10 citations · 10 across the 1 of their papers we have counts for
3 papers · 1 filter
Proving Data-Poisoning Robustness in Decision Trees
Samuel Drews, Aws Albarghouthi, Loris D'Antoni
Machine learning models are brittle, and small changes in the training data can result in different predictions. We study the problem of proving that a prediction is robust to data…
Efficient Synthesis with Probabilistic Constraints
Samuel Drews, Aws Albarghouthi, Loris D'Antoni
We consider the problem of synthesizing a program given a probabilistic specification of its desired behavior. Specifically, we study the recent paradigm of distribution-guided ind…
Quantifying Program Bias
Aws Albarghouthi, Loris D'Antoni, Samuel Drews +1
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. We propose a…