57 citations · 96 across the 12 of their papers we have counts for
4 papers · 1 filter
BOND: License to Train with Black-Box Functions
Andrew Clark, Jack Moursounidis, Osmaan Rasouli +3
We introduce Bounded Numerical Differentiation (BOND), a perturbative method for estimating the gradients of black-box functions. BOND is distinguished by its formulation, which ad…
A Robust and Efficient Pipeline for Enterprise-Level Large-Scale Entity Resolution
Sandeepa Kannangara, Arman Abrahamyan, Daniel Elias +5
Entity resolution (ER) remains a significant challenge in data management, especially when dealing with large datasets. This paper introduces MERAI (Massive Entity Resolution using…
FeatureCuts: Feature Selection for Large Data by Optimizing the Cutoff
Andy Hu, Devika Prasad, Luiz Pizzato +5
In machine learning, the process of feature selection involves finding a reduced subset of features that captures most of the information required to train an accurate and efficien…
Learning Adapter Rank via Symmetry Breaking
Cooper Doyle, Andy Hu, Rebecca Chan +1
Low-rank adaptation is effective partly because downstream updates lie in a low-dimensional subspace, but the latent rank coordinates of LoRA are not identifiable: any invertible r…