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Found 8 papers, 3 papers with code
Balancing Fairness and Accuracy in Data-Restricted Binary Classification
Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier.
Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset.
A Canonical Data Transformation for Achieving Inter- and Within-group Fairness
The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness.
Transparency Tools for Fairness in AI (Luskin)
The three tools are: - A new definition of fairness called "controlled fairness" with respect to choices of protected features and filters.
How to 0wn the NAS in Your Spare Time
New data processing pipelines and novel network architectures increasingly drive the success of deep learning.
How to 0wn NAS in Your Spare Time
This provides an incentive for adversaries to steal these novel architectures; when used in the cloud, to provide Machine Learning as a Service, the adversaries also have an opportunity to reconstruct the architectures by exploiting a range of hardware side channels.
Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks
Based on the extracted architecture attributes, we also demonstrate that an attacker can build a meta-model that accurately fingerprints the architecture and family of the pre-trained model in a transfer learning setting.
Approximate resilience, monotonicity, and the complexity of agnostic learning
We study the notion of $\mathit{approximate}$ $\mathit{resilience}$ of Boolean functions, where we say that $f$ is $\alpha$-approximately $d$-resilient if $f$ is $\alpha$-close to a $[-1, 1]$-valued $d$-resilient function in $\ell_1$ distance.
