26 citations · 72 across the 18 of their papers we have counts for
18 papers
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality
Xuxi Chen, Yu Yang, Zhangyang Wang +1
Dataset distillation aims to minimize the time and memory needed for training deep networks on large datasets, by creating a small set of synthetic images that has a similar genera…
Sieve: Multimodal Dataset Pruning Using Image Captioning Models
Anas Mahmoud, Mostafa Elhoushi, Amro Abbas +4
Vision-Language Models (VLMs) are pretrained on large, diverse, and noisy web-crawled datasets. This underscores the critical need for dataset pruning, as the quality of these data…
Towards Sustainable Learning: Coresets for Data-efficient Deep Learning
Yu Yang, Hao Kang, Baharan Mirzasoleiman
To improve the efficiency and sustainability of learning deep models, we propose CREST, the first scalable framework with rigorous theoretical guarantees to identify the most valua…
Robust Learning with Progressive Data Expansion Against Spurious Correlation
Yihe Deng, Yu Yang, Baharan Mirzasoleiman +1
While deep learning models have shown remarkable performance in various tasks, they are susceptible to learning non-generalizable spurious features rather than the core features th…
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning
Yu Yang, Besmira Nushi, Hamid Palangi +1
Spurious correlations that degrade model generalization or lead the model to be right for the wrong reasons are one of the main robustness concerns for real-world deployments. Howe…
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias
Yu Yang, Eric Gan, Gintare Karolina Dziugaite +1
Neural networks trained with (stochastic) gradient descent have an inductive bias towards learning simpler solutions. This makes them highly prone to learning spurious correlations…