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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |