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..
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
Authors: Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our key technical contribution consists in a new lower bound on the calibration function for the quadratic surrogate, which is non-trivial (not always zero) for inconsistent cases. The main technical contribution consists in a tighter lower bound on the calibration function (Theorem 3), which is strictly more general than the bound of [14]. |
| Researcher Affiliation | Academia | Kirill Struminsky NRU HSE, Moscow, Russia Simon Lacoste-Julien MILA and DIRO Université de Montréal, Canada Anton Osokin NRU HSE, Moscow, Russia Skoltech, Moscow, Russia National Research University Higher School of Economics CIFAR Fellow Samsung-HSE Joint Lab Skolkovo Institute of Science and Technology |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or a link for the open-source code of the described methodology. |
| Open Datasets | No | The paper performs theoretical analysis and numerical computations on abstract loss functions (tree-structured loss, mAP loss) rather than using specific publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical model training, thus it does not provide information about dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware used for its computations or analysis. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, required to replicate its computations. |
| Experiment Setup | No | The paper focuses on theoretical analysis and numerical computation of bounds, and therefore does not describe a conventional experimental setup with hyperparameters or training configurations. |