78 citations · 130 across the 30 of their papers we have counts for
6 papers · 1 filter
Gibbs-Based Information Criteria and the Over-Parameterized Regime
Haobo Chen, Yuheng Bu, Gregory W. Wornell
Double-descent refers to the unexpected drop in test loss of a learning algorithm beyond an interpolating threshold with over-parameterization, which is not predicted by informatio…
Feature Learning in Image Hierarchies using Functional Maximal Correlation
Bo Hu, Yuheng Bu, José C. Príncipe
This paper proposes the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a hierarchical methodology that characterizes dependencies across two hierarchical levels in…
A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch
Amir Weiss, Alejandro Lancho, Yuheng Bu +1
A bilateral (i.e., upper and lower) bound on the mean-square error under a general model mismatch is developed. The bound, which is derived from the variational representation of t…
Reliable Gradient-free and Likelihood-free Prompt Tuning
Maohao Shen, Soumya Ghosh, Prasanna Sattigeri +3
Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs. Fine-tuning such models to downstream tasks is challenging b…
On the Generalization Error of Meta Learning for the Gibbs Algorithm
Yuheng Bu, Harsha Vardhan Tetali, Gholamali Aminian +2
We analyze the generalization ability of joint-training meta learning algorithms via the Gibbs algorithm. Our exact characterization of the expected meta generalization error for t…
Group Fairness with Uncertainty in Sensitive Attributes
Abhin Shah, Maohao Shen, Jongha Jon Ryu +4
Learning a fair predictive model is crucial to mitigate biased decisions against minority groups in high-stakes applications. A common approach to learn such a model involves solvi…