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..
Context Attentive Bandits: Contextual Bandit with Restricted Context
Authors: Djallel Bouneffouf, Irina Rish, Guillermo Cecchi, Raphaël Féraud
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical results demonstrate advantages of the proposed approaches on several real-life datasets. |
| Researcher Affiliation | Industry | 1,2,3IBM Thomas J. Watson Research Center, Yorktown Heights, NY USA 4Orange Labs, 2 av. Pierre Marzin, 22300 Lannion (France) |
| Pseudocode | Yes | Algorithm 1 The CBRC Problem Setting, Algorithm 2 Thompson Sampling with Restricted Context (TSRC) |
| Open Source Code | No | The paper does not provide explicit statements or links to open-source code for the described methodology. |
| Open Datasets | Yes | Empirical evaluation of the proposed methods was based on four datasets from the UCI Machine Learning Repository 2: Covertype, CNAE-9, Internet Advertisements and Poker Hand (for details of each dataset, see Table 1). 2https://archive.ics.uci.edu/ml/datasets.html |
| Dataset Splits | No | The paper describes a sequential data stream simulation and online learning setup rather than explicit, fixed train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We ran the above algorithms and our proposed TSRC and WTSRC methods, in stationary and non-stationary settings, respectively, for different feature subset sizes, such as 5%, 25%, 50% and 75% of the total number of features. [...] In this setting, for each dataset, we run the experiments for 3,000,000 iterations, where we change the label of class at each 500,000 iteration to simulate the non-stationarity. |