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..
Buying Information for Stochastic Optimization
Authors: Mingchen Ma, Christos Tzamos
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Assuming the learner has an oracle for the original optimization problem, we design a 2-competitive deterministic algorithm and a e/(e 1)-competitive randomized algorithm for buying information. We show that this ratio is tight as the problem is equivalent to a robust generalization of the ski-rental problem, which we call super-martingale stopping. |
| Researcher Affiliation | Academia | Mingchen Ma 1 Christos Tzamos 1 ... 1Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA. |
| Pseudocode | Yes | Algorithm 1 DETERMINISTICSTOPPING (2-competitive deterministic algorithm for super-martingale stopping) ... Algorithm 2 RANDOMIZEDSTOPPING ( e e 1-competitive algorithm for super-martingale stopping) ... Algorithm 3 GREEDY (4-competitive Learner for MSSC with Time Dependent Feedback) ... Algorithm 4 GREEDYBUYING (8-competitive learner for MSSC with Feedback) |
| Open Source Code | No | The paper does not provide any concrete links or statements about the availability of open-source code for the described methods. |
| Open Datasets | No | The paper is a theoretical work focusing on algorithm design and competitive analysis. It does not involve empirical training on datasets, thus no training data is mentioned or made available. |
| Dataset Splits | No | The paper is a theoretical work on algorithms and competitive analysis. It does not conduct empirical experiments involving validation datasets or splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and competitive analysis; it does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithm design and competitive analysis. It does not mention any specific software dependencies with version numbers that would be required for empirical reproduction. |
| Experiment Setup | No | The paper is theoretical, presenting algorithms and their competitive analysis. It does not detail an experimental setup with hyperparameters or training configurations. |