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Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?
Trisha Mittal, Akshay Mehra, Joshua Kimball
Dataset distillation (DD) has emerged as a prominent approach in data centric machine learning, aiming to synthesize compact training sets for efficient training by compressing the…
Measuring Time-Series Dataset Similarity using Wasserstein Distance
Hongjie Chen, Akshay Mehra, Josh Kimball +1
The emergence of time-series foundation model research elevates the growing need to measure the (dis)similarity of time-series datasets. A time-series dataset similarity measure ai…
Coreset Selection via LLM-based Concept Bottlenecks
Akshay Mehra, Trisha Mittal, Subhadra Gopalakrishnan +1
Coreset Selection (CS) aims to identify a subset of the training dataset that achieves model performance comparable to using the entire dataset. Many state-of-the-art CS methods se…