85 citations · 121 across the 6 of their papers we have counts for
6 papers
Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers
Colin Wei, Yining Chen, Tengyu Ma
A common lens to theoretically study neural net architectures is to analyze the functions they can approximate. However, constructions from approximation theory may be unrealistic…
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments
Yining Chen, Elan Rosenfeld, Mark Sellke +2
Domain generalization aims at performing well on unseen test environments with data from a limited number of training environments. Despite a proliferation of proposal algorithms f…
Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data
Colin Wei, Kendrick Shen, Yining Chen +1
Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using ne…
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Kaidi Cao, Yining Chen, Junwei Lu +3
Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imba…
Active Online Learning with Hidden Shifting Domains
Yining Chen, Haipeng Luo, Tengyu Ma +1
Online machine learning systems need to adapt to domain shifts. Meanwhile, acquiring label at every timestep is expensive. We propose a surprisingly simple algorithm that adaptivel…
Self-training Avoids Using Spurious Features Under Domain Shift
Yining Chen, Colin Wei, Ananya Kumar +1
In unsupervised domain adaptation, existing theory focuses on situations where the source and target domains are close. In practice, conditional entropy minimization and pseudo-lab…