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Found 9 papers, 1 papers with code
SoK: Taming the Triangle -- On the Interplays between Fairness, Interpretability and Privacy in Machine Learning
Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction.
Probabilistic Dataset Reconstruction from Interpretable Models
In addition, we demonstrate that under realistic assumptions regarding the interpretable models' structure, the uncertainty of the reconstruction can be computed efficiently.
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction
More precisely, we propose a generic reconstruction correction method, which takes as input an initial guess made by the adversary and corrects it to comply with some user-defined constraints (such as the fairness information) while minimizing the changes in the adversary's guess.
Optimizing Binary Decision Diagrams with MaxSAT for classification
Then, we lift the encoding to a MaxSAT model to learn optimal BDDs in limited depths, that maximize the number of examples correctly classified.
Range Estimation of a Moving Target Using Ultrasound Differential Zadoff-Chu Codes
For the same movement range, the system provides range estimates with a root mean square error (RMSE) less than 0. 76 mm in a high SNR scenario (10 dB), and an MSE less than 0. 85 mm in a low SNR scenario (-10 dB).
Learning Fair Rule Lists
While it has been shown that interpretable models can be as accurate as black-box models in several critical domains, existing fair classification techniques that are interpretable by design often display poor accuracy/fairness tradeoffs in comparison with their non-interpretable counterparts.
On the Complexity of Robust Stable Marriage
Then, we show the equivalence between the SAT formulation and finding a (1, 1)-supermatch on a specific family of instances.
Finding Robust Solutions to Stable Marriage
We first define robustness by introducing (a, b)-supermatches.
Three Generalizations of the FOCUS Constraint
The FOCUS constraint expresses the notion that solutions are concentrated.
