Yu Bai

Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification

2021 · International Conference on Machine Learning

Modern machine learning models with high accuracy are often miscalibrated -- the predicted top probability does not reflect the actual accuracy, and tends to be over-confident. It is commonly believed that such over-confidence is mainly …

Improved Online Conformal Prediction via Strongly Adaptive Online Learning

2023 · arXiv (Cornell University)

We study the problem of uncertainty quantification via prediction sets, in an online setting where the data distribution may vary arbitrarily over time. Recent work develops online conformal prediction techniques that leverage regret minimization algorithms …

Reasoning Knowledge Transfer for Logical Table-to-Text Generation

2024

Logical table-to-text generation (LT2T) aims to generate logically faithful textual descriptions from tables. However, existing end-to-end LT2T models, which directly utilize descriptions as learning objectives, often struggle to ensure logical faithfulness due to the absence …

Progressive Stacking for Scalable Graph Condensation

2025

Large-scale graph data has demonstrated significant success in graph representation learning, but the associated high computational cost and inefficiency hinder its widespread adoption across diverse applications. Graph condensation has emerged as a promising solution to …