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..
Optimal Sequential Maximization: One Interview is Enough!
Authors: Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments In this section, we compare the performance of various sequential maximization algorithms SEQELIMINATE (Falahatgar et al., 2017a), AGNOSTIC-SEQ and OPT-AGNOSTIC-SEQ. |
| Researcher Affiliation | Collaboration | 1Apple Inc. 2University of California, San Diego. |
| Pseudocode | Yes | Algorithm 1 ASYMMETRIC-THRESHOLD (A-T), Algorithm 2 OPTIMAL-SEQUENTIAL (O-S), Algorithm 3 OPT-ANCHOR-UPDATE, Algorithm 4 OPT-AGNOSTIC-SEQ |
| Open Source Code | No | The paper does not provide any specific links to source code repositories or explicit statements indicating the availability of the code for its described methodology. |
| Open Datasets | No | The paper describes generating data based on models (e.g., 'all items are essentially equal i.e., pi,j = 1/2 i, j' and 'pi,j = 0.6 i < j') for its experiments. It does not mention using or providing access information for any publicly available or open dataset. |
| Dataset Splits | No | The paper describes experiments on synthetic data models and does not provide specific train/validation/test dataset splits, percentages, or absolute sample counts required for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU/CPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CPLEX versions). |
| Experiment Setup | Yes | In all the experiments in this section, we try to ๏ฌnd an 0.05-maximum with ฮด = 0.1. All results are averaged over 100 runs. and We ๏ฌrst consider the model where all items are essentially equal i.e., pi,j = 1/2 i, j. and We now consider the model where pi,j = 0.6 i < j |