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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Wait-Less Offline Tuning and Re-solving for Online Decision Making
Authors: Jingruo Sun, Wenzhi Gao, Ellen Vitercik, Yinyu Ye
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate at least 10-fold improvements in regret over firstorder methods and 100-fold improvements in runtime over LP-based methods. ... We conduct extensive experiments to evaluate our algorithm s performance and validate our theoretical results. This section is divided into two parts. In the first part (Section 4.1), we evaluate Algorithms 1 and 2 across different choices of re-solving frequency. In the second part (Section 4.2), we compare our algorithm with LP-based and first-order methods in terms of regret and running time. |
| Researcher Affiliation | Academia | 1 Department of Management Science & Engineering, Stanford University 2 Institute for Computational and Mathematical Engineering, Stanford University 3 Department of Computer Science, Stanford University 4 Chinese University of Hong Kong (Shen Zhen) 5 Hong Kong University of Science and Technology. Correspondence to: Jingruo Sun <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Parallel Multi-Phase OLP Algorithm ... Algorithm 2 Enhanced Multi-Start OLP Algorithm |
| Open Source Code | Yes | All implementations can be found at Git Hub Link. |
| Open Datasets | No | We consider the following distributions: Input I: ait Unif[0, 2], rt Unif[0, 10] Input II: ait N(0.5, 1), rt N(0.5m, m) ... Input III: ait min(1, max(0, 1 + z)), rt Unif[0, 1] where z t(1) : Student s t-distribution with 1 degree of freedom. ... We generate {rt, at}T t=1 from Input III |
| Dataset Splits | No | The paper describes generating sequences of data points {(rt, at)}T t=1 randomly from uniform and normal distributions over a time horizon T, but does not specify any explicit train/test/validation splits for the generated data. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only discusses runtime performance generally. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1). |
| Experiment Setup | Yes | Learning rates are selected as specified in Section 3: Algorithm 1: αt = (O(1/f 1/2) t f O(1/f 2/3) t kf Algorithm 2: αt = O(1/t) ... We set the resource types to m = 5, time T to range evenly over [102, 106], average resource di Uniform[1/3, 2/3], and re-solving frequency to f = T 1/3 from Section 4.1. |