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Found 8 papers, 0 papers with code
fAux: Testing Individual Fairness via Gradient Alignment
We propose a novel criterion for evaluating individual fairness and develop a practical testing method based on this criterion which we call fAux (pronounced fox).
A Solver + Gradient Descent Training Algorithm for Deep Neural Networks
We present a novel hybrid algorithm for training Deep Neural Networks that combines the state-of-the-art Gradient Descent (GD) method with a Mixed Integer Linear Programming (MILP) solver, outperforming GD and variants in terms of accuracy, as well as resource and data efficiency for both regression and classification tasks.
Nonlocal optimization of binary neural networks
We explore training Binary Neural Networks (BNNs) as a discrete variable inference problem over a factor graph.
Scalable Whitebox Attacks on Tree-based Models
Adversarial robustness is one of the essential safety criteria for guaranteeing the reliability of machine learning models.
PUMA: Performance Unchanged Model Augmentation for Training Data Removal
It then complements the negative impact of removing marked data by reweighting the remaining data optimally.
Parity Partition Coding for Sharp Multi-Label Classification
The problem of efficiently training and evaluating image classifiers that can distinguish between a large number of object categories is considered.
On Learning Wire-Length Efficient Neural Networks
Pruning neural networks for wiring length efficiency is considered.
Min-Max Propagation
We study the application of min-max propagation, a variation of belief propagation, for approximate min-max inference in factor graphs.
