Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Minimum-Risk Recalibration of Classifiers
Authors: Zeyu Sun, Dogyoon Song, Alfred Hero
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical findings through numerical simulations, which confirm the tightness of the proposed bounds, the optimal number of bins, and the effectiveness of label shift adaptation. |
| Researcher Affiliation | Academia | Zeyu Sun University of Michigan EMAIL Dogyoon Song University of Michigan EMAIL Alfred Hero University of Michigan EMAIL |
| Pseudocode | No | The paper describes methods with numbered steps but does not explicitly present any pseudocode or labeled algorithm blocks. |
| Open Source Code | Yes | Our simulation code is available at https://github.com/Zeyu Sun/calibration_label_ shift. |
| Open Datasets | No | The paper uses simulated data for its experiments, generated from defined distributions (e.g., 'family of joint distributions D(π) of X and Y'). It does not use or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper specifies sample sizes for source and target data ('n P = 103 and n Q = 102') and 'calibration sample size to be n = 5000', but it does not explicitly detail train, validation, and test splits with percentages or counts for a fixed dataset. |
| Hardware Specification | No | The paper mentions 'numerical simulations' but does not provide specific details about the hardware used, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide a reproducible description of ancillary software with specific version numbers for key components or libraries. |
| Experiment Setup | Yes | We vary n [102, 107] and B [6, 103] in the log scale. For each combination of (n, B), we use UMB to recalibrate f on data generated from D(0.5), and compute quadrature estimates of population Rcal(ˆh), Rsha(ˆh), and R(ˆh), as well as their high probability bounds based on Theorem 1. The constant K in Assumption (A3) is selected by numerical maximization. For each setting, we fix calibration sample size to be n = 5000. We consider the label shift with source distribution D(0.5) and target distribution D(πQ), where πQ varies in {0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5}. We vary n P in {10, 103, 105, 107} and n Q in {10, 103, 105}. The number of bins B are chosen to be n1/3 P for COMPOSITE and SOURCE, and n1/3 Q for TARGET. |