Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Authors: Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
NeurIPS 2016 | Venue PDF | LLM Run Details | Input Tokens: 13,677 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,403 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a new oracle-based algorithm, BISTRO+, for the adversarial contextual bandit problem...Our algorithm is computationally efficient, assuming access to an offline optimization oracle, and enjoys a regret of order O((KT) 2 3 (log N) 1 3 )...Our algorithm and regret bound are based on a novel and improved analysis of the minimax problem that arises in the relaxation-based framework of Rakhlin and Sridharan [7]. |
| Researcher Affiliation | Collaboration | Vasilis Syrgkanis Microsoft Research EMAIL Haipeng Luo Microsoft Research EMAIL Akshay Krishnamurthy University of Massachusetts, Amherst EMAIL Robert E. Schapire Microsoft Research EMAIL |
| Pseudocode | Yes | Algorithm 1 BISTRO+ Algorithm 2 Computing q t (ρt) |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and regret bounds. It does not use specific publicly available datasets for empirical training or evaluation. It mentions 'contexts are drawn i.i.d. from a distribution D', implying a theoretical model rather than a specific dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes an algorithm and its theoretical bounds but does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and analysis. It does not provide details about an experimental setup, such as hyperparameter values or training configurations. |