511 citations · 1.1k across the 13 of their papers we have counts for
6 papers · 1 filter
Learning a Canonical Basis of Human Preferences from Binary Ratings
Kailas Vodrahalli, Wei Wei, James Zou
Recent advances in generative AI have been driven by alignment techniques such as reinforcement learning from human feedback (RLHF). RLHF and related techniques typically involve c…
Can large language models provide useful feedback on research papers? A large-scale empirical analysis
Weixin Liang, Yuhui Zhang, Hancheng Cao +9
Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scient…
Adversarial Training Helps Transfer Learning via Better Representations
Zhun Deng, Linjun Zhang, Kailas Vodrahalli +2
Transfer learning aims to leverage models pre-trained on source data to efficiently adapt to target setting, where only limited data are available for model fine-tuning. Recent wor…
Better Knowledge Retention through Metric Learning
Ke Li, Shichong Peng, Kailas Vodrahalli +1
In continual learning, new categories may be introduced over time, and an ideal learning system should perform well on both the original categories and the new categories. While de…
Harmless interpolation of noisy data in regression
Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian +1
A continuing mystery in understanding the empirical success of deep neural networks is their ability to achieve zero training error and generalize well, even when the training data…
Are All Training Examples Created Equal? An Empirical Study
Kailas Vodrahalli, Ke Li, Jitendra Malik
Modern computer vision algorithms often rely on very large training datasets. However, it is conceivable that a carefully selected subsample of the dataset is sufficient for traini…