78 citations · 130 across the 30 of their papers we have counts for
7 papers · 1 filter
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model
Maohao Shen, Yuheng Bu, Prasanna Sattigeri +3
It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures. Existing methods mainl…
Information-theoretic Characterizations of Generalization Error for the Gibbs Algorithm
Gholamali Aminian, Yuheng Bu, Laura Toni +2
Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm. However, existing bounds are often loose and even vacuous when ev…
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
Haiyun He, Gholamali Aminian, Yuheng Bu +2
We provide an exact characterization of the expected generalization error (gen-error) for semi-supervised learning (SSL) with pseudo-labeling via the Gibbs algorithm. The gen-error…
Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
Alejandro Lancho, Amir Weiss, Gary C. F. Lee +4
We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture. In particular, we assume knowledge on…
Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation
Gary C. F. Lee, Amir Weiss, Alejandro Lancho +4
We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains. Unlike c…
Tighter Expected Generalization Error Bounds via Convexity of Information Measures
Gholamali Aminian, Yuheng Bu, Gregory Wornell +1
Generalization error bounds are essential to understanding machine learning algorithms. This paper presents novel expected generalization error upper bounds based on the average jo…
On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation
Maohao Shen, Yuheng Bu, Gregory Wornell
Due to privacy, storage, and other constraints, there is a growing need for unsupervised domain adaptation techniques in machine learning that do not require access to the data use…