14.5k citations · 48.8k across the 185 of their papers we have counts for
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Which Preferences to Train On? End-to-End Multi-Objective Alignment with an Adversarial Preference Distribution
Minjae Lee, Kyunghyun Cho, Sangdon Park
Aligning large language models (LLMs) with human values is important for safe, efficient, and beneficial AI deployment. However, human values are multifaceted: helpfulness, harmles…
Latent State Models of Training Dynamics
Michael Y. Hu, Angelica Chen, Naomi Saphra +1
The impact of randomness on model training is poorly understood. How do differences in data order and initialization actually manifest in the model, such that some training runs ou…
Unsupervised Learning of Initialization in Deep Neural Networks via Maximum Mean Discrepancy
Cheolhyoung Lee, Kyunghyun Cho
Despite the recent success of stochastic gradient descent in deep learning, it is often difficult to train a deep neural network with an inappropriate choice of its initial paramet…
Joint Embedding Predictive Architectures Focus on Slow Features
Vlad Sobal, Jyothir S, Siddhartha Jalagam +3
Many common methods for learning a world model for pixel-based environments use generative architectures trained with pixel-level reconstruction objectives. Recently proposed Joint…
A Non-monotonic Self-terminating Language Model
Eugene Choi, Kyunghyun Cho, Cheolhyoung Lee
Recent large-scale neural autoregressive sequence models have shown impressive performances on a variety of natural language generation tasks. However, their generated sequences of…
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…