Smooth Convex Optimization Using Sub-Zeroth-Order Oracles
Authors: Mustafa O. Karabag, Cyrus Neary, Ufuk Topcu3815-3822
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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 {karabag, cneary, utopcu}@utexas.edu |
| 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. |