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..
$H$-Consistency Guarantees for Regression
Authors: Anqi Mao, Mehryar Mohri, Yutao Zhong
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report favorable experimental results in Section 6. In this section, we demonstrate empirically the effectiveness of the smooth adversarial regression algorithms introduced in the previous section. |
| Researcher Affiliation | Collaboration | 1Courant Institute of Mathematical Sciences, New York, NY; 2Google Research, New York, NY. |
| Pseudocode | No | The paper describes methods and theoretical derivations but does not include any pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We studied two real-world datasets: the Diabetes dataset (Efron et al., 2004) and the Diverse MAGIC wheat dataset (Scott et al., 2021) |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about specific training/validation/test dataset splits, proportions, or cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the 'CVXPY library (Diamond & Boyd, 2016)' but does not specify a version number for the library itself. |
| Experiment Setup | Yes | For our smooth adversarial regression losses (2), we chose L = ℓ2, the squared loss, and L = ℓδ with δ = 0.2, the Huber loss, setting τ = 1 as the default. Other choices for the regression loss functions and the value of τ may yield better performance, which can typically be selected by cross-validation in practice. Both our smooth adversarial regression losses and the adversarial squared loss were optimized using the CVXPY library (Diamond & Boyd, 2016). |