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Documents authored by Mobius, Markus

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Extended Abstract
DOI: 10.4230/LIPIcs.FORC.2024.10
Online Algorithms with Limited Data Retention (Extended Abstract)

Authors: Nicole Immorlica, Brendan Lucier, Markus Mobius, and James Siderius

Published in: LIPIcs, Volume 295, 5th Symposium on Foundations of Responsible Computing (FORC 2024)

Abstract
We introduce a model of online algorithms subject to strict constraints on data retention. An online learning algorithm encounters a stream of data points, one per round, generated by some stationary process. Crucially, each data point can request that it be removed from memory m rounds after it arrives. To model the impact of removal, we do not allow the algorithm to store any information or calculations between rounds other than a subset of the data points (subject to the retention constraints). At the conclusion of the stream, the algorithm answers a statistical query about the full dataset. We ask: what level of performance can be guaranteed as a function of m? We illustrate this framework for multidimensional mean estimation and linear regression problems. We show it is possible to obtain an exponential improvement over a baseline algorithm that retains all data as long as possible. Specifically, we show that m = Poly(d, log(1/ε)) retention suffices to achieve mean squared error ε after observing O(1/ε) d-dimensional data points. This matches the error bound of the optimal, yet infeasible, algorithm that retains all data forever. We also show a nearly matching lower bound on the retention required to guarantee error ε. One implication of our results is that data retention laws are insufficient to guarantee the right to be forgotten even in a non-adversarial world in which firms merely strive to (approximately) optimize the performance of their algorithms. Our approach makes use of recent developments in the multidimensional random subset sum problem to simulate the progression of stochastic gradient descent under a model of adversarial noise, which may be of independent interest.
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Nicole Immorlica, Brendan Lucier, Markus Mobius, and James Siderius. Online Algorithms with Limited Data Retention (Extended Abstract). In 5th Symposium on Foundations of Responsible Computing (FORC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 295, pp. 10:1-10:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{immorlica_et_al:LIPIcs.FORC.2024.10,
  author =	{Immorlica, Nicole and Lucier, Brendan and Mobius, Markus and Siderius, James},
  title =	{{Online Algorithms with Limited Data Retention}},
  booktitle =	{5th Symposium on Foundations of Responsible Computing (FORC 2024)},
  pages =	{10:1--10:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-319-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{295},
  editor =	{Rothblum, Guy N.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2024.10},
  URN =		{urn:nbn:de:0030-drops-200937},
  doi =		{10.4230/LIPIcs.FORC.2024.10},
  annote =	{Keywords: online algorithms, machine learning, data, privacy, law}
}
Document
Extended Abstract
DOI: 10.4230/LIPIcs.ITCS.2024.57
Communicating with Anecdotes (Extended Abstract)

Authors: Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Markus Mobius, and Divyarthi Mohan

Published in: LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)

Abstract
We study a communication game between a sender and receiver. The sender chooses one of her signals about the state of the world (i.e., an anecdote) and communicates it to the receiver who takes an action affecting both players. The sender and receiver both care about the state of the world but are also influenced by personal preferences, so their ideal actions can differ. We characterize perfect Bayesian equilibria. The sender faces a temptation to persuade: she wants to select a biased anecdote to influence the receiver’s action. Anecdotes are still informative to the receiver (who will debias at equilibrium) but the attempt to persuade comes at the cost of precision. This gives rise to informational homophily where the receiver prefers to listen to like-minded senders because they provide higher-precision signals. Communication becomes polarized when the sender is an expert with access to many signals, with the sender choosing extreme outlier anecdotes at equilibrium (unless preferences are perfectly aligned). This polarization dissipates all the gains from communication with an increasingly well-informed sender when the anecdote distribution is heavy-tailed. Experts therefore face a curse of informedness: receivers will prefer to listen to less-informed senders who cannot pick biased signals as easily.
Cite as

Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Markus Mobius, and Divyarthi Mohan. Communicating with Anecdotes (Extended Abstract). In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 57:1-57:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{haghtalab_et_al:LIPIcs.ITCS.2024.57,
  author =	{Haghtalab, Nika and Immorlica, Nicole and Lucier, Brendan and Mobius, Markus and Mohan, Divyarthi},
  title =	{{Communicating with Anecdotes}},
  booktitle =	{15th Innovations in Theoretical Computer Science Conference (ITCS 2024)},
  pages =	{57:1--57:2},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-309-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{287},
  editor =	{Guruswami, Venkatesan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.57},
  URN =		{urn:nbn:de:0030-drops-195852},
  doi =		{10.4230/LIPIcs.ITCS.2024.57},
  annote =	{Keywords: Communication game, Equilibrium, Polarization, Signalling}
}
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