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.
Online learning with kernel losses
Authors: Niladri Chatterji, Aldo Pacchiano, Peter Bartlett
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,290 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 7,262 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 | All the proofs, technical details and experiments are relegated to the appendix. |
| Researcher Affiliation | Academia | 1University of California Berkeley. Correspondence to: Aldo Pacchiano <EMAIL>, Niladri S. Chatterji <EMAIL>. |
| 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. |