5 citations · 7 across the 6 of their papers we have counts for
6 papers
A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)
Nihal V. Nayak, Paula Rodriguez-Diaz, Neha Hulkund +2
Instruction fine-tuning of large language models (LLMs) often involves selecting a subset of instruction training data from a large candidate pool, using a small query set from the…
DataS^3: Dataset Subset Selection for Specialization
Neha Hulkund, Alaa Maalouf, Levi Cai +15
In many real-world machine learning (ML) applications (e.g. detecting broken bones in x-ray images, detecting species in camera traps), in practice models need to perform well on s…
Privacy-preserving data release leveraging optimal transport and particle gradient descent
Konstantin Donhauser, Javier Abad, Neha Hulkund +1
We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government.…
Interpretable Distribution Shift Detection using Optimal Transport
Neha Hulkund, Nicolo Fusi, Jennifer Wortman Vaughan +1
We propose a method to identify and characterize distribution shifts in classification datasets based on optimal transport. It allows the user to identify the extent to which each…
Predicting Out-of-Domain Generalization with Neighborhood Invariance
Nathan Ng, Neha Hulkund, Kyunghyun Cho +1
Developing and deploying machine learning models safely depends on the ability to characterize and compare their abilities to generalize to new environments. Although recent work h…
GAN-based Data Augmentation for Chest X-ray Classification
Shobhita Sundaram, Neha Hulkund
A common problem in computer vision -- particularly in medical applications -- is a lack of sufficiently diverse, large sets of training data. These datasets often suffer from seve…