Remember What You Want to Forget: Algorithms for Machine Unlearning Article Swipe
Ayush Sekhari
,
Jayadev Acharya
,
Gautam Kamath
,
Ananda Theertha Suresh
·
YOU?
·
· 2021
· Open Access
·
YOU?
·
· 2021
· Open Access
·
We study the problem of unlearning datapoints from a learnt model. The learner first receives a dataset $S$ drawn i.i.d. from an unknown distribution, and outputs a model $\widehat{w}$ that performs well on unseen samples from the same distribution. However, at some point in the future, any training datapoint $z \in S$ can request to be unlearned, thus prompting the learner to modify its output model while still ensuring the same accuracy guarantees. We initiate a rigorous study of generalization in machine unlearning, where the goal is to perform well on previously unseen datapoints. Our focus is on both computational and storage complexity. For the setting of convex losses, we provide an unlearning algorithm that can unlearn up to $O(n/d^{1/4})$ samples, where $d$ is the problem dimension. In comparison, in general, differentially private learning (which implies unlearning) only guarantees deletion of $O(n/d^{1/2})$ samples. This demonstrates a novel separation between differential privacy and machine unlearning.
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- https://arxiv.org/abs/2103.03279
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- 19
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- Title
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Remember What You Want to Forget: Algorithms for Machine UnlearningWork title
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articleOpenAlex work type
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enPrimary language
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2021Year of publication
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2021-12-06Full publication date if available
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Ayush Sekhari, Jayadev Acharya, Gautam Kamath, Ananda Theertha SureshList of authors in order
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https://arxiv.org/abs/2103.03279Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/abs/2103.03279Direct OA link when available
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Generalization, Computer science, Focus (optics), Point (geometry), VC dimension, Dimension (graph theory), Artificial intelligence, Regular polygon, Algorithm, Machine learning, Differential privacy, Mathematics, Combinatorics, Mathematical analysis, Geometry, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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19Other works algorithmically related by OpenAlex
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| abstract_inverted_index.storage | 101 |
| abstract_inverted_index.unknown | 22 |
| abstract_inverted_index.unlearn | 116 |
| abstract_inverted_index.However, | 39 |
| abstract_inverted_index.accuracy | 71 |
| abstract_inverted_index.deletion | 139 |
| abstract_inverted_index.ensuring | 68 |
| abstract_inverted_index.general, | 130 |
| abstract_inverted_index.initiate | 74 |
| abstract_inverted_index.learning | 133 |
| abstract_inverted_index.performs | 30 |
| abstract_inverted_index.receives | 14 |
| abstract_inverted_index.rigorous | 76 |
| abstract_inverted_index.samples, | 120 |
| abstract_inverted_index.samples. | 142 |
| abstract_inverted_index.training | 47 |
| abstract_inverted_index.algorithm | 113 |
| abstract_inverted_index.datapoint | 48 |
| abstract_inverted_index.prompting | 58 |
| abstract_inverted_index.datapoints | 6 |
| abstract_inverted_index.dimension. | 126 |
| abstract_inverted_index.guarantees | 138 |
| abstract_inverted_index.previously | 91 |
| abstract_inverted_index.separation | 147 |
| abstract_inverted_index.unlearned, | 56 |
| abstract_inverted_index.unlearning | 5, 112 |
| abstract_inverted_index.comparison, | 128 |
| abstract_inverted_index.complexity. | 102 |
| abstract_inverted_index.datapoints. | 93 |
| abstract_inverted_index.guarantees. | 72 |
| abstract_inverted_index.unlearning) | 136 |
| abstract_inverted_index.unlearning, | 82 |
| abstract_inverted_index.unlearning. | 153 |
| abstract_inverted_index.demonstrates | 144 |
| abstract_inverted_index.differential | 149 |
| abstract_inverted_index.$\widehat{w}$ | 28 |
| abstract_inverted_index.computational | 99 |
| abstract_inverted_index.distribution, | 23 |
| abstract_inverted_index.distribution. | 38 |
| abstract_inverted_index.$O(n/d^{1/2})$ | 141 |
| abstract_inverted_index.$O(n/d^{1/4})$ | 119 |
| abstract_inverted_index.differentially | 131 |
| abstract_inverted_index.generalization | 79 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.17000428 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |