Binary Search with Distributional Predictions
Authors: Michael Dinitz, Sungjin Im, Thomas Lavastida, Ben Moseley, Aidin Niaparast, Sergei Vassilvitskii
NeurIPS 2024 | Conference 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 mdinitz@cs.jhu.edu Sungjin Im UC Merced sim3@ucmerced.edu Thomas Lavastida University of Texas at Dallas thomas.lavastida@utdallas.edu Benjamin Moseley Carnegie Mellon University moseleyb@andrew.cmu.edu Aidin Niaparast Carnegie Mellon University aniapara@andrew.cmu.edu Sergei Vassilvitskii Google Research sergeiv@google.com |
| 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). |