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..
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. |