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..
Dual Mirror Descent for Online Allocation Problems
Authors: Santiago Balseiro, Haihao Lu, Vahab Mirrokni
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6. Numerical Experiment Here, we present a numerical experiment on proportional matching with high entropy (Section 5.2) to verify our results. Figure 1 plots the regret versus horizon T, from which we can clearly see that the regret grows at the rate of T, which verifies the results in Theorem 1. Figure 2 plots the relative reward (ratio between the reward collected by the online algorithm and the offline optimal) versus horizon T. |
| Researcher Affiliation | Collaboration | Santiago Balseiro 1 2 Haihao Lu 2 Vahab Mirrokni 2 ... 1Columbia University, New York, USA 2Google Research, New York, USA. |
| Pseudocode | Yes | Algorithm 1 Dual Mirror Descent Algorithm for (1) ... Algorithm 2 Online Dual Mirror Descent Algorithm for Bidding in Repeated Auctions ... Algorithm 3 Online Dual Mirror Descent Algorithm for Proportional Matching Problems with High Entropy |
| Open Source Code | Yes | The details of the numerical experiment and data generation are presented in Appendix H, and the code to reproduce the results is in supplementary materials. |
| Open Datasets | Yes | The dataset is generated following the procedures stated in Balseiro et al. (2014). |
| Dataset Splits | No | The paper describes generating '100,000 samples' and using '20 random datasets with size T uniformly randomly chosen from 100,000 samples' for experiments, but it does not specify explicit train/validation/test splits for these datasets. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For all experiments, we start from µ0 = 0, utilize h(x) = 1/2 x 2 2 as our reference function (thus the algorithm is dual sub-gradient descent), and choose η = 1/T as the step-size. ... We incorporate the entropy regularizer H(x) to the objective with parameter λ = 0.0002 |