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.
Safety-Aware Algorithms for Adversarial Contextual Bandit
Authors: Wen Sun, Debadeepta Dey, Ashish Kapoor
ICML 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,988 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,702 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We support our theoretical results by demonstrating our algorithm on a simple simulated robotics reactive control task. |
| Researcher Affiliation | Collaboration | 1Robotics Institute, Carnegie Mellon University, USA 2Microsoft Research, Redmond, USA. |
| Pseudocode | Yes | Algorithm 1 OCP with Sequential Constraints via OMD; Algorithm 2 EXP4 with Risk Constraints (EXP4.R) |
| Open Source Code | No | The paper does not provide any explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | The paper describes a "simple synthetic robotics reactive control task" and its "Environment", but it does not specify or provide access information for a publicly available dataset or a generated dataset. |
| Dataset Splits | No | The paper describes a simulation environment but does not provide specific details on dataset splits (e.g., training, validation, testing percentages or counts) or cross-validation methods. |
| Hardware Specification | No | The paper mentions "paralleling computing" but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the simulation. |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software, libraries, or programming languages used. |
| Experiment Setup | No | The paper describes the environment setup for the simulation and some general configurations (e.g., "initialize the weight over all experts uniformly") but does not provide specific hyperparameters (e.g., learning rates, batch sizes) or system-level training settings. |