6 papers
Truthful Calibration Errors for Multi-Class Prediction
Yuxuan Lu, Yifan Wu, Jason Hartline +1
Calibrated predictions are useful because their numerical values can be interpreted as probabilities. Calibration errors are therefore widely used to evaluate, compare, and tune pr…
A Perfectly Truthful Calibration Measure
Jason Hartline, Lunjia Hu, Yifan Wu
Calibration requires that predictions are conditionally unbiased and, therefore, reliably interpretable as probabilities. A calibration measure quantifies how far a predictor is fr…
Pay Attention to Sequence Split: Uncovering the Impacts of Sub-Sequence Splitting on Sequential Recommendation Models
Yizhou Dang, Yifan Wu, Minhan Huang +5
Sub-sequence splitting (SSS) has been demonstrated as an effective approach to mitigate data sparsity in sequential recommendation (SR) by splitting a raw user interaction sequence…
Near-optimal Swap Regret Minimization for Convex Losses
Lunjia Hu, Jon Schneider, Yifan Wu
We give a randomized online algorithm that guarantees near-optimal expected swap regret against any sequence of adaptively chosen Lipschitz convex losse…
The Publication Choice Problem
Haichuan Wang, Yifan Wu, Haifeng Xu
Researchers strategically choose where to submit their work in order to maximize its impact, and these publication decisions in turn determine venues' impact factors. To analyze ho…
Coherence Mechanisms for Provable Self-Improvement
Mehryar Mohri, Jon Schneider, Yifan Wu
Self-improvement is a critical capability for large language models and other intelligent systems, enabling them to refine their behavior and internal consistency without external…