Real-time payment fraud detection is a non-stationary streaming prediction problem: adversaries adapt before supervised labels mature, and localized burst attacks can cause losses before retraining. Production systems typically rely on tabular classifiers and rules, which can struggle to capture these emerging sequenti...
This paper develops theoretical bounds on the unfairness and expected loss at deployment, and derives sufficient conditions under which fairness and accuracy can be perfectly transferred via invariant representation learning, and designs a learning algorithm such that fair ML models learned with training data still hav...
A systematic study of how pruning affects SAE behavior is presented and theoretically shows that, for a fixed SAE, its impact is governed by perturbation energy, a covariance-weighted norm.
Suchit Gupte, Xue-Ru Zhang, M. Khalili· 0 citations
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