Fetching the paper…
Reading the bibliography…
Introduced as a notion of algorithmic fairness, multicalibration has proved to be a powerful and versatile concept with implications far beyond its original intent.
Verification of forecasts expressed in terms of probability
Glenn W Brier · 1950
Earlier work this paper cites.
Objective probability forecasts
A. P. Dawid · 1982
Earlier work this paper cites.
Present position and potential developments: Some personal views statistical theory the prequential approach
A Philip Dawid · 1984
Earlier work this paper cites.
Asymptotic calibration
Dean P. Foster and Rakesh V. Vohra · 1998
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
John Platt et al · 1999
Earlier work this paper cites.
Agnostic boosting
Shai Ben-David, Philip M. Long, and Yishay Mansour · 2001
Earlier work this paper cites.
Boosting using branching programs
Yishay Mansour and David McAllester · 2002
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Pairwise independence and derandomization
Michael Luby and Avi Wigderson · 2006
Earlier work this paper cites.
Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
Earlier work this paper cites.
Deterministic calibration and nash equilibrium
Sham Kakade and Dean Foster · 2008
Earlier work this paper cites.
On agnostic boosting and parity learning
Adam Tauman Kalai, Yishay Mansour, and Elad Verbin · 2008
Earlier work this paper cites.
Distribution-specific agnostic boosting
Vitaly Feldman · 2009
Earlier work this paper cites.
Potential-based agnostic boosting
Adam Kalai and Varun Kanade · 2009
Earlier work this paper cites.
Sum-of-squares proofs and the quest toward optimal algorithms
Boaz Barak and David Steurer · 2014
Earlier work this paper cites.
Preserving statistical validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Leon Roth · 2015
Earlier work this paper cites.
Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration
Meelis Kull and Peter Flach · 2015
Earlier work this paper cites.
Algorithmic stability for adaptive data analysis
Raef Bassily, Kobbi Nissim, Adam Smith, Thomas Steinke, Uri Stemmer, and Jonathan Ullman · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Nati Srebro, et al · 2016
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2016
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Cited alongside, same era.
On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Smooth calibration, leaky forecasts, finite recall, and nash dynamics
Dean P. Foster and Sergiu Hart · 2018
Advancing subgroup fairness via sleeping experts
Avrim Blum and Thodoris Lykouris · 2020
Later among the works it cites.
Developing a COVID-19 mortality risk prediction model when individual-level data are not available
Noam Barda, Dan Riesel, Amichay Akriv, Joseph Levy, Uriah Finkel, Gal Yona, Daniel Greenfeld, Shimon Sheiba, Jonathan Somer, Eitan Bachmat, Guy N. Rothblum, Uri Shalit, Doron Netzer, Ran Balicer, and Noa Dagan · 2020
Later among the works it cites.
A complexity-theoretic perspective on fairness
Michael P. Kim · 2020
Later among the works it cites.
Sample complexity of uniform convergence for multicalibration
Eliran Shabat, Lee Cohen, and Yishay Mansour · 2020
Later among the works it cites.
Individual calibration with randomized forecasting
Shengjia Zhao, Tengyu Ma, and Stefano Ermon · 2020
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.
Multicalibration: Calibration for the (computationally-identifiable) masses
Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
Cited alongside, same era.
Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
Cited alongside, same era.
Fairness through computationally-bounded awareness
Michael P. Kim, Omer Reingold, and Guy N. Rothblum · 2018
Cited alongside, same era.
The limits of distribution-free conditional predictive inference
Rina Foygel Barber, Emmanuel J Candes, Aaditya Ramdas, and Ryan J Tibshirani · 2019
Cited alongside, same era.
Learning from outcomes: Evidence-based rankings
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, and Gal Yona · 2019
Cited alongside, same era.
Tracking and improving information in the service of fairness
Sumegha Garg, Michael P. Kim, and Omer Reingold · 2019
Cited alongside, same era.
Addressing bias in prediction models by improving subpopulation calibration
Noam Barda, Gal Yona, Guy N Rothblum, Philip Greenland, Morton Leibowitz, Ran Balicer, Eitan Bachmat, and Noa Dagan · 2021
Later among the works it cites.
Outcome indistinguishability
Cynthia Dwork, Michael P. Kim, Omer Reingold, Guy N. Rothblum, and Gal Yona · 2021
Later among the works it cites.
Online multivalid learning: Means, moments, and prediction intervals
Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M Pai, and Aaron Roth · 2021
Later among the works it cites.
Multicalibrated partitions for importance weights
Parikshit Gopalan, Omer Reingold, Vatsal Sharan, and Udi Wieder · 2021
Later among the works it cites.
Moment multicalibration for uncertainty estimation
Christopher Jung, Changhwa Lee, Mallesh Pai, Aaron Roth, and Rakesh Vohra · 2021
Later among the works it cites.
Multi-group agnostic pac learnability
Guy N Rothblum and Gal Yona · 2021
Later among the works it cites.
Simple and near-optimal algorithms for hidden stratification and multi-group learning
Christopher Tosh and Daniel Hsu · 2021
Later among the works it cites.
Right decisions from wrong predictions: A mechanism design alternative to individual calibration
Shengjia Zhao and Stefano Ermon · 2021
Later among the works it cites.
Calibrating predictions to decisions: A novel approach to multi-class calibration
Shengjia Zhao, Michael P. Kim, Roshni Sahoo, Tengyu Ma, and Stefano Ermon · 2021
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
Closest in time.
Omnipredictors
Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold, Vatsal Sharan, and Udi Wieder · 2022
Closest in time.
Universal adaptability: Target-independent inference that competes with propensity scoring
Michael P Kim, Christoph Kern, Shafi Goldwasser, Frauke Kreuter, and Omer Reingold · 2022
Closest in time.