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 [1].
Online Algorithm for Unsupervised Sequential Selection with Contextual Information
Authors: Arun Verma, Manjesh Kumar Hanawal, Csaba Szepesvari, Venkatesh Saligrama
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real datasets validate our algorithm. We evaluate the performance of USS-PD on different problem instances derived from synthetic and real datasets. |
| Researcher Affiliation | Collaboration | Arun Verma Department of IEOR IIT Bombay, India EMAIL; Manjesh K. Hanawal Department of IEOR IIT Bombay, India EMAIL; Csaba Szepesvári Deep Mind/University of Alberta Alberta, Canada EMAIL; Venkatesh Saligrama Departmetn of ECE Boston University, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Learning on contextual USS instance (Q, c); USS-PD Algorithm for Contextual USS using Pairwise Disagreement |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We applied our algorithm on PIMA Indian Diabetes (Kaggle, 2016) dataset. Heart Disease dataset (Detrano, 1998; Dheeru and Karra Taniskidou, 2017) |
| Dataset Splits | No | The paper mentions using a synthetic dataset of 5000 samples and splitting features, but it does not provide specific train/validation/test dataset splits (percentages or counts) for any of the datasets used. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions training logistic classifiers and implementing settings, implying software use, but does not specify any software libraries or dependencies with their version numbers. |
| Experiment Setup | No | The paper mentions varying regularization parameters and costs for creating problem instances, but it does not provide specific hyperparameters (e.g., learning rate, batch size, number of epochs) or system-level training settings for the USS-PD algorithm itself. |