Notice: 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. Full accuracy metrics and methodology are described in [1].
Online Learning under Budget and ROI Constraints via Weak Adaptivity
Authors: Matteo Castiglioni, Andrea Celli, Christian Kroer
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove the first best-of-both-worlds no-regret guarantees which hold in absence of the two aforementioned assumptions, under stochastic and adversarial inputs." and "We show that the resulting framework provides best-of-both-worlds no-regret guarantees while solving both limitations. |
| Researcher Affiliation | Academia | 1DEIB, Politecnico di Milano, Milan, Italy 2Department of Computing Sciences, Bocconi University, Milan, Italy 3IEOR Department, Columbia University, New York, NY. |
| Pseudocode | Yes | Algorithm 1 Primal-dual framework." and "Algorithm 2 Primal regret minimizer. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not mention the use of specific public datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data with training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments requiring hardware. No hardware specifications are mentioned. |
| 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 describe empirical experiments with specific setup details like hyperparameters or training configurations. |