6 papers · 1 filter
COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation
Arnav M. Das, Gantavya Bhatt, Lilly Kumari +2
Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing model performance in the low-data…
Tilted Sharpness-Aware Minimization
Tian Li, Tianyi Zhou, Jeffrey A. Bilmes
Sharpness-Aware Minimization (SAM) has been demonstrated to improve the generalization performance of overparameterized models by seeking flat minima on the loss landscape through…
How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models
Sahil Verma, Royi Rassin, Arnav Das +6
Text-to-image models are trained using large datasets of image-text pairs collected from the internet. These datasets often include copyrighted and private images. Training models…
Many-Objective Multi-Solution Transport
Ziyue Li, Tian Li, Virginia Smith +2
Optimizing the performance of many objectives (instantiated by tasks or clients) jointly with a few Pareto stationary solutions (models) is critical in machine learning. However, p…
Deep Submodular Peripteral Networks
Gantavya Bhatt, Arnav Das, Jeff Bilmes
Submodular functions, crucial for various applications, often lack practical learning methods for their acquisition. Seemingly unrelated, learning a scaling from oracles offering g…
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
Gantavya Bhatt, Yifang Chen, Arnav M. Das +9
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language mo…