Search Results for author: Mohammad Yaghini

Found 10 papers, 3 papers with code

Regulation Games for Trustworthy Machine Learning

no code implementations5 Feb 2024 Mohammad Yaghini, Patty Liu, Franziska Boenisch, Nicolas Papernot

Existing work on trustworthy machine learning (ML) often concentrates on individual aspects of trust, such as fairness or privacy.

Fairness Gender Classification

Proof-of-Learning is Currently More Broken Than You Think

no code implementations6 Aug 2022 Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot

They empirically argued the benefit of this approach by showing how spoofing--computing a proof for a stolen model--is as expensive as obtaining the proof honestly by training the model.

Learning Theory

$p$-DkNN: Out-of-Distribution Detection Through Statistical Testing of Deep Representations

no code implementations25 Jul 2022 Adam Dziedzic, Stephan Rabanser, Mohammad Yaghini, Armin Ale, Murat A. Erdogdu, Nicolas Papernot

We introduce $p$-DkNN, a novel inference procedure that takes a trained deep neural network and analyzes the similarity structures of its intermediate hidden representations to compute $p$-values associated with the end-to-end model prediction.

Autonomous Driving Out-of-Distribution Detection +1

SoK: Machine Learning Governance

no code implementations20 Sep 2021 Varun Chandrasekaran, Hengrui Jia, Anvith Thudi, Adelin Travers, Mohammad Yaghini, Nicolas Papernot

The application of machine learning (ML) in computer systems introduces not only many benefits but also risks to society.

BIG-bench Machine Learning

Dataset Inference: Ownership Resolution in Machine Learning

1 code implementation ICLR 2021 Pratyush Maini, Mohammad Yaghini, Nicolas Papernot

We thus introduce $dataset$ $inference$, the process of identifying whether a suspected model copy has private knowledge from the original model's dataset, as a defense against model stealing.

BIG-bench Machine Learning

Proof-of-Learning: Definitions and Practice

2 code implementations9 Mar 2021 Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot

In particular, our analyses and experiments show that an adversary seeking to illegitimately manufacture a proof-of-learning needs to perform *at least* as much work than is needed for gradient descent itself.

A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness

no code implementations8 Nov 2019 Mohammad Yaghini, Andreas Krause, Hoda Heidari

Our family of fairness notions corresponds to a new interpretation of economic models of Equality of Opportunity (EOP), and it includes most existing notions of fairness as special cases.

Decision Making Fairness

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