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Found 6 papers, 1 papers with code
Defending Against Indirect Prompt Injection Attacks With Spotlighting
Indirect prompt injection attacks take advantage of this vulnerability by embedding adversarial instructions into untrusted data being processed alongside user commands.
Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective
We observe on three datasets that we can train a model with this loss term to improve explanation consensus on unseen data, and see improved consensus between explainers other than those used in the loss term.
Repairing Regressors for Fair Binary Classification at Any Decision Threshold
We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds.
Counterfactual Explanations for Machine Learning: Challenges Revisited
Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models.
Amortized Generation of Sequential Algorithmic Recourses for Black-box Models
We propose a novel stochastic-control-based approach that generates sequential ARs, that is, ARs that allow x to move stochastically and sequentially across intermediate states to a final state x'.
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools
There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years.
