no code implementations • 16 Feb 2024 • Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth
There, unlike in classical crowdsourced ML, participants deliberately specialize their efforts by working on subproblems, such as demographic subgroups in the service of fairness.
no code implementations • 8 Dec 2023 • Shuai Tang, Zhiwei Steven Wu, Sergul Aydore, Michael Kearns, Aaron Roth
Our proposed MI attack learns quantile regression models that predict (a quantile of) the distribution of reconstruction loss on examples not used in training.
no code implementations • 26 Jun 2023 • Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth
Importantly, we require that the proxy classification itself not reveal significant information about the sensitive group membership of any individual sample (i. e., it should be sufficiently non-disclosive).
no code implementations • 7 Apr 2023 • Alessandro Achille, Michael Kearns, Carson Klingenberg, Stefano Soatto
One potential fix for training corpus data defects is model disgorgement -- the elimination of not just the improperly used data, but also the effects of improperly used data on any component of an ML model.
2 code implementations • 6 Mar 2023 • Shuai Tang, Sergul Aydore, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu
We revisit the problem of differentially private squared error linear regression.
1 code implementation • 31 Jan 2023 • Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
Using this characterization, we give an exceedingly simple algorithm that can be analyzed both as a boosting algorithm for regression and as a multicalibration algorithm for a class H that makes use only of a standard squared error regression oracle for H. We give a weak learning assumption on H that ensures convergence to Bayes optimality without the need to make any realizability assumptions -- giving us an agnostic boosting algorithm for regression.
1 code implementation • 6 Nov 2022 • Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu
Our attacks significantly outperform those that are based only on access to a public distribution or population from which the private dataset $D$ was sampled, demonstrating that they are exploiting information in the aggregate statistics $Q(D)$, and not simply the overall structure of the distribution.
no code implementations • 15 Sep 2022 • Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth
We show how to take a regression function $\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints.
no code implementations • 15 Sep 2022 • Giuseppe Vietri, Cedric Archambeau, Sergul Aydore, William Brown, Michael Kearns, Aaron Roth, Ankit Siva, Shuai Tang, Zhiwei Steven Wu
A key innovation in our algorithm is the ability to directly handle numerical features, in contrast to a number of related prior approaches which require numerical features to be first converted into {high cardinality} categorical features via {a binning strategy}.
no code implementations • CVPR 2022 • Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto
AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off.
no code implementations • 25 Jan 2022 • Ira Globus-Harris, Michael Kearns, Aaron Roth
We propose and analyze an algorithmic framework for "bias bounties": events in which external participants are invited to propose improvements to a trained model, akin to bug bounty events in software and security.
no code implementations • 9 Jul 2021 • Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi
The goal of the proxy is to allow a general "downstream" learner -- with minimal assumptions on their prediction task -- to be able to use the proxy to train a model that is fair with respect to the true sensitive features.
1 code implementation • 11 Mar 2021 • Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Ankit Siva
We propose, implement, and evaluate a new algorithm for releasing answers to very large numbers of statistical queries like $k$-way marginals, subject to differential privacy.
no code implementations • 16 Feb 2021 • Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
We extend the notion of minimax fairness in supervised learning problems to its natural conclusion: lexicographic minimax fairness (or lexifairness for short).
1 code implementation • 5 Nov 2020 • Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth
We consider a recently introduced framework in which fairness is measured by worst-case outcomes across groups, rather than by the more standard differences between group outcomes.
1 code implementation • 12 Jun 2020 • Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani
We consider a variation on the classical finance problem of optimal portfolio design.
no code implementations • 12 Dec 2019 • Emily Diana, Michael Kearns, Seth Neel, Aaron Roth
We consider a fundamental dynamic allocation problem motivated by the problem of $\textit{securities lending}$ in financial markets, the mechanism underlying the short selling of stocks.
1 code implementation • 25 May 2019 • Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders.
1 code implementation • NeurIPS 2019 • Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi
Given a sample of individuals and classification problems, we design an oracle-efficient algorithm (i. e. one that is given access to any standard, fairness-free learning heuristic) for the fair empirical risk minimization task.
no code implementations • 22 May 2019 • Jinshuo Dong, Hadi Elzayn, Shahin Jabbari, Michael Kearns, Zachary Schutzman
We demonstrate a reduction from this potentially complicated action space to a one-shot, two-action game in which each firm only decides whether or not to buy the data.
no code implementations • 6 Dec 2018 • Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan Ullman
This algorithm is appealingly simple, but must be able to use protected group membership explicitly at test time, which can be viewed as a form of 'disparate treatment'.
no code implementations • 30 Aug 2018 • Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Zachary Schutzman
We formalize this fairness notion for allocation problems and investigate its algorithmic consequences.
5 code implementations • 24 Aug 2018 • Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal et al. [2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes.
no code implementations • NeurIPS 2018 • Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth
We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown similarity metric.
5 code implementations • ICML 2018 • Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
We prove that the computational problem of auditing subgroup fairness for both equality of false positive rates and statistical parity is equivalent to the problem of weak agnostic learning, which means it is computationally hard in the worst case, even for simple structured subclasses.
no code implementations • ICML 2017 • Michael Kearns, Aaron Roth, Zhiwei Steven Wu
We consider the problem of selecting a strong pool of individuals from several populations with incomparable skills (e. g. soccer players, mathematicians, and singers) in a fair manner.
1 code implementation • 7 Jun 2017 • Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems.
no code implementations • 27 Mar 2017 • Richard Berk, Hoda Heidari, Shahin Jabbari, Michael Kearns, Aaron Roth
Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments.
no code implementations • ICML 2017 • Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth
We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards.
no code implementations • 29 Oct 2016 • Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth
We study fairness in linear bandit problems.
no code implementations • 3 Jun 2016 • Michael Kearns, Zhiwei Steven Wu
We consider a new learning model in which a joint distribution over vector pairs $(x, y)$ is determined by an unknown function $c(x)$ that maps input vectors $x$ not to individual outputs, but to entire {\em distributions\/} over output vectors $y$.
no code implementations • NeurIPS 2016 • Matthew Joseph, Michael Kearns, Jamie Morgenstern, Aaron Roth
This tight connection allows us to provide a provably fair algorithm for the linear contextual bandit problem with a polynomial dependence on the dimension, and to show (for a different class of functions) a worst-case exponential gap in regret between fair and non-fair learning algorithms
no code implementations • 27 Jul 2014 • Kareem Amin, Rachel Cummings, Lili Dworkin, Michael Kearns, Aaron Roth
We consider the problem of learning from revealed preferences in an online setting.
no code implementations • NeurIPS 2013 • Tim Roughgarden, Michael Kearns
We consider a number of classical and new computational problems regarding marginal distributions, and inference in models specifying a full joint distribution.
no code implementations • 10 Jan 2013 • Michael Kearns, Michael L. Littman, Satinder Singh
The interpretation is that the payoff to player i is determined entirely by the actions of player i and his neighbors in the graph, and thus the payoff matrix to player i is indexed only by these players.
no code implementations • 28 Oct 2011 • Sanjeev Goyal, Michael Kearns
We also show that if this property is violated the Price of Anarchy can be unbounded, thus yielding sharp threshold behavior for a broad class of dynamics.
Computer Science and Game Theory