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..
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Authors: Dylan J. Foster, Akshay Krishnamurthy
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We use surrogate losses to obtain several new regret bounds and new algorithms for contextual bandit learning. Using the ramp loss, we derive new margin-based regret bounds in terms of standard sequential complexity measures of a benchmark class of real-valued regression functions. Using the hinge loss, we derive an ef๏ฌcient algorithm with a d T-type mistake bound against benchmark policies induced by d-dimensional regressors. Under realizability assumptions, our results also yield classical regret bounds. |
| Researcher Affiliation | Collaboration | Dylan J. Foster Cornell University EMAIL Akshay Krishnamurthy Microsoft Research, NYC EMAIL |
| Pseudocode | Yes | Algorithm 1 HINGE-LMC and Algorithm 2 Langevin Monte Carlo (LMC) |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement) for open-source code related to the described methodology. |
| Open Datasets | No | The paper focuses on theoretical contributions (regret bounds, algorithms) and does not describe experiments that use a specific dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments requiring specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and theoretical bounds, thus it does not provide specific experimental setup details such as hyperparameters for empirical runs. |