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POTs: Protective Optimization Technologies

POTs: Protective Optimization Technologies

7 June 2018
B. Kulynych
R. Overdorf
Carmela Troncoso
Seda F. Gürses
ArXivPDFHTML

Papers citing "POTs: Protective Optimization Technologies"

11 / 11 papers shown
Title
Human-AI Coevolution
Human-AI Coevolution
D. Pedreschi
Luca Pappalardo
Emanuele Ferragina
R. Baeza-Yates
Albert-László Barabási
...
Paul Lukowicz
A. Passarella
Alex Pentland
John Shawe-Taylor
Alessandro Vespignani
49
14
0
23 Jun 2023
Optimization's Neglected Normative Commitments
Optimization's Neglected Normative Commitments
Benjamin Laufer
T. Gilbert
Helen Nissenbaum
OffRL
31
5
0
27 May 2023
FACE-AUDITOR: Data Auditing in Facial Recognition Systems
FACE-AUDITOR: Data Auditing in Facial Recognition Systems
Min Chen
Zhikun Zhang
Tianhao Wang
Michael Backes
Yang Zhang
CVBM
35
15
0
05 Apr 2023
Adversarial Robustness for Tabular Data through Cost and Utility
  Awareness
Adversarial Robustness for Tabular Data through Cost and Utility Awareness
Klim Kireev
B. Kulynych
Carmela Troncoso
AAML
31
16
0
27 Aug 2022
Addressing Privacy Threats from Machine Learning
Addressing Privacy Threats from Machine Learning
Mary Anne Smart
29
2
0
25 Oct 2021
Data Poisoning Won't Save You From Facial Recognition
Data Poisoning Won't Save You From Facial Recognition
Evani Radiya-Dixit
Sanghyun Hong
Nicholas Carlini
Florian Tramèr
AAML
PICV
22
57
0
28 Jun 2021
RobustBench: a standardized adversarial robustness benchmark
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
234
682
0
19 Oct 2020
Subpopulation Data Poisoning Attacks
Subpopulation Data Poisoning Attacks
Matthew Jagielski
Giorgio Severi
Niklas Pousette Harger
Alina Oprea
AAML
SILM
24
115
0
24 Jun 2020
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial
  Machine Learning
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
Pieter Delobelle
Paul Temple
Gilles Perrouin
Benoit Frénay
P. Heymans
Bettina Berendt
AAML
FaML
27
14
0
14 May 2020
Evading classifiers in discrete domains with provable optimality
  guarantees
Evading classifiers in discrete domains with provable optimality guarantees
B. Kulynych
Jamie Hayes
N. Samarin
Carmela Troncoso
AAML
21
19
0
25 Oct 2018
Fair prediction with disparate impact: A study of bias in recidivism
  prediction instruments
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
207
2,093
0
24 Oct 2016
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