Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Smooth Convex Optimization Using Sub-Zeroth-Order Oracles
Authors: Mustafa O. Karabag, Cyrus Neary, Ufuk Topcu3815-3822
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,371 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,943 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |