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..
Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented One-Max-Search
Authors: Ziyad Benomar, Lorenzo Croissant, Vianney Perchet, Spyros Angelopoulos
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical results through numerical experiments, in which we compare our algorithm to the state of the art, by testing it under both synthetic and real data. |
| Researcher Affiliation | Academia | 1CREST, ENSAE, Palaiseau, France 2INRIA Fairplay joint team, Palaiseau, France 3Criteo AI Lab, Paris, France 4CNRS and International Laboratory on Learning Systems, Montreal, Canada. Correspondence to: Ziyad Benomar <EMAIL>. |
| Pseudocode | No | The paper describes algorithms using mathematical formulations and descriptive text, such as "the proposed algorithms select the first price that exceeds a threshold Φ(y)", but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements about open-sourcing its code or provide any links to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions using 'real Bitcoin data (USD) recorded every minute from the beginning of 2020 to the end of 2024' but does not provide a specific link, DOI, repository name, or formal citation for accessing this dataset. |
| Dataset Splits | No | The paper describes a simulation setup involving randomly sampled 10-week windows of prices and a process of generating predictions, but it does not specify explicit training/test/validation dataset splits with percentages, sample counts, or predefined partition references. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU models, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | We fix θ = 5, λ = 0.5, and r = θ (1 λ/2). We randomly sample a 10-week window of prices W0... To simulate worst-case scenarios, the last price in W0 is changed to L with a probability of 0.75. For each value of α, we sample m = 100 windows... We choose the robustness r of Aρ r by setting λ [0, 1] and r = θ (1 λ/2). |