Black-Box Generalization: Stability of Zeroth-Order Learning
Authors: Konstantinos Nikolakakis, Farzin Haddadpour, Dionysis Kalogerias, Amin Karbasi
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide the first generalization error analysis for black-box learning through derivative-free optimization. Under the assumption of a Lipschitz and smooth unknown loss, we consider the Zeroth-order Stochastic Search (Zo SS) algorithm, that updates a d-dimensional model by replacing stochastic gradient directions with stochastic differences of K + 1 perturbed loss evaluations per dataset (example) query. For both unbounded and bounded possibly nonconvex losses, we present the first generalization bounds for the Zo SS algorithm. |
| Researcher Affiliation | Collaboration | Konstantinos E. Nikolakakis Yale University konstantinos.nikolakakis@yale.edu Farzin Haddadpour Yale University farzin.haddadpour@yale.edu Dionysios S. Kalogerias Yale University dionysis.kalogerias@yale.edu Amin Karbasi Yale University & Google Research amin.karbasi@yale.edu |
| Pseudocode | No | The paper describes algorithms using mathematical equations but does not present pseudocode or clearly labeled algorithm blocks within its content. |
| Open Source Code | No | Under section '3. If you ran experiments...', all sub-sections related to code and data reproduction are marked as '[N/A]', indicating no such materials were provided. |
| Open Datasets | No | The paper is theoretical and does not describe experiments performed on a specific, publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not report empirical studies and therefore does not provide details on training/test/validation dataset splits. |
| Hardware Specification | No | Under section '3. If you ran experiments...', the question about total amount of compute and type of resources used is marked as '[N/A]', indicating no hardware specifications were provided. |
| Software Dependencies | No | Under section '3. If you ran experiments...', the question about specifying training details (e.g., data splits, hyperparameters, how they were chosen) is marked as '[N/A]', which implies no software dependency details were provided. |
| Experiment Setup | No | Under section '3. If you ran experiments...', the question about specifying training details (e.g., data splits, hyperparameters, how they were chosen) is marked as '[N/A]', indicating no such experimental setup details were provided. |