On Structured Prediction Theory with Calibrated Convex Surrogate Losses
Authors: Anton Osokin, Francis Bach, Simon Lacoste-Julien
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | 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. |