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2012.06421
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When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?
Symposium on the Theory of Computing (STOC), 2020
11 December 2020
Gavin Brown
Mark Bun
Vitaly Feldman
Adam D. Smith
Kunal Talwar
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ArXiv (abs)
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Papers citing
"When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?"
50 / 92 papers shown
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Access Paths for Efficient Ordering with Large Language Models
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389
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Michèle Finck
Kristof Meding
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552
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The Pitfalls of Memorization: When Memorization Hurts Generalization
International Conference on Learning Representations (ICLR), 2024
Reza Bayat
Mohammad Pezeshki
Elvis Dohmatob
David Lopez-Paz
Pascal Vincent
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463
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Improved Localized Machine Unlearning Through the Lens of Memorization
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Reza Nasirigerdeh
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Eleni Triantafillou
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287
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Slowing Down Forgetting in Continual Learning
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475
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Diyi Yang
Christopher Potts
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382
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Data Reconstruction: When You See It and When You Don't
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Anastasiia Sedova
Pedro Henrique Luz de Araujo
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Lisa Nussbaumer
Benjamin Roth
316
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Differentially Private Reinforcement Learning with Self-Play
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285
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Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems
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298
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Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization
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376
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408
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Memorization in Self-Supervised Learning Improves Downstream Generalization
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Privacy Threats in Stable Diffusion Models
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262
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Why Train More? Effective and Efficient Membership Inference via Memorization
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What do larger image classifiers memorise?
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Memory-Query Tradeoffs for Randomized Convex Optimization
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Machine Unlearning: A Survey
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