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The application of machine learning (ML) in computer systems introduces not only many benefits but also risks to society.
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Closest in time.
2021
Closest in time.
A. Travers, L. Licollari, G. Wang, V. Chandrasekaran, A. Dziedzic, D. Lie, and N. Papernot, “On the exploitability of audio machine learning pipelines to surreptitious adversarial examples,” 2021
2021
Closest in time.
M. Miceli, T. Yang, L. Naudts, M. Schuessler, D. Serbanescu, and A. Hanna, “Documenting computer vision datasets: An invitation to reflexive data practices,” in
2021
Closest in time.
A. Levine and S. Feizi, “Deep partition aggregation: Provable defenses against general poisoning attacks,” in
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
B. Reagen, W.-S. Choi, Y. Ko, V. T. Lee, H.-H. S. Lee, G.-Y. Wei, and D. Brooks, “Cheetah: Optimizing and accelerating homomorphic encryption for private inference,” in
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
K. Peng, A. Mathur, and A. Narayanan, “Mitigating dataset harms requires stewardship: Lessons from 1000 papers,”
2021
Closest in time.
A. Kapishnikov, S. Venugopalan, B. Avci, B. Wedin, M. Terry, and T. Bolukbasi, “Guided integrated gradients: An adaptive path method for removing noise,” in
2021
Closest in time.
G. Kaptchuk, R. Cummings, and E. M. Redmiles, ““i need a better description”: An investigation into user expectations for differential privacy,” in
2021
Closest in time.