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..
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Authors: Anton Osokin, Francis Bach, Simon Lacoste-Julien
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide novel theoretical insights on structured prediction in the context of efficient convex surrogate loss minimization with consistency guarantees. For any task loss, we construct a convex surrogate that can be optimized via stochastic gradient descent and we prove tight bounds on the so-called calibration function relating the excess surrogate risk to the actual risk. We propose a theoretical framework that jointly tackles these two aspects and allows to judge the feasibility of efficient learning. |
| Researcher Affiliation | Academia | Anton Osokin INRIA/ENS , Paris, France HSE , Moscow, Russia Francis Bach INRIA/ENS , Paris, France Simon Lacoste-Julien MILA and DIRO Université de Montréal, Canada DI École normale supérieure, CNRS, PSL Research University National Research University Higher School of Economics |
| Pseudocode | No | The paper describes the ASGD update rule in text (equation 9), but does not provide a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not provide any concrete access information to source code, such as a repository link or an explicit statement about code release. |
| Open Datasets | No | The paper is theoretical and does not mention using or providing access to any specific dataset, public or otherwise. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on specific datasets, thus no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup or the hardware used for computations. |
| Software Dependencies | No | The paper is purely theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not provide details about an experimental setup, such as hyperparameters or system-level training settings. |