48 citations · 77 across the 16 of their papers we have counts for
6 papers · 1 filter
Characterizing Out-of-Distribution Error via Optimal Transport
Yuzhe Lu, Yilong Qin, Runtian Zhai +7
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models, so methods of predicting a model's performance on OOD data without labels are important…
Predicting Out-of-Distribution Error with Confidence Optimal Transport
Yuzhe Lu, Zhenlin Wang, Runtian Zhai +3
Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops. Being able to estimate…
Wasserstein Task Embedding for Measuring Task Similarities
Xinran Liu, Yikun Bai, Yuzhe Lu +2
Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most curr…
Teaching Networks to Solve Optimization Problems
Xinran Liu, Yuzhe Lu, Ali Abbasi +3
Leveraging machine learning to facilitate the optimization process is an emerging field that holds the promise to bypass the fundamental computational bottleneck caused by classic…
SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings
Yuzhe Lu, Xinran Liu, Andrea Soltoggio +1
Learning from set-structured data is an essential problem with many applications in machine learning and computer vision. This paper focuses on non-parametric and data-independent…
Compressive Neural Representations of Volumetric Scalar Fields
Yuzhe Lu, Kairong Jiang, Joshua A. Levine +1
We present an approach for compressing volumetric scalar fields using implicit neural representations. Our approach represents a scalar field as a learned function, wherein a neura…