Binary Search with Distributional Predictions
Authors: Michael Dinitz, Sungjin Im, Thomas Lavastida, Ben Moseley, Aidin Niaparast, Sergei Vassilvitskii
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement this with a lower bound showing that this query complexity is essentially optimal (up to constants), and experiments validating the practical usefulness of our algorithm. |
| Researcher Affiliation | Collaboration | Michael Dinitz Johns Hopkins University EMAIL Sungjin Im UC Merced EMAIL Thomas Lavastida University of Texas at Dallas EMAIL Benjamin Moseley Carnegie Mellon University EMAIL Aidin Niaparast Carnegie Mellon University EMAIL Sergei Vassilvitskii Google Research EMAIL |
| Pseudocode | No | The algorithm is described step-by-step in natural language within Section 3 and Section 4.1, but it is not presented in a formal pseudocode block or labeled 'Algorithm X' figure. |
| Open Source Code | Yes | Our implementation can be found at https://github.com/Aidin Niaparast/Learned-BST. |
| Open Datasets | Yes | In order to test our approach on real-world data, we use temporal networks from Stanford Large Network Dataset Collection5. These datasets represent the interactions on stack exchange websites Stack Overflow, Ask Ubuntu, and Super User [Paranjape et al., 2017]. 5https://snap.stanford.edu/data/index.html |
| Dataset Splits | Yes | For t = 5, 10, . . . , 50, we use the first t percent of A as training data and the rest as test data. |
| Hardware Specification | Yes | We use Python 3.10 for conducting our experiments on a system equipped with an 11th Generation Intel Core i7 CPU running at 2.80GHz, 32GB of RAM, a 128GB NVMe KIOXIA disk drive, and a 64-bit Windows 10 Enterprise operating system. |
| Software Dependencies | No | The paper mentions 'Python 3.10' as the software used for experiments, but it does not list multiple key software components with their specific version numbers or a self-contained solver/package with a version number. |
| Experiment Setup | Yes | We make one modification, setting the parameter d larger to broaden the search space in the very early iterations, setting d to min(2^8 2i, r − ℓ). |