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..
Smooth Convex Optimization Using Sub-Zeroth-Order Oracles
Authors: Mustafa O. Karabag, Cyrus Neary, Ufuk Topcu3815-3822
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The results of this paper are theoretical. |
| Researcher Affiliation | Academia | Mustafa O. Karabag, Cyrus Neary, Ufuk Topcu The University of Texas at Austin, Austin, TX EMAIL |
| Pseudocode | Yes | Algorithm 1 The optimization algorithm OPTIMIZEDP(X, ψDP) for the directional preference oracle; Function PD-DP(x, θ, TA,x); Function COMPARE-DP(X, ε); Algorithm 2 The optimization algorithm OPTIMIZE-C(ε) for the comparator oracle; Function FDD-C(A, x0, d, t); Function PD-C(x, θ, A, t); Algorithm 3 The low regret algorithm REGRET-NV(T, δ) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve training data or experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not present experimental setup details such as hyperparameters or training configurations. |