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Peva Blanchard

2 accepted papers

2025

Generalizing while preserving monotonicity in comparison-based preference learning models

NeurIPS 2025poster

If you tell a learning model that you prefer an alternative $a$ over another alternative $b$, then you probably expect the model to be *monotone*, that is, the valuation of $a$ increases, and that of $b$ decreases. Yet, perhaps surprisingly, many widely deployed comparison-based preference learning…

Cited by 2SourceScholar
2017

Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent

NeurIPS 2017poster

We study the resilience to Byzantine failures of distributed implementations of Stochastic Gradient Descent (SGD). So far, distributed machine learning frameworks have largely ignored the possibility of failures, especially arbitrary (i.e., Byzantine) ones. Causes of failures include software bugs…

Cited by 2435SourcePDFScholar