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Found 12 papers, 4 papers with code
Verified Training for Counterfactual Explanation Robustness under Data Shift
Our empirical evaluation demonstrates that VeriTraCER generates CEs that (1) are verifiably robust to small model updates and (2) display competitive robustness to state-of-the-art approaches in handling empirical model updates including random initialization, leave-one-out, and distribution shifts.
The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions
We introduce dataset multiplicity, a way to study how inaccuracies, uncertainty, and social bias in training datasets impact test-time predictions.
PECAN: A Deterministic Certified Defense Against Backdoor Attacks
Neural networks are vulnerable to backdoor poisoning attacks, where the attackers maliciously poison the training set and insert triggers into the test input to change the prediction of the victim model.
Certifying Data-Bias Robustness in Linear Regression
Datasets typically contain inaccuracies due to human error and societal biases, and these inaccuracies can affect the outcomes of models trained on such datasets.
BagFlip: A Certified Defense against Data Poisoning
Machine learning models are vulnerable to data-poisoning attacks, in which an attacker maliciously modifies the training set to change the prediction of a learned model.
Certifying Robustness to Programmable Data Bias in Decision Trees
To certify robustness, we use a novel symbolic technique to evaluate a decision-tree learner on a large, or infinite, number of datasets, certifying that each and every dataset produces the same prediction for a specific test point.
Certified Robustness to Programmable Transformations in LSTMs
Deep neural networks for natural language processing are fragile in the face of adversarial examples -- small input perturbations, like synonym substitution or word duplication, which cause a neural network to change its prediction.
Robustness to Programmable String Transformations via Augmented Abstract Training
We then present an approach to adversarially training models that are robust to such user-defined string transformations.
Proving Data-Poisoning Robustness in Decision Trees
Machine learning models are brittle, and small changes in the training data can result in different predictions.
Quantifying Program Bias
With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is imperative that we aggressively investigate whether programs are biased.
Fairness as a Program Property
We explore the following question: Is a decision-making program fair, for some useful definition of fairness?
Learning Syntactic Program Transformations from Examples
In the second domain, we use repetitive edits applied by developers to the same project to synthesize a program transformation that applies these edits to other locations in the code.
