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..
A Learning-Augmented Approach to Online Allocation Problems
Authors: Ilan Cohen, Debmalya Panigrahi
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we develop a general learning-augmented algorithmic framework for online allocation problems that produces a nearly optimal solution using only a single d-dimensional vector of learned weights. Using this general framework, we derive learning-augmented online algorithms for a broad range of application problems in routing, scheduling, and fair allocation. Our main tool is convex programming duality, which may also have further implications for learning-augmented algorithms in the future. |
| Researcher Affiliation | Academia | Ilan Reuven Cohen Faculty of Engineering Bar-Ilan University Ramat Gan, Israel. EMAIL Debmalya Panigrahi Department of Computer Science Duke University Durham, NC, USA. EMAIL |
| Pseudocode | No | The paper describes algorithmic steps and mathematical formulations (e.g., Figures 1 and 2 for Convex Programming Formulation), but it does not include explicitly labeled pseudocode blocks or algorithms in a structured, step-by-step format typically associated with pseudocode. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide any links to code repositories. |
| Open Datasets | No | The paper focuses on theoretical algorithmic frameworks for online allocation problems and discusses applications like online routing and Nash Social Welfare, but it does not use or refer to any specific publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, therefore, no dataset splits are provided. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments or computations. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |