Anqi Liu

Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction

2015 · Proceedings of the AAAI Conference on Artificial Intelligence

Existing approaches to active learning are generally optimistic about their certainty with respect to data shift between labeled and unlabeled data. They assume that unknown datapoint labels follow the inductive biases of the active learner. …

Consistent Robust Adversarial Prediction for General Multiclass Classification

2018 · arXiv (Cornell University)

We propose a robust adversarial prediction framework for general multiclass classification. Our method seeks predictive distributions that robustly optimize non-convex and non-continuous multiclass loss metrics against the worst-case conditional label distributions (the adversarial distributions) that …

Active Learning under Label Shift

2021 · CaltechAUTHORS (California Institute of Technology)

We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a tradeoff between importance weighting and class-balanced sampling …

MegaWika: Millions of reports and their sources across 50 diverse languages

2023 · arXiv (Cornell University)

To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages, along with their 71 million referenced source materials. We process …

Addressing the Binning Problem in Calibration Assessment through Scalar Annotations

2024 · Transactions of the Association for Computational Linguistics

Abstract Computational linguistics models commonly target the prediction of discrete—categorical—labels. When assessing how well-calibrated these model predictions are, popular evaluation schemes require practitioners to manually determine a binning scheme: grouping labels into bins to approximate …