Gopalan, Lunjia Hu, Michael P. Kim, Omer Reingold, and Udi Wieder · 2023
Closest in time.
Omnipredictors for constrained optimization
Lunjia Hu, Inbal Rachel Livni Navon, Omer Reingold, and Chutong Yang · 2023
Closest in time.
Making Decisions Under Outcome Performativity
Michael P. Kim and Juan C. Perdomo · 2023
Closest in time.