Online learning with kernel losses
Authors: Niladri Chatterji, Aldo Pacchiano, Peter Bartlett
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | All the proofs, technical details and experiments are relegated to the appendix. |
| Researcher Affiliation | Academia | 1University of California Berkeley. Correspondence to: Aldo Pacchiano <pacchiano@berkeley.edu>, Niladri S. Chatterji <chatterji@berkeley.edu>. |
| Pseudocode | Yes | Algorithm 1 Finite dimensional proxy construction, Algorithm 2 Bandit Information: Exponential Weights, Algorithm 3 Full Information: Exponential Weights, Algorithm 4 Full Information: Conditional Gradient. |
| Open Source Code | No | The paper does not provide any explicit statements about open-source code availability, nor does it include links to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not present empirical studies or use specific datasets for experimental evaluation. |
| Dataset Splits | No | The paper is theoretical and does not present empirical studies with datasets, therefore no training/validation/test splits are discussed. |
| Hardware Specification | No | The paper is theoretical and does not present empirical experiments, therefore no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not present empirical experiments, therefore no software dependencies with version numbers are specified. |
| Experiment Setup | No | The paper is theoretical and does not present empirical experiments, therefore no experimental setup details such as hyperparameters or training settings are provided. |